Interview——IT 行业应试学知识库
> 程序员的双手是魔术师的双手，他们把枯燥无味的代码变成了丰富多彩的软件。——《疯狂的程序员》 ## 在线阅读 * 网址: https://interview.apachecn.org ## **协议** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh) ## 赞助我们
---
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FILE: SUMMARY.md
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+ [Introduction](README.md)
+ 面试求职
+ [简历](docs/面试求职/简历.md)
+ [简历范文](docs/面试求职/简历范文.md)
+ [学历](docs/面试求职/学历.md)
+ [刷题](docs/面试求职/刷题.md)
+ [公司](docs/面试求职/公司.md)
+ [职场](docs/面试求职/职场.md)
+ [理财](docs/面试求职/理财.md)
+ [年龄](docs/面试求职/年龄.md)
+ [算法刷题](docs/Algorithm/README.md)
+ [数据结构](docs/Algorithm/DataStructure/README.md)
+ [LeetCode C++ 版本↗](https://algo.apachecn.org/#/docs/leetcode/cpp/README)
+ [LeetCode Java 版本↗](https://algo.apachecn.org/#/docs/leetcode/java/README)
+ [LeetCode Python 版本↗](https://algo.apachecn.org/#/docs/leetcode/python/README)
+ [LeetCode JavaScript 版本↗](https://algo.apachecn.org/#/docs/leetcode/javascript/README)
+ [剑指offer↗](https://algo.apachecn.org/#/docs/jianzhioffer/java/README)
+ [IT 八股文↗](https://bgww.apachecn.org/#/)
+ [Kaggle比赛](docs/Kaggle/README.md)
+ [Kernel 备份（一）↗](https://github.com/it-ebooks-0/kaggle-kernel-pt1)
+ [Kernel 备份（二）↗](https://github.com/it-ebooks-0/kaggle-kernel-pt2)
+ [Kernel 备份（三）↗](https://github.com/it-ebooks-0/kaggle-kernel-pt3)
+ [Kernel 备份（四）↗](https://github.com/it-ebooks-0/kaggle-kernel-pt4)
+ 职业认证
+ [AQF↗](https://github.com/apachecn/interview-books/tree/master/AQF)
+ [CCNA/NP/IE↗](https://github.com/apachecn/interview-books/tree/master/CCNA-NP-IE)
+ [CEH↗](https://github.com/apachecn/interview-books/tree/master/CEH)
+ [PMP↗](https://github.com/apachecn/interview-books/tree/master/PMP)
+ [职位数据库↗](https://github.com/apachecn/interview-books/tree/master/%E8%81%8C%E4%BD%8D%E6%95%B0%E6%8D%AE%E5%BA%93)
+ [面试译文集↗](https://itvw.apachecn.org)
+ [牛客面试题库↗](https://github.com/apachecn/interview-books/tree/master/NowCoder)
+ [GitHub快速入门](docs/GitHub/README.md)
+ [导师评价网备份↗](https://rms.apachecn.org/#/)
+ [校招污点公司记录↗](https://github.com/ShameCom/ShameCom)
+ [独立开发/自由职业/远程工作/数字游民知识库↗](https://idw.apachecn.org/#/)
+ [贡献者名单](CONTRIBUTORS.md)
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================================================
FILE: asset/docsify-quick-page.css
================================================
#prev-page-button {
position:fixed;
top:140px;
width: 35px;
height: 35px;
right: 15px;
background-color: transparent;
background-image: url(left.svg);
background-repeat: no-repeat;
background-size: cover;
border:0;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
outline:none;
cursor: pointer;
}
#next-page-button {
position:fixed;
top:180px;
width:35px;
height:35px;
right:15px;
background-color: transparent;
background-image: url(right.svg);
background-repeat: no-repeat;
background-size: cover;
border:0;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
outline:none;
cursor: pointer;
}
================================================
FILE: asset/docsify-quick-page.js
================================================
document.addEventListener('DOMContentLoaded', function() {
var prevBtn = document.createElement("div")
prevBtn.id = "prev-page-button"
document.body.appendChild(prevBtn)
var nextBtn = document.createElement("div");
nextBtn.id = "next-page-button"
document.body.appendChild(nextBtn)
var links = null
var linkMap = null
var getCurIdx = function() {
if (!links) {
links = Array
.from(document.querySelectorAll(".sidebar-nav a"))
.map(x => x.href)
linkMap = {}
links.forEach((x, i) => linkMap[x] = i)
}
var elem = document.querySelector(".active a")
var curIdx = elem? linkMap[elem.href]: -1
return curIdx
}
prevBtn.addEventListener('click', function () {
if (!document.body.classList.contains('ready'))
return
var curIdx = getCurIdx()
location.href = curIdx == -1?
links[0]:
links[(curIdx - 1 + links.length) % links.length]
document.body.scrollIntoView()
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nextBtn.addEventListener('click', function () {
if (!document.body.classList.contains('ready'))
return
var curIdx = getCurIdx()
location.href = links[(curIdx + 1) % links.length]
document.body.scrollIntoView()
}, false)
})
================================================
FILE: asset/edit.css
================================================
#edit-btn {
position: fixed;
right: 15px;
top: 260px;
width: 35px;
height: 35px;
background-repeat: no-repeat;
background-size: cover;
cursor: pointer;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
background-image: url(edit.svg);
}
================================================
FILE: asset/edit.js
================================================
document.addEventListener('DOMContentLoaded', function() {
var editBtn = document.createElement('div')
editBtn.id = 'edit-btn'
document.body.append(editBtn)
var repo = window.$docsify.repo
editBtn.addEventListener('click', function() {
if (!repo) return
if (!/https?:\/\//.exec(repo))
repo = 'https://github.com/' + repo
var url = repo + '/tree/master' +
location.hash.slice(1) + '.md'
window.open(url)
})
})
================================================
FILE: asset/prism-darcula.css
================================================
/**
* Darcula theme
*
* Adapted from a theme based on:
* IntelliJ Darcula Theme (https://github.com/bulenkov/Darcula)
*
* @author Alexandre Paradis
* @version 1.0
*/
code[class*="lang-"],
pre[data-lang] {
color: #a9b7c6 !important;
background-color: #2b2b2b !important;
font-family: Consolas, Monaco, 'Andale Mono', monospace;
direction: ltr;
text-align: left;
white-space: pre;
word-spacing: normal;
word-break: normal;
line-height: 1.5;
-moz-tab-size: 4;
-o-tab-size: 4;
tab-size: 4;
-webkit-hyphens: none;
-moz-hyphens: none;
-ms-hyphens: none;
hyphens: none;
}
pre[data-lang]::-moz-selection, pre[data-lang] ::-moz-selection,
code[class*="lang-"]::-moz-selection, code[class*="lang-"] ::-moz-selection {
color: inherit;
background: rgba(33, 66, 131, .85);
}
pre[data-lang]::selection, pre[data-lang] ::selection,
code[class*="lang-"]::selection, code[class*="lang-"] ::selection {
color: inherit;
background: rgba(33, 66, 131, .85);
}
/* Code blocks */
pre[data-lang] {
padding: 1em;
margin: .5em 0;
overflow: auto;
}
:not(pre) > code[class*="lang-"],
pre[data-lang] {
background: #2b2b2b;
}
/* Inline code */
:not(pre) > code[class*="lang-"] {
padding: .1em;
border-radius: .3em;
}
.token.comment,
.token.prolog,
.token.cdata {
color: #808080;
}
.token.delimiter,
.token.boolean,
.token.keyword,
.token.selector,
.token.important,
.token.atrule {
color: #cc7832;
}
.token.operator,
.token.punctuation,
.token.attr-name {
color: #a9b7c6;
}
.token.tag,
.token.tag .punctuation,
.token.doctype,
.token.builtin {
color: #e8bf6a;
}
.token.entity,
.token.number,
.token.symbol {
color: #6897bb;
}
.token.property,
.token.constant,
.token.variable {
color: #9876aa;
}
.token.string,
.token.char {
color: #6a8759;
}
.token.attr-value,
.token.attr-value .punctuation {
color: #a5c261;
}
.token.attr-value .punctuation:first-child {
color: #a9b7c6;
}
.token.url {
color: #287bde;
text-decoration: underline;
}
.token.function {
color: #ffc66d;
}
.token.regex {
background: #364135;
}
.token.bold {
font-weight: bold;
}
.token.italic {
font-style: italic;
}
.token.inserted {
background: #294436;
}
.token.deleted {
background: #484a4a;
}
code.lang-css .token.property,
code.lang-css .token.property + .token.punctuation {
color: #a9b7c6;
}
code.lang-css .token.id {
color: #ffc66d;
}
code.lang-css .token.selector > .token.class,
code.lang-css .token.selector > .token.attribute,
code.lang-css .token.selector > .token.pseudo-class,
code.lang-css .token.selector > .token.pseudo-element {
color: #ffc66d;
}
================================================
FILE: asset/share.css
================================================
#share-btn {
position: fixed;
right: 15px;
top: 220px;
width: 35px;
height: 35px;
background-repeat: no-repeat;
background-size: cover;
cursor: pointer;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
background-image: url('share.svg');
}
================================================
FILE: asset/share.js
================================================
document.addEventListener('DOMContentLoaded', function() {
var shareBtn = document.createElement('a')
shareBtn.id = 'share-btn'
shareBtn.className = 'bdsharebuttonbox'
shareBtn.setAttribute('data-cmd', 'more')
document.body.append(shareBtn)
window._bd_share_config = {
"common":{
"bdSnsKey":{},
"bdText":"",
"bdMini":"1",
"bdMiniList":false,
"bdPic":"",
"bdStyle":"2",
"bdSize":"16"
},
"share":{}
}
})
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================================================
FILE: asset/style.css
================================================
/*隐藏头部的目录*/
#main>ul:nth-child(1) {
display: none;
}
#main>ul:nth-child(2) {
display: none;
}
.markdown-section h1 {
margin: 3rem 0 2rem 0;
}
.markdown-section h2 {
margin: 2rem 0 1rem;
}
img,
pre {
border-radius: 8px;
}
.content,
.sidebar,
.markdown-section,
body,
.search input {
background-color: rgba(243, 242, 238, 1) !important;
}
@media (min-width:600px) {
.sidebar-toggle {
background-color: #f3f2ee;
}
}
.docsify-copy-code-button {
background: #f8f8f8 !important;
color: #7a7a7a !important;
}
body {
/*font-family: Microsoft YaHei, Source Sans Pro, Helvetica Neue, Arial, sans-serif !important;*/
}
.markdown-section>p {
font-size: 16px !important;
}
.markdown-section pre>code {
font-family: Consolas, Roboto Mono, Monaco, courier, monospace !important;
font-size: .9rem !important;
}
/*.anchor span {
color: rgb(66, 185, 131);
}*/
section.cover h1 {
margin: 0;
}
body>section>div.cover-main>ul>li>a {
color: #42b983;
}
.markdown-section img {
box-shadow: 7px 9px 10px #aaa !important;
}
pre {
background-color: #f3f2ee !important;
}
@media (min-width:600px) {
pre code {
/*box-shadow: 2px 1px 20px 2px #aaa;*/
/*border-radius: 10px !important;*/
padding-left: 20px !important;
}
}
@media (max-width:600px) {
pre {
padding-left: 0px !important;
padding-right: 0px !important;
}
}
.markdown-section pre {
padding-left: 0 !important;
padding-right: 0px !important;
box-shadow: 2px 1px 20px 2px #aaa;
}
iframe {
display: inline;
}
================================================
FILE: asset/vue.css
================================================
@import url("https://fonts.googleapis.com/css?family=Roboto+Mono|Source+Sans+Pro:300,400,600");
* {
-webkit-font-smoothing: antialiased;
-webkit-overflow-scrolling: touch;
-webkit-tap-highlight-color: rgba(0,0,0,0);
-webkit-text-size-adjust: none;
-webkit-touch-callout: none;
box-sizing: border-box;
}
body:not(.ready) {
overflow: hidden;
}
body:not(.ready) [data-cloak],
body:not(.ready) .app-nav,
body:not(.ready) > nav {
display: none;
}
div#app {
font-size: 30px;
font-weight: lighter;
margin: 40vh auto;
text-align: center;
}
div#app:empty::before {
content: 'Loading...';
}
.emoji {
height: 1.2rem;
vertical-align: middle;
}
.progress {
background-color: var(--theme-color, #42b983);
height: 2px;
left: 0px;
position: fixed;
right: 0px;
top: 0px;
transition: width 0.2s, opacity 0.4s;
width: 0%;
z-index: 999999;
}
.search a:hover {
color: var(--theme-color, #42b983);
}
.search .search-keyword {
color: var(--theme-color, #42b983);
font-style: normal;
font-weight: bold;
}
html,
body {
height: 100%;
}
body {
-moz-osx-font-smoothing: grayscale;
-webkit-font-smoothing: antialiased;
color: #34495e;
font-family: 'Source Sans Pro', 'Helvetica Neue', Arial, sans-serif;
font-size: 15px;
letter-spacing: 0;
margin: 0;
overflow-x: hidden;
}
img {
max-width: 100%;
}
a[disabled] {
cursor: not-allowed;
opacity: 0.6;
}
kbd {
border: solid 1px #ccc;
border-radius: 3px;
display: inline-block;
font-size: 12px !important;
line-height: 12px;
margin-bottom: 3px;
padding: 3px 5px;
vertical-align: middle;
}
li input[type='checkbox'] {
margin: 0 0.2em 0.25em 0;
vertical-align: middle;
}
.app-nav {
margin: 25px 60px 0 0;
position: absolute;
right: 0;
text-align: right;
z-index: 10;
/* navbar dropdown */
}
.app-nav.no-badge {
margin-right: 25px;
}
.app-nav p {
margin: 0;
}
.app-nav > a {
margin: 0 1rem;
padding: 5px 0;
}
.app-nav ul,
.app-nav li {
display: inline-block;
list-style: none;
margin: 0;
}
.app-nav a {
color: inherit;
font-size: 16px;
text-decoration: none;
transition: color 0.3s;
}
.app-nav a:hover {
color: var(--theme-color, #42b983);
}
.app-nav a.active {
border-bottom: 2px solid var(--theme-color, #42b983);
color: var(--theme-color, #42b983);
}
.app-nav li {
display: inline-block;
margin: 0 1rem;
padding: 5px 0;
position: relative;
cursor: pointer;
}
.app-nav li ul {
background-color: #fff;
border: 1px solid #ddd;
border-bottom-color: #ccc;
border-radius: 4px;
box-sizing: border-box;
display: none;
max-height: calc(100vh - 61px);
overflow-y: auto;
padding: 10px 0;
position: absolute;
right: -15px;
text-align: left;
top: 100%;
white-space: nowrap;
}
.app-nav li ul li {
display: block;
font-size: 14px;
line-height: 1rem;
margin: 0;
margin: 8px 14px;
white-space: nowrap;
}
.app-nav li ul a {
display: block;
font-size: inherit;
margin: 0;
padding: 0;
}
.app-nav li ul a.active {
border-bottom: 0;
}
.app-nav li:hover ul {
display: block;
}
.github-corner {
border-bottom: 0;
position: fixed;
right: 0;
text-decoration: none;
top: 0;
z-index: 1;
}
.github-corner:hover .octo-arm {
-webkit-animation: octocat-wave 560ms ease-in-out;
animation: octocat-wave 560ms ease-in-out;
}
.github-corner svg {
color: #fff;
fill: var(--theme-color, #42b983);
height: 80px;
width: 80px;
}
main {
display: block;
position: relative;
width: 100vw;
height: 100%;
z-index: 0;
}
main.hidden {
display: none;
}
.anchor {
display: inline-block;
text-decoration: none;
transition: all 0.3s;
}
.anchor span {
color: #34495e;
}
.anchor:hover {
text-decoration: underline;
}
.sidebar {
border-right: 1px solid rgba(0,0,0,0.07);
overflow-y: auto;
padding: 40px 0 0;
position: absolute;
top: 0;
bottom: 0;
left: 0;
transition: transform 250ms ease-out;
width: 300px;
z-index: 20;
}
.sidebar > h1 {
margin: 0 auto 1rem;
font-size: 1.5rem;
font-weight: 300;
text-align: center;
}
.sidebar > h1 a {
color: inherit;
text-decoration: none;
}
.sidebar > h1 .app-nav {
display: block;
position: static;
}
.sidebar .sidebar-nav {
line-height: 2em;
padding-bottom: 40px;
}
.sidebar li.collapse .app-sub-sidebar {
display: none;
}
.sidebar ul {
margin: 0 0 0 15px;
padding: 0;
}
.sidebar li > p {
font-weight: 700;
margin: 0;
}
.sidebar ul,
.sidebar ul li {
list-style: none;
}
.sidebar ul li a {
border-bottom: none;
display: block;
}
.sidebar ul li ul {
padding-left: 20px;
}
.sidebar::-webkit-scrollbar {
width: 4px;
}
.sidebar::-webkit-scrollbar-thumb {
background: transparent;
border-radius: 4px;
}
.sidebar:hover::-webkit-scrollbar-thumb {
background: rgba(136,136,136,0.4);
}
.sidebar:hover::-webkit-scrollbar-track {
background: rgba(136,136,136,0.1);
}
.sidebar-toggle {
background-color: transparent;
background-color: rgba(255,255,255,0.8);
border: 0;
outline: none;
padding: 10px;
position: absolute;
bottom: 0;
left: 0;
text-align: center;
transition: opacity 0.3s;
width: 284px;
z-index: 30;
cursor: pointer;
}
.sidebar-toggle:hover .sidebar-toggle-button {
opacity: 0.4;
}
.sidebar-toggle span {
background-color: var(--theme-color, #42b983);
display: block;
margin-bottom: 4px;
width: 16px;
height: 2px;
}
body.sticky .sidebar,
body.sticky .sidebar-toggle {
position: fixed;
}
.content {
padding-top: 60px;
position: absolute;
top: 0;
right: 0;
bottom: 0;
left: 300px;
transition: left 250ms ease;
}
.markdown-section {
margin: 0 auto;
max-width: 80%;
padding: 30px 15px 40px 15px;
position: relative;
}
.markdown-section > * {
box-sizing: border-box;
font-size: inherit;
}
.markdown-section > :first-child {
margin-top: 0 !important;
}
.markdown-section hr {
border: none;
border-bottom: 1px solid #eee;
margin: 2em 0;
}
.markdown-section iframe {
border: 1px solid #eee;
/* fix horizontal overflow on iOS Safari */
width: 1px;
min-width: 100%;
}
.markdown-section table {
border-collapse: collapse;
border-spacing: 0;
display: block;
margin-bottom: 1rem;
overflow: auto;
width: 100%;
}
.markdown-section th {
border: 1px solid #ddd;
font-weight: bold;
padding: 6px 13px;
}
.markdown-section td {
border: 1px solid #ddd;
padding: 6px 13px;
}
.markdown-section tr {
border-top: 1px solid #ccc;
}
.markdown-section tr:nth-child(2n) {
background-color: #f8f8f8;
}
.markdown-section p.tip {
background-color: #f8f8f8;
border-bottom-right-radius: 2px;
border-left: 4px solid #f66;
border-top-right-radius: 2px;
margin: 2em 0;
padding: 12px 24px 12px 30px;
position: relative;
}
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================================================
FILE: docs/Algorithm/DataStructure/JavaScript.md
================================================
# 十大排序算法集合
## 冒泡排序
通过相邻元素的比较和交换，使得每一趟循环都能找到未有序数组的最大值或最小值。
最好：`O(n)`，只需要冒泡一次数组就有序了。
最坏：`O(n²)`
平均：`O(n²)`
```
function bubbleSort(nums) {
for(let i=0, len=nums.length; i nums[j+1]) {
[nums[j], nums[j+1]] = [nums[j+1], nums[j]];
mark = false;
}
}
if(mark) return;
}
}
```
## 双向冒泡
普通的冒泡排序在一趟循环中只能找出一个最大值或最小值，双向冒泡则是多一轮循环既找出最大值也找出最小值。
```
function bubbleSort_twoWays(nums) {
let low = 0;
let high = nums.length - 1;
while(low < high) {
let mark = true;
// 找到最大值放到右边
for(let i=low; i nums[i+1]) {
[nums[i], nums[i+1]] = [nums[i+1], nums[i]];
mark = false;
}
}
high--;
// 找到最小值放到左边
for(let j=high; j>low; j--) {
if(nums[j] < nums[j-1]) {
[nums[j], nums[j-1]] = [nums[j-1], nums[j]];
mark = false;
}
}
low++;
if(mark) return;
}
}
```
## 选择排序
和冒泡排序相似，区别在于选择排序是将每一个元素和它后面的元素进行比较和交换。
最好：`O(n²)`
最坏：`O(n²)`
平均：`O(n²)`
```
function selectSort(nums) {
for(let i=0, len=nums.length; i nums[j]) {
[nums[i], nums[j]] = [nums[j], nums[i]];
}
}
}
}
```
## 插入排序
以第一个元素作为有序数组，其后的元素通过在这个已有序的数组中找到合适的位置并插入。
最好：`O(n)`，原数组已经是升序的。
最坏：`O(n²)`
平均：`O(n²)`
```
function insertSort(nums) {
for(let i=1, len=nums.length; i= 0 && temp < nums[j-1]) {
nums[j] = nums[j-1];
j--;
}
nums[j] = temp;
}
}
```
## 快速排序
选择一个元素作为基数（通常是第一个元素），把比基数小的元素放到它左边，比基数大的元素放到它右边（相当于二分），再不断递归基数左右两边的序列。
最好：`O(n * logn)`，所有数均匀分布在基数的两边，此时的递归就是不断地二分左右序列。
最坏：`O(n²)` ，所有数都分布在基数的一边，此时划分左右序列就相当于是插入排序。
平均：`O(n * logn)`
参考学习链接：
[算法 3：最常用的排序——快速排序](https://wiki.jikexueyuan.com/project/easy-learn-algorithm/fast-sort.html)
[三种快速排序以及快速排序的优化](https://blog.csdn.net/insistGoGo/article/details/7785038)
### 快速排序之填坑
从右边向中间推进的时候，遇到小于基数的数就赋给左边（一开始是基数的位置），右边保留原先的值等之后被左边的值填上。
```
function quickSort(nums) {
// 递归排序基数左右两边的序列
function recursive(arr, left, right) {
if(left >= right) return;
let index = partition(arr, left, right);
recursive(arr, left, index - 1);
recursive(arr, index + 1, right);
return arr;
}
// 将小于基数的数放到基数左边，大于基数的数放到基数右边，并返回基数的位置
function partition(arr, left, right) {
// 取第一个数为基数
let temp = arr[left];
while(left < right) {
while(left < right && arr[right] >= temp) right--;
arr[left] = arr[right];
while(left < right && arr[left] < temp) left++;
arr[right] = arr[left];
}
// 修改基数的位置
arr[left] = temp;
return left;
}
recursive(nums, 0, nums.length-1);
}
```
### 快速排序之交换
从左右两边向中间推进的时候，遇到不符合的数就两边交换值。
```
function quickSort1(nums) {
function recursive(arr, left, right) {
if(left >= right) return;
let index = partition(arr, left, right);
recursive(arr, left, index - 1);
recursive(arr, index + 1, right);
return arr;
}
function partition(arr, left, right) {
let temp = arr[left];
let p = left + 1;
let q = right;
while(p <= q) {
while(p <= q && arr[p] < temp) p++;
while(p <= q && arr[q] > temp) q--;
if(p <= q) {
[arr[p], arr[q]] = [arr[q], arr[p]];
// 交换值后两边各向中间推进一位
p++;
q--;
}
}
// 修改基数的位置
[arr[left], arr[q]] = [arr[q], arr[left]];
return q;
}
recursive(nums, 0, nums.length-1);
}
```
## 归并排序
递归将数组分为两个序列，有序合并这两个序列。
最好：`O(n * logn)`
最坏：`O(n * logn)`
平均：`O(n * logn)`
参考学习链接：
[图解排序算法(四)之归并排序](https://www.cnblogs.com/chengxiao/p/6194356.html)
```
function mergeSort(nums) {
// 有序合并两个数组
function merge(l1, r1, l2, r2) {
let arr = [];
let index = 0;
let i = l1, j = l2;
while(i <= r1 && j <= r2) {
arr[index++] = nums[i] < nums[j] ? nums[i++] : nums[j++];
}
while(i <= r1) arr[index++] = nums[i++];
while(j <= r2) arr[index++] = nums[j++];
// 将有序合并后的数组修改回原数组
for(let t=0; t= right) return;
// 比起(left+right)/2，更推荐下面这种写法，可以避免数溢出
let mid = parseInt((right - left) / 2) + left;
recursive(left, mid);
recursive(mid+1, right);
merge(left, mid, mid+1, right);
return nums;
}
recursive(0, nums.length-1);
}
```
## 桶排序
取 n 个桶，根据数组的最大值和最小值确认每个桶存放的数的区间，将数组元素插入到相应的桶里，最后再合并各个桶。
最好：`O(n)`，每个数都在分布在一个桶里，这样就不用将数插入排序到桶里了(类似于计数排序以空间换时间)。
最坏：`O(n²)`，所有的数都分布在一个桶里。
平均：`O(n + k)`，k表示桶的个数。
参考学习链接：
[拜托，面试别再问我桶排序了！！！](http://zhuanlan.51cto.com/art/201811/586129.htm)
```
function bucketSort(nums) {
// 桶的个数，只要是正数即可
let num = 5;
let max = Math.max(...nums);
let min = Math.min(...nums);
// 计算每个桶存放的数值范围，至少为1，
let range = Math.ceil((max - min) / num) || 1;
// 创建二维数组，第一维表示第几个桶，第二维表示该桶里存放的数
let arr = Array.from(Array(num)).map(() => Array().fill(0));
nums.forEach(val => {
// 计算元素应该分布在哪个桶
let index = parseInt((val - min) / range);
// 防止index越界，例如当[5,1,1,2,0,0]时index会出现5
index = index >= num ? num - 1 : index;
let temp = arr[index];
// 插入排序，将元素有序插入到桶中
let j = temp.length - 1;
while(j >= 0 && val < temp[j]) {
temp[j+1] = temp[j];
j--;
}
temp[j+1] = val;
})
// 修改回原数组
let res = [].concat.apply([], arr);
nums.forEach((val, i) => {
nums[i] = res[i];
})
}
```
## 基数排序
使用十个桶 0-9，把每个数从低位到高位根据位数放到相应的桶里，以此循环最大值的位数次。**但只能排列正整数，因为遇到负号和小数点无法进行比较**。
最好：`O(n * k)`，k表示最大值的位数。
最坏：`O(n * k)`
平均：`O(n * k)`
参考学习链接：
[算法总结系列之五: 基数排序(Radix Sort)](https://www.cnblogs.com/sun/archive/2008/06/26/1230095.html)
[]()
```
function radixSort(nums) {
// 计算位数
function getDigits(n) {
let sum = 0;
while(n) {
sum++;
n = parseInt(n / 10);
}
return sum;
}
// 第一维表示位数即0-9，第二维表示里面存放的值
let arr = Array.from(Array(10)).map(() => Array());
let max = Math.max(...nums);
let maxDigits = getDigits(max);
for(let i=0, len=nums.length; i=0; i--) {
// 循环每一个桶
for(let j=0; j<=9; j++) {
let temp = arr[j]
let len = temp.length;
// 根据当前的位数i把桶里的数放到相应的桶里
while(len--) {
let str = temp[0];
temp.shift();
arr[str[i]].push(str);
}
}
}
// 修改回原数组
let res = [].concat.apply([], arr);
nums.forEach((val, index) => {
nums[index] = +res[index];
})
}
```
## 计数排序
以数组元素值为键，出现次数为值存进一个临时数组，最后再遍历这个临时数组还原回原数组。因为 JavaScript 的数组下标是以字符串形式存储的，所以**计数排序可以用来排列负数，但不可以排列小数**。
最好：`O(n + k)`，k是最大值和最小值的差。
最坏：`O(n + k)`
平均：`O(n + k)`
```
function countingSort(nums) {
let arr = [];
let max = Math.max(...nums);
let min = Math.min(...nums);
// 装桶
for(let i=0, len=nums.length; i 0) {
nums[index++] = i;
arr[i]--;
}
}
}
```
## 计数排序优化
把每一个数组元素都加上 min 的相反数，来避免特殊情况下的空间浪费，通过这种优化可以把所开的空间大小从 max+1 降低为 max-min+1，max 和 min 分别为数组中的最大值和最小值。
比如数组 [103, 102, 101, 100]，普通的计数排序需要开一个长度为 104 的数组，而且前面 100 个值都是 undefined，使用该优化方法后可以只开一个长度为 4 的数组。
```
function countingSort(nums) {
let arr = [];
let max = Math.max(...nums);
let min = Math.min(...nums);
// 加上最小值的相反数来缩小数组范围
let add = -min;
for(let i=0, len=nums.length; i 0) {
nums[index++] = i;
temp--;
}
}
}
```
## 堆排序
根据数组建立一个堆（类似完全二叉树），每个结点的值都大于左右结点（最大堆，通常用于升序），或小于左右结点（最小堆，通常用于降序）。对于升序排序，先构建最大堆后，交换堆顶元素（表示最大值）和堆底元素，每一次交换都能得到未有序序列的最大值。重新调整最大堆，再交换堆顶元素和堆底元素，重复 n-1 次后就能得到一个升序的数组。
最好：`O(n * logn)`，logn是调整最大堆所花的时间。
最坏：`O(n * logn)`
平均：`O(n * logn)`
参考学习链接：
[常见排序算法 - 堆排序 (Heap Sort)](http://bubkoo.com/2014/01/14/sort-algorithm/heap-sort/)
[图解排序算法(三)之堆排序](https://www.cnblogs.com/chengxiao/p/6129630.html)
```
function heapSort(nums) {
// 调整最大堆，使index的值大于左右节点
function adjustHeap(nums, index, size) {
// 交换后可能会破坏堆结构，需要循环使得每一个父节点都大于左右结点
while(true) {
let max = index;
let left = index * 2 + 1; // 左节点
let right = index * 2 + 2; // 右节点
if(left < size && nums[max] < nums[left]) max = left;
if(right < size && nums[max] < nums[right]) max = right;
// 如果左右结点大于当前的结点则交换，并再循环一遍判断交换后的左右结点位置是否破坏了堆结构（比左右结点小了）
if(index !== max) {
[nums[index], nums[max]] = [nums[max], nums[index]];
index = max;
}
else {
break;
}
}
}
// 建立最大堆
function buildHeap(nums) {
// 注意这里的头节点是从0开始的，所以最后一个非叶子结点是 parseInt(nums.length/2)-1
let start = parseInt(nums.length / 2) - 1;
let size = nums.length;
// 从最后一个非叶子结点开始调整，直至堆顶。
for(let i=start; i>=0; i--) {
adjustHeap(nums, i, size);
}
}
buildHeap(nums);
// 循环n-1次，每次循环后交换堆顶元素和堆底元素并重新调整堆结构
for(let i=nums.length-1; i>0; i--) {
[nums[i], nums[0]] = [nums[0], nums[i]];
adjustHeap(nums, 0, i);
}
}
```
## 希尔排序
通过某个增量 gap，将整个序列分给若干组，从后往前进行组内成员的比较和交换，随后逐步缩小增量至 1。希尔排序类似于插入排序，只是一开始向前移动的步数从 1 变成了 gap。
最好：`O(n * logn)`，步长不断二分。
最坏：`O(n * logn)`
平均：`O(n * logn)`
参考学习链接：
[图解排序算法(二)之希尔排序](https://www.cnblogs.com/chengxiao/p/6104371.html)
```
function shellSort(nums) {
let len = nums.length;
// 初始步数
let gap = parseInt(len / 2);
// 逐渐缩小步数
while(gap) {
// 从第gap个元素开始遍历
for(let i=gap; i=0; j-=gap) {
if(nums[j] > nums[j+gap]) {
[nums[j], nums[j+gap]] = [nums[j+gap], nums[j]];
}
else {
break;
}
}
}
gap = parseInt(gap / 2);
}
}
```
================================================
FILE: docs/Algorithm/DataStructure/Others/NextNodeInOrderTree.md
================================================
``` python
8
/
5
/ \
3 6
/ \ \
1 4 7
class Node {
Node parent, lc, rc;
int val;
}
```
1. 首先判断其自有无右孩子，若有，则取其右子树的最左节点; 若无，则开始2
2. 它是其父亲节点的左孩子，则其父亲节点
2. 它是其父亲节点的右孩子，则从其父亲开始往上追溯到第一个向右的节点，如果没有这个节点或者说没有父亲节点，则无下一个节点，若有则取之
```python
def nextNode(node):
def leftest(node):
while node.lc:
node = node.lc
return node
if node.rc:
return leftest(node.rc)
if not node.parent:
return None
if node == node.parent.lc:
return node.parent
elif node == node.parent.rc:
while node.parent.parent:
if node.parent != node.parent.parent.lc:
node = node.parent
else:
return node.parent.parent
return None
```
================================================
FILE: docs/Algorithm/DataStructure/Others/TopKWords.java
================================================
import java.io.*;
import java.util.*;
/* display the most frequent K words in the file and the times it appear
in the file – shown in order (ignore case and periods) */
public class TopKWords {
static class CountWords {
private String fileName;
public CountWords(String fileName) {
this.fileName = fileName;
}
public Map getDictionary() {
Map dictionary = new HashMap<>();
FileInputStream fis = null;
try {
fis = new FileInputStream(fileName); // open the file
int in = 0;
String s = ""; // init a empty word
in = fis.read(); // read one character
while (-1 != in) {
if (Character.isLetter((char)in)) {
s += (char)in; //if get a letter, append to s
} else {
// this branch means an entire word has just been read
if (s.length() > 0) {
// see whether word exists or not
if (dictionary.containsKey(s)) {
// if exist, count++
dictionary.put(s, dictionary.get(s) + 1);
} else {
// if not exist, initiate count of this word with 1
dictionary.put(s, 1);
}
}
s = ""; // reInit a empty word
}
in = fis.read();
}
return dictionary;
} catch (IOException e) {
e.printStackTrace();
} finally {
try {
// you always have to close the I/O streams
fis.close();
} catch (IOException e) {
e.printStackTrace();
}
}
return null;
}
}
public static void main(String[] args) {
// you can replace the filePath with yours, e.g.
// CountWords cw = new CountWords("/Users/lisanaaa/Desktop/words.txt");
CountWords cw = new CountWords("/words.txt");
Map dictionary = cw.getDictionary(); // get the words dictionary: {word: frequency}
// we change the map to list for convenient sort
List> list = new ArrayList<>(dictionary.entrySet());
// sort by lambda valueComparator
list.sort(Comparator.comparing(
m -> m.getValue())
);
Scanner input = new Scanner(System.in);
int k = input.nextInt();
while (k > list.size()) {
System.out.println("Retype a number, your number is too large");
input = new Scanner(System.in);
k = input.nextInt();
}
for (int i = 0; i < k; i++) {
System.out.println(list.get(list.size() - i - 1));
}
}
}
================================================
FILE: docs/Algorithm/DataStructure/Others/words.txt
================================================
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam non pharetra purus. Quisque non tristique risus. Nulla ultricies eget nunc a volutpat. Aliquam semper, eros sit amet semper pellentesque, elit magna auctor ligula, sit amet vulputate nunc arcu sit amet ipsum. Aenean efficitur, felis ut tincidunt semper, sem sapien facilisis felis, et elementum ipsum risus sit amet metus. Pellentesque et orci at odio hendrerit lobortis vitae non felis. Phasellus nunc eros, ultricies sed dolor vel, ultricies mattis neque. Mauris placerat fringilla libero id efficitur. Donec nec imperdiet augue. Sed sit amet risus in turpis scelerisque rutrum. Ut sed pretium dolor. Donec metus nisl, blandit quis augue et, interdum suscipit metus. Morbi rutrum eros vel lacus aliquet, et maximus erat dapibus. Vivamus est justo, sagittis a augue id, vulputate vehicula nunc. Praesent mattis eros sapien, ac sagittis urna accumsan sit amet. Proin non dui tincidunt, tristique nisi in, vestibulum lorem.
Quisque fermentum justo lacus, sit amet tempus lectus congue eu. Nulla sed quam nec nulla consequat tincidunt. Sed sed nunc diam. Integer ex ante, accumsan id fermentum a, interdum eu sapien. Aliquam justo dui, luctus vel ligula in, lacinia ornare turpis. Praesent leo purus, fringilla ut lobortis et, porta ac urna. Mauris id velit porta, iaculis felis non, sagittis nunc. Quisque non condimentum nisl, vitae venenatis urna. Nam commodo euismod felis, ac efficitur turpis scelerisque nec. Phasellus sagittis nec lacus eu bibendum. Suspendisse finibus vestibulum quam, quis volutpat ante. Duis nibh ligula, dapibus at est sed, tincidunt convallis augue. Pellentesque non consequat mi. Curabitur consequat imperdiet efficitur.
Mauris ipsum arcu, fermentum in urna ultricies, venenatis vehicula nisl. Donec viverra non tellus sit amet porta. Phasellus ornare magna eget condimentum mollis. In hac habitasse platea dictumst. Proin in nibh venenatis, fermentum neque nec, commodo urna. In eget condimentum risus, ac interdum dolor. Sed ut neque sapien. Proin nulla diam, lobortis sed ultrices eget, blandit ut libero.
Fusce at varius dui. Quisque viverra vulputate consectetur. Quisque sagittis id ante a vestibulum. Phasellus vel lobortis lectus. Duis vestibulum, quam vel congue elementum, lacus nibh efficitur odio, consectetur dapibus ipsum velit at diam. Duis eu nunc id diam tempor vestibulum sed luctus arcu. Nunc eu luctus ex. Morbi et commodo eros, non suscipit enim. Ut fringilla odio nec tincidunt scelerisque. Nam quis elit cursus, ullamcorper lorem id, convallis dui. Mauris elementum tortor odio, nec imperdiet nisl bibendum eget. Suspendisse potenti.
Cras ut efficitur enim. Sed consequat non ante id euismod. Ut at magna viverra, aliquam purus a, lobortis mi. Donec hendrerit odio eu nunc imperdiet, quis pharetra sapien volutpat. Morbi leo libero, egestas vitae tortor eget, dictum volutpat augue. Donec arcu lacus, tristique eu posuere ac, pharetra vel ante. Nunc efficitur arcu elit, quis semper risus vestibulum eu.
================================================
FILE: docs/Algorithm/DataStructure/Queue/README.md
================================================
## Queue Implementation
### Queue using Linked List
```python
class ListNode(object):
def __init__(self, x):
self.val = x
self.next = None
class Queue(object):
def __init__(self):
self.head = None
self.tail = None
self.size = 0
def __str__(self):
'''print Queue'''
res = ''
cur = self.head
while cur:
res += str(cur.val)
res += ' '
cur = cur.next
return res
def push(self, val):
node = ListNode(val)
if not self.head:
self.head = self.tail = node
else:
self.tail.next = node
self.tail = node
self.size += 1
def popleft(self):
if not self.head:
return None
pop_val = self.head.val
self.head = self.head.next
if self.head == None:
self.tail = None
self.size -= 1
return pop_val
def peekleft(self):
if self.head:
return self.head.val
return None
def get_size(self):
return self.size
def is_empty(self):
return self.size == 0
```
================================================
FILE: docs/Algorithm/DataStructure/README.md
================================================
## 二分查找
* 二分查找: [BinarySearch.py](/src/py3.x/DataStructure/BinarySearch.py)
## 八大排序算法
> Python 模版
| 名称 | 动图 | 代码 |
| --- | --- | --- |
| 冒泡排序 |  | [BubbleSort.py](/src/py3.x/DataStructure/BubbleSort.py) |
| 插入排序 |  | [InsertSort.py](/src/py3.x/DataStructure/InsertionSort.py) |
| 选择排序 |  | [SelectionSort.py](/src/py3.x/DataStructure/SelectionSort.py) |
| 快速排序 |  | [QuickSort.py](/src/py3.x/DataStructure/QuickSort.py) |
| 希尔排序 |  | [ShellSort.py](/src/py3.x/DataStructure/ShellSort.py) |
| 归并排序 |  | [MergeSort.py](/src/py3.x/DataStructure/MergeSort.py) |
| 基数排序 |  | [RadixSort.py](/src/py3.x/DataStructure/RadixSort.py) |
补充: [JavaScript 模块](https://github.com/apachecn/Interview/tree/master/docs/Algorithm/DataStructure/JavaScript.md)
================================================
FILE: docs/Algorithm/DataStructure/Stack/README.md
================================================
## Stack Implementation
### Stack using Linked List
```python
class ListNode(object):
def __init__(self, x):
self.val = x
self.next = None
class Stack(object):
def __init__(self):
self.head = None
self.size = 0
def push(self, val):
new_node = ListNode(val)
new_node.next = self.head
self.head = new_node
self.size += 1
def pop(self):
if self.head == None:
return None
pop_val = self.head.val
self.head = self.head.next
self.size -= 1
return pop_val
def peek(self):
return self.head.val
def get_size(self):
return self.size
def is_empty(self):
return self.size == 0
```
================================================
FILE: docs/Algorithm/DataStructure/Summarization/Binary Search.md
================================================
### Binary Search
```python
def binarySearch(nums, target):
l, r = 0, len(nums) -1
while l <= r:
mid = l + ((r-l) >> 2)
if nums[mid] > target:
r = mid - 1
elif nums[mid] < target:
l = mid + 1
else:
return mid
return -1
```
================================================
FILE: docs/Algorithm/DataStructure/Summarization/DFS和BFS.md
================================================
# Graph Search，DFS, BFS
## DFS/BFS
可以让stack/queue记录更多一些的东西，因为反正stack/queue更像通用结构
```
A
/ \
C B
\ / \
\ D E
\ /
F
graph = {'A': set(['B', 'C']),
'B': set(['A', 'D', 'E']),
'C': set(['A', 'F']),
'D': set(['B']),
'E': set(['B', 'F']),
'F': set(['C', 'E'])}
```
### DFS
迭代版本
```python
def dfs(graph, start): # iterative
visited, stack = [], [start]
while stack:
vertex = stack.pop()
if vertex not in visited:
visited.append(vertex)
stack.extend(graph[vertex] - set(visited))
return visited
print(dfs(graph, 'A')) # ['A', 'C', 'F', 'E', 'B', 'D'] 这只是其中一种答案
```
递归版本
```python
def dfs(graph, start, visited=None): # recursive
if visited is None:
visited = []
print('visiting', start)
visited.append(start)
for next in graph[start]:
if next not in visited:
dfs(graph, next, visited)
return visited
print(dfs(graph, 'A')) # ['A', 'C', 'F', 'E', 'B', 'D'] 这只是其中一种答案
```
迭代打印出从出发点到终点的路径
```python
def dfs_paths(graph, start, goal): # iterative
stack = [(start, [start])]
while stack:
(vertex, path) = stack.pop()
for next in graph[vertex] - set(path):
if next == goal:
yield path + [next]
else:
stack.append((next, path + [next]))
print(list(dfs_paths(graph, 'A', 'F'))) # [['A', 'C', 'F'], ['A', 'B', 'E', 'F']]
```
递归打印出从出发点到终点的路径
```python
def dfs_paths(graph, start, goal, path=None): # recursive
if path is None:
path = [start]
if start == goal:
yield path
for next in graph[start] - set(path):
yield from dfs_paths(graph, next, goal, path + [next])
print(list(dfs_paths(graph, 'C', 'F'))) # [['C', 'A', 'B', 'E', 'F'], ['C', 'F']]
```
### BFS
迭代版本，和DFS唯一的区别就是pop(0)而不是pop()
```python
def bfs(graph, start): # iterative
visited, queue = [], [start]
while queue:
vertex = queue.pop(0)
if vertex not in visited:
visited.append(vertex)
queue.extend(graph[vertex] - set(visited))
return visited
print(bfs(graph, 'A')) # ['A', 'C', 'B', 'F', 'D', 'E']
```
返回两点之间的所有路径，第一个一定是最短的
```python
def bfs_paths(graph, start, goal):
queue = [(start, [start])]
while queue:
(vertex, path) = queue.pop(0)
for next in graph[vertex] - set(path):
if next == goal:
yield path + [next]
else:
queue.append((next, path + [next]))
print(list(bfs_paths(graph, 'A', 'F'))) # [['A', 'C', 'F'], ['A', 'B', 'E', 'F']]
```
知道了这个特性，最短路径就很好搞了
```python
def bfs_paths(graph, start, goal):
queue = [(start, [start])]
while queue:
(vertex, path) = queue.pop(0)
for next in graph[vertex] - set(path):
if next == goal:
yield path + [next]
else:
queue.append((next, path + [next]))
def shortest_path(graph, start, goal):
try:
return next(bfs_paths(graph, start, goal))
except StopIteration:
return None
print(shortest_path(graph, 'A', 'F')) # ['A', 'C', 'F']
```
#### Improvement/Follow up
1. 一旦BFS/DFS与更具体的，更有特性的data structure结合起来，比如binary search tree，那么BFS/DFS会针对这个tree traversal显得更有特性。
2. it's worth mentioning that there is an optimized queue object in the collections module called [deque](https://docs.python.org/2/library/collections.html#collections.deque)) for which removing items from the beginning ( or popleft ) takes constant time as opposed to O(n) time for lists.
### Resources
1. [Depth-and Breadth-First Search](https://jeremykun.com/2013/01/22/depth-and-breadth-first-search/)
2. [Edd Mann](http://eddmann.com/posts/depth-first-search-and-breadth-first-search-in-python/)
3. [graph - Depth-first search in Python](https://codereview.stackexchange.com/questions/78577/depth-first-search-in-python)
================================================
FILE: docs/Algorithm/DataStructure/Summarization/Data Structure and Algorthim Review.md
================================================
### Data Structure and Algorthim Review
- [x] Binary Search Tree
- [x] 插入
• 如果树为空，创建一个叶子节点，令该节点的key = k;
• 如果k小于根节点的key，将它插入到左子树中;
• 如果k大于根节点的key，将它插入到右子树中。
- [x] 遍历
• 前序: 根，左，右
• 中序：左，根，右 **有序**
• 后序：左，右，根
- [x] 搜索
- look up ： 是否存在
• 如果树为空，搜索失败;
• 如果根节点的key等于待搜索的值，搜索成功，返回根节点作为结果;
• 如果待搜索的值小于根节点的key，继续在左子树中递归搜索;
• 否则，待搜索的值大于根节点的key，继续在右子树中递归搜索。
- 最大元素和最小元素
• 最右和最左
- 前驱(Successor)和后继(predecessor)
给定元素x，它的后继元素y是满足y > x的最小值
• 如果x所在的节点有一个非空的右子树，则右子树中的最小值就是答案
• 否则我们需要向上回溯，找到最近的一个祖先，使得该祖先的左侧孩子，也为x的祖 先。
- [x] 删除
• 如果x没有子节点，或者只有一个孩子，直接将x“切下”;
• 否则，x有两个孩子，我们用其右子树中的最小值替换掉x，然后将右子树中的这一最小值“切掉”。
- [x] 递归
- [x] 入门
- 回文
- 阶乘 factorial， 慕指数
- 分形
- Tower of Hanoi
- [x] 排列 Permutation
- [x] 子集 Subsets
- [ ] backtracking
- [x] dynamic programming
- coin change
- longest common subsequence
- edit distance
-[ ] majority element
- [ ] 随机
- 水塘抽样
- 洗牌
-[ ] 荷兰旗问题
-[ ] quick select
-[ ] median of two sorted array
-[ ] regular expression
================================================
FILE: docs/Algorithm/DataStructure/Summarization/Dynamic Programming.md
================================================
### Dynamic Programming
- Fibonacci Numbers
- Shortest Path (no cycles)
- subproblems
- guessing
- relate subproblems
- recurse & memoize (bulid DP table)
- solve original problem
感觉DP有几类：
- 容易写出递推公式的
- 画表更容易理解的
* DP ≈ “controlled brute force”
* DP ≈ recursion + re-use
================================================
FILE: docs/Algorithm/DataStructure/Summarization/Java各种类型的转换.md
================================================
之前在写java程序的时候，经常会遇到很多的需要需要转换基础数据类型的情况，然后我就一直去记录这些情况，今天做了一下总结，当然转换的方法肯定不止我写的这些，有的我可能只会写其中的一种，以后再遇到其他的情况的话，我会慢慢来补充，希望这篇文章会对大家能有所帮助。
------
# String的转换
首先介绍一下String类型的转换，一般遇到的情况可能会有以下几种：Strng转int，String转long，String转byte数组，String转float，下面主要介绍这四种情况。
## String转int
把String类型转换为int类型，常用的有以下三种方法：
```
public class StringToInt {
public static void main(String[] args) {
String number = "123456";
int num1 = Integer.parseInt(number);//使用Integer的parseInt方法
int num2 = new Integer(number);//强制转换
int num3 = Integer.valueOf(number).intValue();//先转Integer类型，再调用intValue()转为int
}
}
```
## String转long
把String类型转换为long类型的方法跟上面的方法类似。
```
public class StringToLong {
public static void main(String[] args) {
String number = "1234567890";
long num1 = Long.parseLong(number);//调用Long类型的parseLong方法
long num2 = new Long(number);//强制转换
long num3 = Long.valueOf(number).longValue();//先转换Long类型，再使用longValue方法转为long
}
}
```
## String转float
把String类型转换为float类型的方法也跟上面的类似。
```
public class StringToFloat {
public static void main(String[] args) {
String number = "1234.202";
float num1 = Float.parseFloat(number);//调用Float的parseFloat方法
float num2 = new Float(number);//强制转换
float num3 = Float.valueOf(number).floatValue();//先转为Float类型再使用floatValue转为float
}
}
```
## String转byte[]
String类型转byte数组方法一般使用String类自带的`getBytes()`方法。
```
public class StringToByte {
public static void main(String[] args) {
byte[] num = new byte[200];
String number = "1234567890";
num = number.getBytes();
}
}
```
这里补充一个path类型转换为String类型的方法：
```
String fileName=path.getFileName().toString();
```
------
# long类型转换
long类型的转换，这一部分用的情况也很多，下面介绍几种常见的情况。
## long转String
long类型转String类型，这里主要介绍三种方法：
```
public class LongToString {
public static void main(String[] args) {
long number = 1234567890l;
String num1 = Long.toString(number);//Long的tostring方法
String num2 = String.valueOf(number);//使用String的valueOf方法
String num3 = "" + number;//这个应该属于强制转换吧
}
}
```
## long转int
long类型转换为int类型，这里也主要介绍三种方法：
```
public class LongToInt {
public static void main(String[] args) {
long number = 121121121l;
int num1 = (int) number;// 强制类型转换
int num2 = new Long(number).intValue();// 调用intValue方法
int num3 = Integer.parseInt(String.valueOf(number));// 先把long转换位字符串String，然后转换为Integer
}
}
```
## long与byte数组的相互转换
一直都感觉byte数组转换比较繁琐，这里也不再叙述，我就给出一篇别人的博客让大家作为参考吧，这里面byte数组与多种数据类型的转换——[ java Byte和各数据类型(short,int,long,float,double)之间的转换](http://blog.csdn.net/cshichao/article/details/9813973)
------
# int类型的转换
int类型的转换也是我们经常使用的情况，下面也主要介绍几种常见的情况。
## int转String
int类型转换为String类型与long转String的类似，一般也有以下三种方法。
```
public class IntToString {
public static void main(String[] args) {
int number = 121121;
String num1 = Integer.toString(number);//使用Integer的toString方法
String num2 = String.valueOf(number);//使用String的valueOf方法
String num3 = "" + number;//也是强制转换吧
}
}
```
## int与Byte的相互转换
关于int类型与byte[]数组的转换，一般情况下，我们使用条件都是在这里转换过来，在另外一个地方就要转换回来，这里介绍两种int与byte数组互相转换的方式。
```
//int类型转换为byte[]数组
public static byte[] intToByteArray(int i) {
byte[] result = new byte[4];
// 由高位到低位
result[0] = (byte) ((i >> 24) & 0xFF);
result[1] = (byte) ((i >> 16) & 0xFF);
result[2] = (byte) ((i >> 8) & 0xFF);
result[3] = (byte) (i & 0xFF);
return result;
}
//byte数组转换为int类型
public static int byteArrayToInt(byte[] bytes) {
int value = 0;
// 由高位到低位
for (int i = 0; i < 4; i++) {
int shift = (4 - 1 - i) * 8;
value += (bytes[i] & 0x000000FF) << shift;// 往高位游
}
return value;
}
```
还有一种为：
```
//int类型转换为byte[]数组
public static byte[] intToByteArray(int x) {
byte[] bb = new byte[4];
bb[3] = (byte) (x >> 24);
bb[2] = (byte) (x >> 16);
bb[1] = (byte) (x >> 8);
bb[0] = (byte) (x >> 0);
return bb;
}
//byte数组转换为int类型
public static int byteArrayToInt(byte[] bb) {
return (int) ((((bb[3] & 0xff) << 24) | ((bb[2] & 0xff) << 16) | ((bb[1] & 0xff) << 8) | ((bb[0] & 0xff) << 0)));
}
```
## int转long
int类型转换为long类型的情况并不是大多，这里主要接收几种转换方法：
```
public class IntToLong {
public static void main(String[] args) {
int number = 123111;
long num1 = (long) number;//强制
long num2 = Long.parseLong(new Integer(number).toString());//先转String再进行转换
long num3 = Long.valueOf(number);
}
}
```
## int转Interger
int类型转换为Interger类型的情况，我是基本上每怎么遇到过，在这里也上网查询一些资料找到了两种方法。
```
public class IntToInterge {
public static void main(String[] args) {
int number = 123456;
Integer num1 = Integer.valueOf(number);
Integer num2 = new Integer(number);
}
}
```
------
# byte数组的转换
关于byte数组的转换，上面有几个都是它们只见相互转换的，所以这里就不再介绍那么多，只介绍一个byte数组转换String类型的方法，其他的类型可以通过String类型再进行转换。
byte数组转String类型的方法经常用的可能就是下面这种方法。
```
public class ByteToString {
public static void main(String[] args) {
byte[] number = "121121".getBytes();
String num1 = new String(number);
}
}
```
------
最后简单补充以下Java基本数据类型的一些知识：
| 类型 | 字节数 | 类名称 | 范围 |
| ------ | ---- | -------- | ---------------------------------------- |
| int | 4字节 | Interger | -2147483648 ~ 2147483647 |
| short | 2字节 | Short | -32768 ～ 32767 |
| long | 8字节 | Long | -9223372036854775808 ～ 9223372036854775807 |
| byte | 1字节 | Byte | -128 ～ 127 |
| float | 4字节 | Float | |
| double | 8字节 | Double | |
================================================
FILE: docs/Algorithm/DataStructure/Summarization/LinkedList技巧.md
================================================
# LinkedList
结点定义如下：
class ListNode(object):
def __init__(self, x):
self.val = x
self.next = None
可以使用的技巧包括:
## Dummy head
有的时候因为边界条件，需要判定是否是list的head，因为处理起来会有些不同，而创造一个dummy head则可以极大的解决一些问题。
```
dummy = ListNode(-1)
dummy.next = head
```
## 双指针
- 19. Remove Nth Node From End of List
两个指针p,q， q先走n步，然后p和q一起走，直到q走到结点，删除p.next解决。
理解： 先走了n步，q始终在p前方n个，这样q走到末尾，p的下一个则是距离尾端n个的，画个图还是容易理解。
- 160. Intersection of Two Linked Lists
如果两个linkedlist有intersection的话，可以看到，其实如果一开始我们就走到b2的话，那么我们就可以两个pointer一个一个的对比，到哪一个地址一样，接下来就是intersection部分。
就一开始把长的那条list走掉多余部分。
还有这里保证了是无环的状况
```
A: a1 → a2
↘
c1 → c2 → c3
↗
B: b1 → b2 → b3
```
## 快慢指针
- 141. Linked List Cycle
用两个指针，一个每次走两步，一个每次走一步，如果慢的最终和快的相遇，那么说明有环，否则没有环，直观的理解是如果两个跑的速度不一的人进操场跑步，那么最终慢的会追上快的.
## Reverse Linked List
- 206. Reverse Linked List
loop版本用prev, cur ,nxt 三个指针过一遍，recursion版本如下，可以再消化消化
```
class Solution(object):
def reverseList(self, head):
"""
:type head: ListNode
:rtype: ListNode
"""
return self.doReverse(head, None)
def doReverse(self, head, newHead):
if head == None:
return newHead
nxt = head.next
head.next = newHead
return self.doReverse(nxt, head)
```
## 寻找LinkedList中间项
依旧使用双指针，快慢指针：快指针每次走两步，慢指针每次走一步，快指针如果到头了，那么慢指针也会在中间了，这个中间可以考量，如果是奇数的话必然是中间。
如果是偶数则是偏前面的中间一项
```
1 -> 2 -> 3 -> 4: 2
1 -> 2 -> 3 -> 4 -> 5 -> 6 : 3
1 -> 2 -> 3 -> 4 -> 5 -> 6 -> 7 -> 8 : 4
```
算法：
```
def findMid(head):
if head == None or head.next == None:
return head
slow = head
fast = head
while fast.next and fast.next.next:
slow = slow.next
fast = fast.next.next
return slow
```
================================================
FILE: docs/Algorithm/DataStructure/Summarization/Python刷题技巧笔记.py
================================================
from __future__ import print_function
import heapq
# python有无数奇技淫巧和许多人不知道的秘密，这里用简洁的语言一条条表述出来，不断更新, 大家一起贡献！
# 1. python 排序
# 用lst.sort() 而不是nlst = sorted(lst)， 区别在于lst.sort()是 in-place sort，改变lst, sorted会创建新list，成本比较高。
# 2. xrange和range的区别
# range会产生list存在memory中，xrange更像是生成器，generate on demand所以有的时候xrange会更快
# 3. python处理矩阵
row = len(matrix)
col = len(matrix[0]) if row else 0
# 这样写通用的原因是， 当matrix = [], row = 0, col = 0
# 4. python列表生成式
lst = [0 for i in range(3)] # lst = [0,0,0]
lst = [[0 for i in range(3)] for j in range(2)] # lst = [[0, 0, 0], [0, 0, 0]]
# 下面这种写法危险：
# lst1 = [ 0, 0, 0 ]
# lst2 = [lst1] * 2 # lst2 = [ [0,0,0] , [0,0,0] ]
# lst2[0][0] = 1 # lst2 = [ [1,0,0], [1,0,0]]
# 因为lst1是object，改一个相当于全改, 这样写会踩坑
# 5. D.get(key, default)
# 如果这个key 没有在dict里面，给它一个默认值：
D = {}
if 1 in D:
val = D[1]
else :
val = 0
# 等同于这样写：
val = D.get(1, 0)
# 6. 字典赋值
if key in D:
D[key].append(1)
else :
D[key] = []
# 7. 字符串反转
# python字符串没有reverse函数，只能str[::-1]
string[::-1]
# python的list可以直接reverse()，因此也可以借用这个特性
"".join([string].reverse())
# 8. 快速统计
import collections
lst = [1, 1, 1, 2, 3, 4, 5, 5]
collections.Counter(lst) # Counter({1: 3, 5: 2, 2: 1, 3: 1, 4: 1})
# 9. python自带小顶堆heapq
# Python有built-in heap, 默认min heap.
heapq.heappush(heap, item) # 把item添加到heap中（heap是一个列表）
heapq.heappop(heap) # 把堆顶元素弹出，返回的就是堆顶
heapq.heappushpop(heap, item) # 先把item加入到堆中，然后再pop，比heappush()再heappop()要快得多
heapq.heapreplace(heap, item) # 先pop，然后再把item加入到堆中，比heappop()再heappush()要快得多
heapq.heapify(x) # 将列表x进行堆调整，默认的是小顶堆
heapq.merge(*iterables) # 将多个列表合并，并进行堆调整，返回的是合并后的列表的迭代器
heapq.nlargest(n, iterable, key=None) # 返回最大的n个元素（Top-K问题）
heapq.nsmallest(n, iterable, key=None) # 返回最小的n个元素（Top-K问题）
# 如何来用它实现max heap呢，看到过一个有意思的方法是把key取负，比如把100变成-100，5变成-5
import heapq
mylist = [1, 2, 3, 4, 5, 10, 9, 8, 7, 6]
largest = heapq.nlargest(3, mylist) # [10, 9, 8]
smallest = heapq.nsmallest(3, mylist) # [1, 2, 3]
# 10. 双端队列deque [http://stackoverflow.com/questions/4098179/anyone-know-this-python-data-structure]
# 可以很简单的.popleft(), .popright(), .appendleft(), .appendright(),最关键的是时间是O(1), 而用list来模拟队列是O(n)的时间复杂度
# 还有很好用的rotate函数，
# 一个简单的跑马灯程序
import sys
import time
from collections import deque
fancy_loading = deque('>--------------------')
while True:
print('\r%s' % ''.join(fancy_loading))
fancy_loading.rotate(1)
sys.stdout.flush()
time.sleep(0.08)
# 11. 用yield 不用return，可以返回一个generator
# 12. 符号～的巧妙应用
for i in range(n):
# 这里的```[~i]``` 意思就是 ```[n-1-i]```
a[~i] = 1
================================================
FILE: docs/Algorithm/DataStructure/Summarization/Recusrion & BackTracking.md
================================================
#Recusrion & BackTracking
##Recusrion
### DrawFractal
```
void DrawFractal(double x, double y, double w, double h)
{
DrawTriangel(x, y, w, h);
if(w < .2 || h < .2) return ;
double halfH = h/2;
double halfw = w/2;
DrawFractal(x, y, halfW, halfH);
DrawFractal(x + halfW/2, y + halfH, halfW, halfH);
DrawFractal(x + halfW, y, halfW, halfH);
}
```
Sierpinski triangle更伪码的写法：
```
void DrawFractal (x, y, w, h){
if (too small) return ;
DrawTriangle(x, y, w, h);
DrawFractal(.left);
DrawFractal(.top);
DrawFractal(.right);
}
```
实际上老师故意调了里面几句代码的顺序，让来看到虽然结果相同，但是画的过程是不一样的。
然后老师还在黑板上画了过程树，分枝是怎样的，实际上当学到DFS的preOrder， inOrder 和 postOrder的时候会更印象深刻。
一个分支走完之后再回去走另一些。
### DrawMondrian
```
void DrawMondrian(double x, double y, double w, double h){
if(w < 1 || h < 1) return ;// base case
FillRectangle(x,y,w,h,RandomColor()); // fill background
switch(RandomInteger(0, 2)){
case 0: // do nothing
break;
case 1: // bisect vertically
double midX = RandomReal(0,w);
DrawBlackLine( x + midX, y, h);
DrawMondrian(x, y, midX, h);
DrawMondrian(x + midx, y, w- midX, h);
break;
case 2: // bisect horizontally
double midY = RandomReal(0,h);
DrawBlackLine( x, y+ midY, h);
DrawMondrian(x, y, w, midY);
DrawMondrian(x, y+midY,w, midY);
break;
}
}
```
### The tower of Hanoi
```
void MoveTower(int n, char src, char dst, char tmp){
if (n > 0){
MoveTower(n - 1, src, tmp, dst );
MoveSingleDisk(src, dst);
MoveTower(n -1, tmp, dst, src);
}
}
```
### Permutation
老师说permutation 和 subset 是 mother problems of all recursion.
given a string， print out its all permutations
思路如下：
- 使用了的string sofar，以及还未使用的string rest
- 一开始rest就是给的string本身，然后sofar是空
- 每次挑一个rest里面的char，然后递归的再把rest剩下的拿来permutation,这样每次都会有一个char从rest shuffle到sofar
- n 次之后 rest为空，制造了一个permutation
```
void RecPermute(string sofar, string rest){
if(rest = ""){
cout << soFar << endl;
} else {
for(int i = 0 ; i < rest.length(); i++){
string next = soFar + rest[i];
string remaining = rest.substr(0,i) + rest.substr(i+1);
RecPermute(next, remaining);
}
}
}
// "wrapper" function
void ListPermutations(string s)
{
RecPermute("",s);
}
```
老师的黑板图真的是击中要害。
因为老师强调的是，也要用mind来trace它是如何操作的。
### Subsets
```
void RecSubsets(string soFar, string rest)
{
if(rest = "")
cout << soFar << endl;
else {
// add to subset, remove from rest, recur
RecSubsets(soFar + rest[0],rest.substr(1));
//don't add to substr, remove from rest, recur
RecSubsets(soFar, rest.substr(1));
}
}
void ListSubsets(string str)
{
RecSubsets("",str);
}
```
代码非常容易理解
比较一下：两个都是有关选择，permutation是每次选哪一个char，而subsets是选择这个char 是否in.
两个recursion tree都是有branching 和 depth的， depth都是n，每次选一个，知道n个选完.
branching是how many recusive calls 每次made，subset每次都是两个，in/out，而permutation则是n，n-1.......grows very quickly.
因为permutation是n！，subsets是2^n，跟树对应。这些都是比较intractable的问题，并不是因为recursion，而是问题本身的复杂度。
这两个问题都是exhaustive的，然而，我们会更多碰到一些问题，有着
similar exhaustive structure，但是遇到'satisfactory' outcome就会stop的 -> 也就是backtracking了.
##BackTracking
### pseudocode
把问题转成decision problem，然后开始make choice.
```
bool Solve(configuration conf)
{
if (no more choices) // BASE CASE
return (conf is goal state);
for (all available choices){
try one choice c;
// sove from here, it works out. you're done.
if (Solve(conf with choice c made)) return true;
unmake choice c;
}
return false; //tried all choices, no soln found
}
```
###IsAnagram
```
bool IsAnagram(string soFar, string rest, Lexicon & lex)
{
if(rest == ""){
if(lex.contains(soFar)){
cout << soFar << endl;
return true;
}
} else {
for(int i = 0; i < rest.length() ; i++ ){
string next = soFar + rest[i];
string remaining = rest.substr(0,i) + rest.substr(i+1);
if(IsAnagram(next, remaining, lex)) return true;
}
}
return false;
}
```
### 8 Queens
```
bool Solve(Grid &board, int col)
{
if(col > = board.numCols()) return true;
for(int rowToTry = 0; rowToTry < board.numRows(); rowToTry++){
if (IsSafe(board,rowToTry, col)){
PlaceQueen(board,rowToTry,col);
if (Solve(board,col+1)) return true;
RemoveQueen(board,rowToTry, col);
}
}
return false;
}
```
================================================
FILE: docs/Algorithm/DataStructure/Summarization/backtracking思路.md
================================================
## backtracking 全集
### 回溯是啥
用爬山来比喻回溯，好比从山脚下找一条爬上山顶的路,起初有好几条道可走,当选择一条道走到某处时,又有几条岔道可供选择,只能选择其中一条道往前走,若能这样子顺利爬上山顶则罢了,否则走到一条绝路上时,只好返回到最近的一个路口,重新选择另一条没走过的道往前走。如果该路口的所有路都走不通,只得从该路口继续回返。照此规则走下去,要么找到一条到达山顶的路,要么最终试过所有可能的道,无法到达山顶。
回溯是一种穷举，但与brute force有一些区别，回溯带了两点脑子的，并不多，brute force一点也没带。
第一点脑子是回溯知道回头；相反如果是brute force,发现走不通立刻跳下山摔死，换第二条命从头换一条路走。
第二点脑子是回溯知道剪枝；如果有一条岔路上放了一坨屎，那这条路我们不走，就可以少走很多不必要走的路。
还有一些爱混淆的概念：递归，回溯，DFS。
回溯是一种找路方法，搜索的时候走不通就回头换路接着走，直到走通了或者发现此山根本不通。
DFS是一种开路策略，就是一条道先走到头，再往回走一步换一条路走到头，这也是回溯用到的策略。在树和图上回溯时人们叫它DFS。
递归是一种行为，回溯和递归如出一辙，都是一言不合就回到来时的路，所以一般回溯用递归实现；当然也可以不用，用栈。
以下以回溯统称，因为这个词听上去很文雅。
### 识别回溯
判断回溯很简单，拿到一个问题，你感觉如果不穷举一下就没法知道答案，那就可以开始回溯了。
一般回溯的问题有三种：
1. Find a path to success 有没有解
2. Find all paths to success 求所有解
- 2.1 求所有解的个数，
- 2.2 求所有解的具体信息
3. Find the best path to success 求最优解
理解回溯：给一堆选择, 必须从里面选一个. 选完之后我又有了新的一组选择. ```This procedure is repeated over and over until you reach a final state. If you made a good sequence of choices, your final state is a goal state; if you didn't, it isn't.```
回溯可以抽象为一棵树，我们的目标可以是找这个树有没有good leaf，也可以是问有多少个good leaf，也可以是找这些good leaf都在哪，也可以问哪个good leaf最好，分别对应上面所说回溯的问题分类。
good leaf都在leaf上。good leaf是我们的goal state，leaf node是final state，是解空间的边界。
对于第一类问题(问有没有解)，基本都是长着个样子的，理解了它，其他类别迎刃而解：
```java
boolean solve(Node n) {
if n is a leaf node {
if the leaf is a goal node, return true
else return false
} else {
for each child c of n {
if solve(c) succeeds, return true
}
return false
}
}
```
请读以下这段话以加深理解：
```Notice that the algorithm is expressed as a boolean function. This is essential to understanding the algorithm. If solve(n) is true, that means node n is part of a solution--that is, node n is one of the nodes on a path from the root to some goal node. We say that n is solvable. If solve(n) is false, then there is no path that includes n to any goal node.```
还不懂的话请通读全文吧：[Backtracking - David Matuszek](https://www.cis.upenn.edu/~matuszek/cit594-2012/Pages/backtracking.html)
关于回溯的三种问题，模板略有不同，
第一种，返回值是true/false。
第二种，求个数，设全局counter，返回值是void；求所有解信息，设result，返回值void。
第三种，设个全局变量best，返回值是void。
第一种：
```java
boolean solve(Node n) {
if n is a leaf node {
if the leaf is a goal node, return true
else return false
} else {
for each child c of n {
if solve(c) succeeds, return true
}
return false
}
}
```
第二种：
```java
void solve(Node n) {
if n is a leaf node {
if the leaf is a goal node, count++, return;
else return
} else {
for each child c of n {
solve(c)
}
}
}
```
第三种：
```java
void solve(Node n) {
if n is a leaf node {
if the leaf is a goal node, update best result, return;
else return
} else {
for each child c of n {
solve(c)
}
}
}
```
题目
八皇后 N-Queens
问题
1. 给个n，问有没有解；
2. 给个n，有几种解；(Leetcode N-Queens II)
3. 给个n，给出所有解；(Leetcode N-Queens I)
解答
1.有没有解
怎么做：一行一行的放queen，每行尝试n个可能，有一个可达，返回true；都不可达，返回false.
边界条件leaf:放完第n行 或者 该放第n+1行(出界，返回)
目标条件goal:n行放满且isValid，即目标一定在leaf上
helper函数：
boolean solve(int i, int[][] matrix)
在进来的一瞬间，满足property：第i行还没有被放置，前i-1行放置完毕且valid
solve要在给定的matrix上试图给第i行每个位置放queen。
```java
public static boolean solve1(int i, List matrix, int n) {
if (i == n) {
if (isValid(matrix))
return true;
return false;
} else {
for (int j = 0; j < n; j++) {
matrix.add(j);
if (isValid(matrix)) { //剪枝
if (solve1(i + 1, matrix, n))
return true;
}
matrix.remove(matrix.size() - 1);
}
return false;
}
}
```
2.求解的个数
怎么做：一行一行的放queen，每行尝试n个可能。这回因为要找所有，返回值就没有了意义，用void即可。在搜索时，如果有一个可达，仍要继续尝试；每个子选项都试完了，返回.
边界条件leaf:放完第n行 或者 该放第n+1行(出界，返回)
目标条件goal:n行放满且isValid，即目标一定在leaf上
helper函数：
void solve(int i, int[][] matrix)
在进来的一瞬间，满足property：第i行还没有被放置，前i-1行放置完毕且valid
solve要在给定的matrix上试图给第i行每个位置放queen。
这里为了记录解的个数，设置一个全局变量(static)int是比较efficient的做法。
```java
public static void solve2(int i, List matrix, int n) {
if (i == n) {
if (isValid(matrix))
count++;
return;
} else {
for (int j = 0; j < n; j++) {
matrix.add(j);
if (isValid(matrix)) { //剪枝
solve2(i + 1, matrix, n);
}
matrix.remove(matrix.size() - 1);
}
}
}
```
3.求所有解的具体信息
怎么做：一行一行的放queen，每行尝试n个可能。返回值同样用void即可。在搜索时，如果有一个可达，仍要继续尝试；每个子选项都试完了，返回.
边界条件leaf:放完第n行 或者 该放第n+1行(出界，返回)
目标条件goal:n行放满且isValid，即目标一定在leaf上
helper函数：
void solve(int i, int[][] matrix)
在进来的一瞬间，满足property：第i行还没有被放置，前i-1行放置完毕且valid
solve要在给定的matrix上试图给第i行每个位置放queen。
这里为了记录解的具体情况，设置一个全局变量(static)集合是比较efficient的做法。
当然也可以把结果集合作为参数传来传去。
```java
public static void solve3(int i, List matrix, int n) {
if (i == n) {
if (isValid(matrix))
result.add(new ArrayList(matrix));
return;
} else {
for (int j = 0; j < n; j++) {
matrix.add(j);
if (isValid(matrix)) { //剪枝
solve3(i + 1, matrix, n);
}
matrix.remove(matrix.size() - 1);
}
}
}
```
优化
上面的例子用了省空间的方法。
由于每行只能放一个，一共n行的话，用一个大小为n的数组，数组的第i个元素表示第i行放在了第几列上。
Utility(给一个list判断他的最后一行是否和前面冲突):
```java
public static boolean isValid(List list){
int row = list.size() - 1;
int col = list.get(row);
for (int i = 0; i <= row - 1; i++) {
int row1 = i;
int col1 = list.get(i);
if (col == col1)
return false;
if (row1 - row == col1 - col)
return false;
if (row1 - row == col - col1)
return false;
}
return true;
}
```
参考[Backtracking回溯法(又称DFS,递归)全解](https://segmentfault.com/a/1190000006121957)
以及 [Python Patterns - Implementing Graphs](https://www.python.org/doc/essays/graphs/)
## 以Generate Parentheses为例，backtrack的题到底该怎么去思考？
所谓Backtracking都是这样的思路：在当前局面下，你有若干种选择。那么尝试每一种选择。如果已经发现某种选择肯定不行（因为违反了某些限定条件），就返回；如果某种选择试到最后发现是正确解，就将其加入解集
所以你思考递归题时，只要明确三点就行：选择 (Options)，限制 (Restraints)，结束条件 (Termination)。即“ORT原则”（这个是我自己编的）
对于这道题，在任何时刻，你都有两种选择：
1. 加左括号。
2. 加右括号。
同时有以下限制：
1. 如果左括号已经用完了，则不能再加左括号了。
2. 如果已经出现的右括号和左括号一样多，则不能再加右括号了。因为那样的话新加入的右括号一定无法匹配。
结束条件是：
左右括号都已经用完。
结束后的正确性：
左右括号用完以后，一定是正确解。因为1. 左右括号一样多，2. 每个右括号都一定有与之配对的左括号。因此一旦结束就可以加入解集（有时也可能出现结束以后不一定是正确解的情况，这时要多一步判断）。
递归函数传入参数：
限制和结束条件中有“用完”和“一样多”字样，因此你需要知道左右括号的数目。
当然你还需要知道当前局面sublist和解集res。
因此，把上面的思路拼起来就是代码：
if (左右括号都已用完) {
加入解集，返回
}
//否则开始试各种选择
if (还有左括号可以用) {
加一个左括号，继续递归
}
if (右括号小于左括号) {
加一个右括号，继续递归
}
你帖的那段代码逻辑中加了一条限制：“3. 是否还有右括号剩余。如有才加右括号”。这是合理的。不过对于这道题，如果满足限制1、2时，3一定自动满足，所以可以不判断3。
这题其实是最好的backtracking初学练习之一，因为ORT三者都非常简单明显。你不妨按上述思路再梳理一遍，还有问题的话再说。
以上文字来自 1point3arces的牛人解答
Backtracking 伪码
```
Pick a starting point.
while(Problem is not solved)
For each path from the starting point.
check if selected path is safe, if yes select it
and make recursive call to rest of the problem
If recursive calls returns true, then return true.
else undo the current move and return false.
End For
If none of the move works out, return false, NO SOLUTON.
```
================================================
FILE: docs/Algorithm/DataStructure/Summarization/delete_node_in_a_linked_list问题.md
================================================
##Delete Node in a Linked List问题
This is a LeetCode question, I knew its solution, but wondering about why my code not work.
>Write a function to delete a node (except the tail) in a singly linked list, given only access to that node.
>Supposed the linked list is 1 -> 2 -> 3 -> 4 and you are given the third node with value 3, the linked list should become 1 -> 2 -> 4 after calling your function
At first glance, my intution is delete like an array:
shift all the node values one front, then delete the tail, here's my implementation and test case:
class ListNode(object):
def __init__(self, x):
self.val = x
self.next = None
node1 = ListNode(1)
node2 = ListNode(2)
node3 = ListNode(3)
node4 = ListNode(4)
node5 = ListNode(5)
node1.next = node2
node2.next = node3
node3.next = node4
node4.next = node5
def deleteNode(node):
"""
:type node: ListNode
:rtype: void Do not return anything, modify node in-place instead.
"""
while node.next:
node.val = node.next.val
node = node.next
node = None
deleteNode(node4)
But After deletion, it has two 5 value nodes, the tail was still kept, can anyone please explain to me what's wrong here?
deleteNode(node4)
node1.val
Out[162]: 1
node1.next.val
Out[163]: 2
node1.next.next.val
Out[164]: 3
node1.next.next.next.val
Out[165]: 5
node1.next.next.next.next.val
Out[166]: 5
Really appreciate any help.
================================================
FILE: docs/Algorithm/DataStructure/Summarization/python_base.py
================================================
# _*_ coding: utf-8 _*_
"""类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算----类型和运算"""
#-- 寻求帮助:
dir(obj) # 简单的列出对象obj所包含的方法名称，返回一个字符串列表
help(obj.func) # 查询obj.func的具体介绍和用法
#-- 测试类型的三种方法，推荐第三种
if type(L) == type([]):
print("L is list")
if type(L) == list:
print("L is list")
if isinstance(L, list):
print("L is list")
#-- Python数据类型：哈希类型、不可哈希类型
# 哈希类型，即在原地不能改变的变量类型，不可变类型。可利用hash函数查看其hash值，也可以作为字典的key
"数字类型：int, float, decimal.Decimal, fractions.Fraction, complex"
"字符串类型：str, bytes"
"元组：tuple"
"冻结集合：frozenset"
"布尔类型：True, False"
"None"
# 不可hash类型：原地可变类型：list、dict和set。它们不可以作为字典的key。
#-- 数字常量
1234, -1234, 0, 999999999 # 整数
1.23, 1., 3.14e-10, 4E210, 4.0e+210 # 浮点数
0o177, 0x9ff, 0X9FF, 0b101010 # 八进制、十六进制、二进制数字
3+4j, 3.0+4.0j, 3J # 复数常量，也可以用complex(real, image)来创建
hex(I), oct(I), bin(I) # 将十进制数转化为十六进制、八进制、二进制表示的“字符串”
int(string, base) # 将字符串转化为整数，base为进制数
# 2.x中，有两种整数类型：一般整数（32位）和长整数（无穷精度）。可以用l或L结尾，迫使一般整数成为长整数
float('inf'), float('-inf'), float('nan') # 无穷大, 无穷小, 非数
#-- 数字的表达式操作符
yield x # 生成器函数发送协议
lambda args: expression # 生成匿名函数
x if y else z # 三元选择表达式
x and y, x or y, not x # 逻辑与、逻辑或、逻辑非
x in y, x not in y # 成员对象测试
x is y, x is not y # 对象实体测试
xy, x>=y, x==y, x!=y # 大小比较，集合子集或超集值相等性操作符
1 < a < 3 # Python中允许连续比较
x|y, x&y, x^y # 位或、位与、位异或
x<>y # 位操作：x左移、右移y位
+, -, *, /, //, %, ** # 真除法、floor除法：返回不大于真除法结果的整数值、取余、幂运算
-x, +x, ~x # 一元减法、识别、按位求补（取反）
x[i], x[i:j:k] # 索引、分片、调用
int(3.14), float(3) # 强制类型转换
#-- 整数可以利用bit_length函数测试所占的位数
a = 1; a.bit_length() # 1
a = 1024; a.bit_length() # 11
#-- repr和str显示格式的区别
"""
repr格式：默认的交互模式回显，产生的结果看起来它们就像是代码。
str格式：打印语句，转化成一种对用户更加友好的格式。
"""
#-- 数字相关的模块
# math模块
# Decimal模块：小数模块
import decimal
from decimal import Decimal
Decimal("0.01") + Decimal("0.02") # 返回Decimal("0.03")
decimal.getcontext().prec = 4 # 设置全局精度为4 即小数点后边4位
# Fraction模块：分数模块
from fractions import Fraction
x = Fraction(4, 6) # 分数类型 4/6
x = Fraction("0.25") # 分数类型 1/4 接收字符串类型的参数
#-- 集合set
"""
set是一个无序不重复元素集, 基本功能包括关系测试和消除重复元素。
set支持union(联合), intersection(交), difference(差)和symmetric difference(对称差集)等数学运算。
set支持x in set, len(set), for x in set。
set不记录元素位置或者插入点, 因此不支持indexing, slicing, 或其它类序列的操作
"""
s = set([3,5,9,10]) # 创建一个数值集合，返回{3, 5, 9, 10}
t = set("Hello") # 创建一个唯一字符的集合返回{}
a = t | s; t.union(s) # t 和 s的并集
b = t & s; t.intersection(s) # t 和 s的交集
c = t – s; t.difference(s) # 求差集（项在t中, 但不在s中）
d = t ^ s; t.symmetric_difference(s) # 对称差集（项在t或s中, 但不会同时出现在二者中）
t.add('x'); t.remove('H') # 增加/删除一个item
s.update([10,37,42]) # 利用[......]更新s集合
x in s, x not in s # 集合中是否存在某个值
s.issubset(t); s <= t # 测试是否 s 中的每一个元素都在 t 中
s.issuperset(t); s >= t # 测试是否 t 中的每一个元素都在 s 中
s.copy();
s.discard(x); # 删除s中x
s.clear() # 清空s
{x**2 for x in [1, 2, 3, 4]} # 集合解析，结果：{16, 1, 4, 9}
{x for x in 'spam'} # 集合解析，结果：{'a', 'p', 's', 'm'}
#-- 集合frozenset，不可变对象
"""
set是可变对象，即不存在hash值，不能作为字典的键值。同样的还有list等(tuple是可以作为字典key的)
frozenset是不可变对象，即存在hash值，可作为字典的键值
frozenset对象没有add、remove等方法，但有union/intersection/difference等方法
"""
a = set([1, 2, 3])
b = set()
b.add(a) # error: set是不可哈希类型
b.add(frozenset(a)) # ok，将set变为frozenset，可哈希
#-- 布尔类型bool
type(True) # 返回
isinstance(False, int) # bool类型属于整型，所以返回True
True == 1; True is 1 # 输出(True, False)
#-- 动态类型简介
"""
变量名通过引用，指向对象。
Python中的“类型”属于对象，而不是变量，每个对象都包含有头部信息，比如"类型标示符" "引用计数器"等
"""
#共享引用及在原处修改：对于可变对象，要注意尽量不要共享引用！
#共享引用和相等测试：
L = [1], M = [1], L is M # 返回False
L = M = [1, 2, 3], L is M # 返回True，共享引用
#增强赋值和共享引用：普通+号会生成新的对象，而增强赋值+=会在原处修改
L = M = [1, 2]
L = L + [3, 4] # L = [1, 2, 3, 4], M = [1, 2]
L += [3, 4] # L = [1, 2, 3, 4], M = [1, 2, 3, 4]
#-- 常见字符串常量和表达式
S = '' # 空字符串
S = "spam’s" # 双引号和单引号相同
S = "s\np\ta\x00m" # 转义字符
S = """spam""" # 三重引号字符串，一般用于函数说明
S = r'\temp' # Raw字符串，不会进行转义，抑制转义
S = b'Spam' # Python3中的字节字符串
S = u'spam' # Python2.6中的Unicode字符串
s1+s2, s1*3, s[i], s[i:j], len(s) # 字符串操作
'a %s parrot' % 'kind' # 字符串格式化表达式
'a {1} {0} parrot'.format('kind', 'red')# 字符串格式化方法
for x in s: print(x) # 字符串迭代，成员关系
[x*2 for x in s] # 字符串列表解析
','.join(['a', 'b', 'c']) # 字符串输出，结果：a,b,c
#-- 内置str处理函数：
str1 = "stringobject"
str1.upper(); str1.lower(); str1.swapcase(); str1.capitalize(); str1.title() # 全部大写，全部小写、大小写转换，首字母大写，每个单词的首字母都大写
str1.ljust(width) # 获取固定长度，左对齐，右边不够用空格补齐
str1.rjust(width) # 获取固定长度，右对齐，左边不够用空格补齐
str1.center(width) # 获取固定长度，中间对齐，两边不够用空格补齐
str1.zfill(width) # 获取固定长度，右对齐，左边不足用0补齐
str1.find('t',start,end) # 查找字符串，可以指定起始及结束位置搜索
str1.rfind('t') # 从右边开始查找字符串
str1.count('t') # 查找字符串出现的次数
#上面所有方法都可用index代替，不同的是使用index查找不到会抛异常，而find返回-1
str1.replace('old','new') # 替换函数，替换old为new，参数中可以指定maxReplaceTimes，即替换指定次数的old为new
str1.strip(); # 默认删除空白符
str1.strip('d'); # 删除str1字符串中开头、结尾处，位于 d 删除序列的字符
str1.lstrip();
str1.lstrip('d'); # 删除str1字符串中开头处，位于 d 删除序列的字符
str1.rstrip();
str1.rstrip('d') # 删除str1字符串中结尾处，位于 d 删除序列的字符
str1.startswith('start') # 是否以start开头
str1.endswith('end') # 是否以end结尾
str1.isalnum(); str1.isalpha(); str1.isdigit(); str1.islower(); str1.isupper() # 判断字符串是否全为字符、数字、小写、大写
#-- 三重引号编写多行字符串块，并且在代码折行处嵌入换行字符\n
mantra = """hello world
hello python
hello my friend"""
# mantra为"""hello world \n hello python \n hello my friend"""
#-- 索引和分片：
S[0], S[len(S)–1], S[-1] # 索引
S[1:3], S[1:], S[:-1], S[1:10:2] # 分片，第三个参数指定步长，如`S[1:10:2]`是从1位到10位没隔2位获取一个字符。
#-- 字符串转换工具：
int('42'), str(42) # 返回(42, '42')
float('4.13'), str(4.13) # 返回(4.13, '4.13')
ord('s'), chr(115) # 返回(115, 's')
int('1001', 2) # 将字符串作为二进制数字，转化为数字，返回9
bin(13), oct(13), hex(13) # 将整数转化为二进制/八进制/十六进制字符串，返回('0b1101', '015', '0xd')
#-- 另类字符串连接
name = "wang" "hong" # 单行，name = "wanghong"
name = "wang" \
"hong" # 多行，name = "wanghong"
#-- Python中的字符串格式化实现1--字符串格式化表达式
"""
基于C语言的'print'模型，并且在大多数的现有的语言中使用。
通用结构：%[(name)][flag][width].[precision]typecode
"""
"this is %d %s bird" % (1, 'dead') # 一般的格式化表达式
"%s---%s---%s" % (42, 3.14, [1, 2, 3]) # 字符串输出：'42---3.14---[1, 2, 3]'
"%d...%6d...%-6d...%06d" % (1234, 1234, 1234, 1234) # 对齐方式及填充："1234... 1234...1234 ...001234"
x = 1.23456789
"%e | %f | %g" % (x, x, x) # 对齐方式："1.234568e+00 | 1.234568 | 1.23457"
"%6.2f*%-6.2f*%06.2f*%+6.2f" % (x, x, x, x) # 对齐方式：' 1.23*1.23 *001.23* +1.23'
"%(name1)d---%(name2)s" % {"name1":23, "name2":"value2"} # 基于字典的格式化表达式
"%(name)s is %(age)d" % vars() # vars()函数调用返回一个字典，包含了所有本函数调用时存在的变量
#-- Python中的字符串格式化实现2--字符串格式化调用方法
# 普通调用
"{0}, {1} and {2}".format('spam', 'ham', 'eggs') # 基于位置的调用
"{motto} and {pork}".format(motto = 'spam', pork = 'ham') # 基于Key的调用
"{motto} and {0}".format('ham', motto = 'spam') # 混合调用
# 添加键 属性 偏移量 (import sys)
"my {1[spam]} runs {0.platform}".format(sys, {'spam':'laptop'}) # 基于位置的键和属性
"{config[spam]} {sys.platform}".format(sys = sys, config = {'spam':'laptop'}) # 基于Key的键和属性
"first = {0[0]}, second = {0[1]}".format(['A', 'B', 'C']) # 基于位置的偏移量
# 具体格式化
"{0:e}, {1:.3e}, {2:g}".format(3.14159, 3.14159, 3.14159) # 输出'3.141590e+00, 3.142e+00, 3.14159'
"{fieldname:format_spec}".format(......)
# 说明:
"""
fieldname是指定参数的一个数字或关键字, 后边可跟可选的".name"或"[index]"成分引用
format_spec ::= [[fill]align][sign][#][0][width][,][.precision][type]
fill ::= #填充字符
align ::= "<" | ">" | "=" | "^" #对齐方式
sign ::= "+" | "-" | " " #符号说明
width ::= integer #字符串宽度
precision ::= integer #浮点数精度
type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
"""
# 例子:
'={0:10} = {1:10}'.format('spam', 123.456) # 输出'=spam = 123.456'
'={0:>10}='.format('test') # 输出'= test='
'={0:<10}='.format('test') # 输出'=test ='
'={0:^10}='.format('test') # 输出'= test ='
'{0:X}, {1:o}, {2:b}'.format(255, 255, 255) # 输出'FF, 377, 11111111'
'My name is {0:{1}}.'.format('Fred', 8) # 输出'My name is Fred .' 动态指定参数
#-- 常用列表常量和操作
L = [[1, 2], 'string', {}] # 嵌套列表
L = list('spam') # 列表初始化
L = list(range(0, 4)) # 列表初始化
list(map(ord, 'spam')) # 列表解析
len(L) # 求列表长度
L.count(value) # 求列表中某个值的个数
L.append(obj) # 向列表的尾部添加数据，比如append(2)，添加元素2
L.insert(index, obj) # 向列表的指定index位置添加数据，index及其之后的数据后移
L.extend(interable) # 通过添加iterable中的元素来扩展列表，比如extend([2])，添加元素2，注意和append的区别
L.index(value, [start, [stop]]) # 返回列表中值value的第一个索引
L.pop([index]) # 删除并返回index处的元素，默认为删除并返回最后一个元素
L.remove(value) # 删除列表中的value值，只删除第一次出现的value的值
L.reverse() # 反转列表
L.sort(cmp=None, key=None, reverse=False) # 排序列表
a = [1, 2, 3], b = a[10:] # 注意，这里不会引发IndexError异常，只会返回一个空的列表[]
a = [], a += [1] # 这里实在原有列表的基础上进行操作，即列表的id没有改变
a = [], a = a + [1] # 这里最后的a要构建一个新的列表，即a的id发生了变化
#-- 用切片来删除序列的某一段
a = [1, 2, 3, 4, 5, 6, 7]
a[1:4] = [] # a = [1, 5, 6, 7]
a = [0, 1, 2, 3, 4, 5, 6, 7]
del a[::2] # 去除偶数项(偶数索引的)，a = [1, 3, 5, 7]
#-- 常用字典常量和操作
D = {}
D = {'spam':2, 'tol':{'ham':1}} # 嵌套字典
D = dict.fromkeys(['s', 'd'], 8) # {'s': 8, 'd': 8}
D = dict(name = 'tom', age = 12) # {'age': 12, 'name': 'tom'}
D = dict([('name', 'tom'), ('age', 12)]) # {'age': 12, 'name': 'tom'}
D = dict(zip(['name', 'age'], ['tom', 12])) # {'age': 12, 'name': 'tom'}
D.keys(); D.values(); D.items() # 字典键、值以及键值对
D.get(key, default) # get函数
D.update(D_other) # 合并字典，如果存在相同的键值，D_other的数据会覆盖掉D的数据
D.pop(key, [D]) # 删除字典中键值为key的项，返回键值为key的值，如果不存在，返回默认值D，否则异常
D.popitem() # pop字典中随机的一项（一个键值对）
D.setdefault(k[, d]) # 设置D中某一项的默认值。如果k存在，则返回D[k]，否则设置D[k]=d，同时返回D[k]。
del D # 删除字典
del D['key'] # 删除字典的某一项
if key in D: if key not in D: # 测试字典键是否存在
# 字典注意事项：（1）对新索引赋值会添加一项（2）字典键不一定非得是字符串，也可以为任何的不可变对象
# 不可变对象：调用对象自身的任意方法，也不会改变该对象自身的内容，这些方法会创建新的对象并返回。
# 字符串、整数、tuple都是不可变对象，dict、set、list都是可变对象
D[(1,2,3)] = 2 # tuple作为字典的key
#-- 字典解析
D = {k:8 for k in ['s', 'd']} # {'s': 8, 'd': 8}
D = {k:v for (k, v) in zip(['name', 'age'], ['tom', 12])} # {'age': 12, 'name': tom}
#-- 字典的特殊方法__missing__：当查找找不到key时，会执行该方法
class Dict(dict):
def __missing__(self, key):
self[key] = []
return self[key]
dct = dict()
dct["foo"].append(1) # 这有点类似于collections.defalutdict
dct["foo"] # [1]
#-- 元组和列表的唯一区别在于元组是不可变对象，列表是可变对象
a = [1, 2, 3] # a[1] = 0, OK
a = (1, 2, 3) # a[1] = 0, Error
a = ([1, 2]) # a[0][1] = 0, OK
a = [(1, 2)] # a[0][1] = 0, Error
#-- 元组的特殊语法: 逗号和圆括号
D = (12) # 此时D为一个整数 即D = 12
D = (12, ) # 此时D为一个元组 即D = (12, )
#-- 文件基本操作
output = open(r'C:\spam', 'w') # 打开输出文件，用于写
input = open('data', 'r') # 打开输入文件，用于读。打开的方式可以为'w', 'r', 'a', 'wb', 'rb', 'ab'等
fp.read([size]) # size为读取的长度，以byte为单位
fp.readline([size]) # 读一行，如果定义了size，有可能返回的只是一行的一部分
fp.readlines([size]) # 把文件每一行作为一个list的一个成员，并返回这个list。其实它的内部是通过循环调用readline()来实现的。如果提供size参数，size是表示读取内容的总长。
fp.readable() # 是否可读
fp.write(str) # 把str写到文件中，write()并不会在str后加上一个换行符
fp.writelines(seq) # 把seq的内容全部写到文件中(多行一次性写入)
fp.writeable() # 是否可写
fp.close() # 关闭文件。
fp.flush() # 把缓冲区的内容写入硬盘
fp.fileno() # 返回一个长整型的”文件标签“
fp.isatty() # 文件是否是一个终端设备文件（unix系统中的）
fp.tell() # 返回文件操作标记的当前位置，以文件的开头为原点
fp.next() # 返回下一行，并将文件操作标记位移到下一行。把一个file用于for … in file这样的语句时，就是调用next()函数来实现遍历的。
fp.seek(offset[,whence]) # 将文件打开操作标记移到offset的位置。whence为0表示从头开始计算，1表示以当前位置为原点计算。2表示以文件末尾为原点进行计算。
fp.seekable() # 是否可以seek
fp.truncate([size]) # 把文件裁成规定的大小，默认的是裁到当前文件操作标记的位置。
for line in open('data'):
print(line) # 使用for语句，比较适用于打开比较大的文件
with open('data') as file:
print(file.readline()) # 使用with语句，可以保证文件关闭
with open('data') as file:
lines = file.readlines() # 一次读入文件所有行，并关闭文件
open('f.txt', encoding = 'latin-1') # Python3.x Unicode文本文件
open('f.bin', 'rb') # Python3.x 二进制bytes文件
# 文件对象还有相应的属性：buffer closed encoding errors line_buffering name newlines等
#-- 其他
# Python中的真假值含义：1. 数字如果非零，则为真，0为假。 2. 其他对象如果非空，则为真
# 通常意义下的类型分类：1. 数字、序列、映射。 2. 可变类型和不可变类型
"""语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句----语法和语句"""
#-- 赋值语句的形式
spam = 'spam' # 基本形式
spam, ham = 'spam', 'ham' # 元组赋值形式
[spam, ham] = ['s', 'h'] # 列表赋值形式
a, b, c, d = 'abcd' # 序列赋值形式
a, *b, c = 'spam' # 序列解包形式（Python3.x中才有）
spam = ham = 'no' # 多目标赋值运算，涉及到共享引用
spam += 42 # 增强赋值，涉及到共享引用
#-- 序列赋值 序列解包
[a, b, c] = (1, 2, 3) # a = 1, b = 2, c = 3
a, b, c, d = "spam" # a = 's', b = 'p', c = 'a', d = 'm'
a, b, c = range(3) # a = 0, b = 1, c = 2
a, *b = [1, 2, 3, 4] # a = 1, b = [2, 3, 4]
*a, b = [1, 2, 3, 4] # a = [1, 2, 3], b = 4
a, *b, c = [1, 2, 3, 4] # a = 1, b = [2, 3], c = 4
# 带有*时 会优先匹配*之外的变量 如
a, *b, c = [1, 2] # a = 1, c = 2, b = []
#-- print函数原型
print(value, ..., sep=' ', end='\n', file=sys.stdout, flush=False)
# 流的重定向
print('hello world') # 等于sys.stdout.write('hello world')
temp = sys.stdout # 原有流的保存
sys.stdout = open('log.log', 'a') # 流的重定向
print('hello world') # 写入到文件log.log
sys.stdout.close()
sys.stdout = temp # 原有流的复原
#-- Python中and或or总是返回对象(左边的对象或右边的对象) 且具有短路求值的特性
1 or 2 or 3 # 返回 1
1 and 2 and 3 # 返回 3
#-- if/else三元表达符（if语句在行内）
A = 1 if X else 2
A = 1 if X else (2 if Y else 3)
# 也可以使用and-or语句（一条语句实现多个if-else）
a = 6
result = (a > 20 and "big than 20" or a > 10 and "big than 10" or a > 5 and "big than 5") # 返回"big than 5"
#-- Python的while语句或者for语句可以带else语句 当然也可以带continue/break/pass语句
while a > 1:
anything
else:
anything
# else语句会在循环结束后执行，除非在循环中执行了break，同样的还有for语句
for i in range(5):
anything
else:
anything
#-- for循环的元组赋值
for (a, b) in [(1, 2), (3, 4)]: # 最简单的赋值
for ((a, b), c) in [((1, 2), 3), ((4, 5), 6)]: # 自动解包赋值
for ((a, b), c) in [((1, 2), 3), ("XY", 6)]: # 自动解包 a = X, b = Y, c = 6
for (a, *b) in [(1, 2, 3), (4, 5, 6)]: # 自动解包赋值
#-- 列表解析语法
M = [[1,2,3], [4,5,6], [7,8,9]]
res = [sum(row) for row in M] # G = [6, 15, 24] 一般的列表解析 生成一个列表
res = [c * 2 for c in 'spam'] # ['ss', 'pp', 'aa', 'mm']
res = [a * b for a in [1, 2] for b in [4, 5]] # 多解析过程 返回[4, 5, 8, 10]
res = [a for a in [1, 2, 3] if a < 2] # 带判断条件的解析过程
res = [a if a > 0 else 0 for a in [-1, 0, 1]] # 带判断条件的高级解析过程
# 两个列表同时解析：使用zip函数
for teama, teamb in zip(["Packers", "49ers"], ["Ravens", "Patriots"]):
print(teama + " vs. " + teamb)
# 带索引的列表解析：使用enumerate函数
for index, team in enumerate(["Packers", "49ers", "Ravens", "Patriots"]):
print(index, team) # 输出0, Packers \n 1, 49ers \n ......
#-- 生成器表达式
G = (sum(row) for row in M) # 使用小括号可以创建所需结果的生成器generator object
next(G), next(G), next(G) # 输出(6, 15, 24)
G = {sum(row) for row in M} # G = {6, 15, 24} 解析语法还可以生成集合和字典
G = {i:sum(M[i]) for i in range(3)} # G = {0: 6, 1: 15, 2: 24}
#-- 文档字符串:出现在Module的开端以及其中函数或类的开端 使用三重引号字符串
"""
module document
"""
def func():
"""
function document
"""
print()
class Employee(object):
"""
class document
"""
print()
print(func.__doc__) # 输出函数文档字符串
print(Employee.__doc__) # 输出类的文档字符串
#-- 命名惯例:
"""
以单一下划线开头的变量名(_X)不会被from module import*等语句导入
前后有两个下划线的变量名(__X__)是系统定义的变量名，对解释器有特殊意义
以两个下划线开头但不以下划线结尾的变量名(__X)是类的本地(私有)变量
"""
#-- 列表解析 in成员关系测试 map sorted zip enumerate内置函数等都使用了迭代协议
'first line' in open('test.txt') # in测试 返回True或False
list(map(str.upper, open('t'))) # map内置函数
sorted(iter([2, 5, 8, 3, 1])) # sorted内置函数
list(zip([1, 2], [3, 4])) # zip内置函数 [(1, 3), (2, 4)]
#-- del语句: 手动删除某个变量
del X
#-- 获取列表的子表的方法:
x = [1,2,3,4,5,6]
x[:3] # 前3个[1,2,3]
x[1:5] # 中间4个[2,3,4,5]
x[-3:] # 最后3个[4,5,6]
x[::2] # 奇数项[1,3,5]
x[1::2] # 偶数项[2,4,6]
#-- 手动迭代：iter和next
L = [1, 2]
I = iter(L) # I为L的迭代器
I.next() # 返回1
I.next() # 返回2
I.next() # Error:StopIteration
#-- Python中的可迭代对象
"""
1.range迭代器
2.map、zip和filter迭代器
3.字典视图迭代器：D.keys()), D.items()等
4.文件类型
"""
"""函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则----函数语法规则"""
#-- 函数相关的语句和表达式
myfunc('spam') # 函数调用
def myfunc(): # 函数定义
return None # 函数返回值
global a # 全局变量
nonlocal x # 在函数或其他作用域中使用外层（非全局）变量
yield x # 生成器函数返回
lambda # 匿名函数
#-- Python函数变量名解析:LEGB原则，即:
"""
local(functin) --> encloseing function locals --> global(module) --> build-in(python)
说明:以下边的函数maker为例 则相对于action而言 X为Local N为Encloseing
"""
#-- 嵌套函数举例:工厂函数
def maker(N):
def action(X):
return X ** N
return action
f = maker(2) # pass 2 to N
f(3) # 9, pass 3 to X
#-- 嵌套函数举例:lambda实例
def maker(N):
action = (lambda X: X**N)
return action
f = maker(2) # pass 2 to N
f(3) # 9, pass 3 to X
#-- nonlocal和global语句的区别
# nonlocal应用于一个嵌套的函数的作用域中的一个名称 例如:
start = 100
def tester(start):
def nested(label):
nonlocal start # 指定start为tester函数内的local变量 而不是global变量start
print(label, start)
start += 3
return nested
# global为全局的变量 即def之外的变量
def tester(start):
def nested(label):
global start # 指定start为global变量start
print(label, start)
start += 3
return nested
#-- 函数参数，不可变参数通过“值”传递，可变参数通过“引用”传递
def f(a, b, c): print(a, b, c)
f(1, 2, 3) # 参数位置匹配
f(1, c = 3, b = 2) # 参数关键字匹配
def f(a, b=1, c=2): print(a, b, c)
f(1) # 默认参数匹配
f(1, 2) # 默认参数匹配
f(a = 1, c = 3) # 关键字参数和默认参数的混合
# Keyword-Only参数:出现在*args之后 必须用关键字进行匹配
def keyOnly(a, *b, c): print('') # c就为keyword-only匹配 必须使用关键字c = value匹配
def keyOnly(a, *, b, c): ...... # b c为keyword-only匹配 必须使用关键字匹配
def keyOnly(a, *, b = 1): ...... # b有默认值 或者省略 或者使用关键字参数b = value
#-- 可变参数匹配: * 和 **
def f(*args): print(args) # 在元组中收集不匹配的位置参数
f(1, 2, 3) # 输出(1, 2, 3)
def f(**args): print(args) # 在字典中收集不匹配的关键字参数
f(a = 1, b = 2) # 输出{'a':1, 'b':2}
def f(a, *b, **c): print(a, b, c) # 两者混合使用
f(1, 2, 3, x=4, y=5) # 输出1, (2, 3), {'x':4, 'y':5}
#-- 函数调用时的参数解包: * 和 ** 分别解包元组和字典
func(1, *(2, 3)) <==> func(1, 2, 3)
func(1, **{'c':3, 'b':2}) <==> func(1, b = 2, c = 3)
func(1, *(2, 3), **{'c':3, 'b':2}) <==> func(1, 2, 3, b = 2, c = 3)
#-- 函数属性:(自己定义的)函数可以添加属性
def func():.....
func.count = 1 # 自定义函数添加属性
print.count = 1 # Error 内置函数不可以添加属性
#-- 函数注解: 编写在def头部行 主要用于说明参数范围、参数类型、返回值类型等
def func(a:'spam', b:(1, 10), c:float) -> int :
print(a, b, c)
func.__annotations__ # {'c':, 'b':(1, 10), 'a':'spam', 'return':}
# 编写注解的同时 还是可以使用函数默认值 并且注解的位置位于=号的前边
def func(a:'spam'='a', b:(1, 10)=2, c:float=3) -> int :
print(a, b, c)
#-- 匿名函数:lambda
f = lambda x, y, z : x + y + z # 普通匿名函数，使用方法f(1, 2, 3)
f = lambda x = 1, y = 1: x + y # 带默认参数的lambda函数
def action(x): # 嵌套lambda函数
return (lambda y : x + y)
f = lambda: a if xxx() else b # 无参数的lambda函数，使用方法f()
#-- lambda函数与map filter reduce函数的结合
list(map((lambda x: x + 1), [1, 2, 3])) # [2, 3, 4]
list(filter((lambda x: x > 0), range(-4, 5))) # [1, 2, 3, 4]
functools.reduce((lambda x, y: x + y), [1, 2, 3]) # 6
functools.reduce((lambda x, y: x * y), [2, 3, 4]) # 24
#-- 生成器函数:yield VS return
def gensquare(N):
for i in range(N):
yield i** 2 # 状态挂起 可以恢复到此时的状态
for i in gensquare(5): # 使用方法
print(i, end = ' ') # [0, 1, 4, 9, 16]
x = gensquare(2) # x是一个生成对象
next(x) # 等同于x.__next__() 返回0
next(x) # 等同于x.__next__() 返回1
next(x) # 等同于x.__next__() 抛出异常StopIteration
#-- 生成器表达式:小括号进行列表解析
G = (x ** 2 for x in range(3)) # 使用小括号可以创建所需结果的生成器generator object
next(G), next(G), next(G) # 和上述中的生成器函数的返回值一致
#（1）生成器(生成器函数/生成器表达式)是单个迭代对象
G = (x ** 2 for x in range(4))
I1 = iter(G) # 这里实际上iter(G) = G
next(I1) # 输出0
next(G) # 输出1
next(I1) # 输出4
#（2）生成器不保留迭代后的结果
gen = (i for i in range(4))
2 in gen # 返回True
3 in gen # 返回True
1 in gen # 返回False，其实检测2的时候，1已经就不在生成器中了，即1已经被迭代过了，同理2、3也不在了
#-- 本地变量是静态检测的
X = 22 # 全局变量X的声明和定义
def test():
print(X) # 如果没有下一语句 则该句合法 打印全局变量X
X = 88 # 这一语句使得上一语句非法 因为它使得X变成了本地变量 上一句变成了打印一个未定义的本地变量(局部变量)
if False: # 即使这样的语句 也会把print语句视为非法语句 因为:
X = 88 # Python会无视if语句而仍然声明了局部变量X
def test(): # 改进
global X # 声明变量X为全局变量
print(X) # 打印全局变量X
X = 88 # 改变全局变量X
#-- 函数的默认值是在函数定义的时候实例化的 而不是在调用的时候 例子:
def foo(numbers=[]): # 这里的[]是可变的
numbers.append(9)
print(numbers)
foo() # first time, like before, [9]
foo() # second time, not like before, [9, 9]
foo() # third time, not like before too, [9, 9, 9]
# 改进:
def foo(numbers=None):
if numbers is None: numbers = []
numbers.append(9)
print(numbers)
# 另外一个例子 参数的默认值为不可变的:
def foo(count=0): # 这里的0是数字, 是不可变的
count += 1
print(count)
foo() # 输出1
foo() # 还是输出1
foo(3) # 输出4
foo() # 还是输出1
"""函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子----函数例子"""
"""数学运算类"""
abs(x) # 求绝对值，参数可以是整型，也可以是复数，若参数是复数，则返回复数的模
complex([real[, imag]]) # 创建一个复数
divmod(a, b) # 分别取商和余数，注意：整型、浮点型都可以
float([x]) # 将一个字符串或数转换为浮点数。如果无参数将返回0.0
int([x[, base]]) # 将一个字符串或浮点数转换为int类型，base表示进制
long([x[, base]]) # 将一个字符串或浮点数转换为long类型
pow(x, y) # 返回x的y次幂
range([start], stop[, step]) # 产生一个序列，默认从0开始
round(x[, n]) # 四舍五入
sum(iterable[, start]) # 对集合求和
oct(x) # 将一个数字转化为8进制字符串
hex(x) # 将一个数字转换为16进制字符串
chr(i) # 返回给定int类型对应的ASCII字符
unichr(i) # 返回给定int类型的unicode
ord(c) # 返回ASCII字符对应的整数
bin(x) # 将整数x转换为二进制字符串
bool([x]) # 将x转换为Boolean类型
"""集合类操作"""
basestring() # str和unicode的超类，不能直接调用，可以用作isinstance判断
format(value [, format_spec]) # 格式化输出字符串，格式化的参数顺序从0开始，如“I am {0},I like {1}”
enumerate(sequence[, start=0]) # 返回一个可枚举的对象，注意它有第二个参数
iter(obj[, sentinel]) # 生成一个对象的迭代器，第二个参数表示分隔符
max(iterable[, args...][key]) # 返回集合中的最大值
min(iterable[, args...][key]) # 返回集合中的最小值
dict([arg]) # 创建数据字典
list([iterable]) # 将一个集合类转换为另外一个集合类
set() # set对象实例化
frozenset([iterable]) # 产生一个不可变的set
tuple([iterable]) # 生成一个tuple类型
str([object]) # 转换为string类型
sorted(iterable[, cmp[, key[, reverse]]]) # 集合排序
L = [('b',2),('a',1),('c',3),('d',4)]
sorted(L, key=lambda x: x[1]), reverse=True) # 使用Key参数和reverse参数
sorted(L, key=lambda x: (x[0], x[1])) # 使用key参数进行多条件排序，即如果x[0]相同，则比较x[1]
"""逻辑判断"""
all(iterable) # 集合中的元素都为真的时候为真，特别的，若为空串返回为True
any(iterable) # 集合中的元素有一个为真的时候为真，特别的，若为空串返回为False
cmp(x, y) # 如果x < y ,返回负数；x == y, 返回0；x > y,返回正数
"""IO操作"""
file(filename [, mode [, bufsize]]) # file类型的构造函数。
input([prompt]) # 获取用户输入，推荐使用raw_input，因为该函数将不会捕获用户的错误输入，意思是自行判断类型
# 在 Built-in Functions 里有一句话是这样写的：Consider using the raw_input() function for general input from users.
raw_input([prompt]) # 设置输入，输入都是作为字符串处理
open(name[, mode[, buffering]]) # 打开文件，与file有什么不同？推荐使用open
"""其他"""
callable(object) # 检查对象object是否可调用
classmethod(func) # 用来说明这个func是个类方法
staticmethod(func) # 用来说明这个func为静态方法
dir([object]) # 不带参数时，返回当前范围内的变量、方法和定义的类型列表；带参数时，返回参数的属性、方法列表。
help(obj) # 返回obj的帮助信息
eval(expression) # 计算表达式expression的值，并返回
exec(str) # 将str作为Python语句执行
execfile(filename) # 用法类似exec()，不同的是execfile的参数filename为文件名，而exec的参数为字符串。
filter(function, iterable) # 构造一个序列，等价于[item for item in iterable if function(item)]，function返回值为True或False的函数
list(filter(bool, range(-3, 4)))# 返回[-3, -2, -1, 1, 2, 3], 没有0
hasattr(object, name) # 判断对象object是否包含名为name的特性
getattr(object, name [, defalut]) # 获取一个类的属性
setattr(object, name, value) # 设置属性值
delattr(object, name) # 删除object对象名为name的属性
globals() # 返回一个描述当前全局符号表的字典
hash(object) # 如果对象object为哈希表类型，返回对象object的哈希值
id(object) # 返回对象的唯一标识，一串数字
isinstance(object, classinfo) # 判断object是否是class的实例
isinstance(1, int) # 判断是不是int类型
isinstance(1, (int, float)) # isinstance的第二个参数接受一个元组类型
issubclass(class, classinfo) # 判断class是否为classinfo的子类
locals() # 返回当前的变量列表
map(function, iterable, ...) # 遍历每个元素，执行function操作
list(map(abs, range(-3, 4))) # 返回[3, 2, 1, 0, 1, 2, 3]
next(iterator[, default]) # 类似于iterator.next()
property([fget[, fset[, fdel[, doc]]]]) # 属性访问的包装类，设置后可以通过c.x=value等来访问setter和getter
reduce(function, iterable[, initializer]) # 合并操作，从第一个开始是前两个参数，然后是前两个的结果与第三个合并进行处理，以此类推
def add(x,y):return x + y
reduce(add, range(1, 11)) # 返回55 (注:1+2+3+4+5+6+7+8+9+10 = 55)
reduce(add, range(1, 11), 20) # 返回75
reload(module) # 重新加载模块
repr(object) # 将一个对象变幻为可打印的格式
slice(start, stop[, step]) # 产生分片对象
type(object) # 返回该object的类型
vars([object]) # 返回对象的变量名、变量值的字典
a = Class(); # Class为一个空类
a.name = 'qi', a.age = 9
vars(a) # {'name':'qi', 'age':9}
zip([iterable, ...]) # 返回对应数组
list(zip([1, 2, 3], [4, 5, 6])) # [(1, 4), (2, 5), (3, 6)]
a = [1, 2, 3], b = ["a", "b", "c"]
z = zip(a, b) # 压缩：[(1, "a"), (2, "b"), (3, "c")]
zip(*z) # 解压缩：[(1, 2, 3), ("a", "b", "c")]
unicode(string, encoding, errors) # 将字符串string转化为unicode形式，string为encoded string。
"""模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle----模块Moudle"""
#-- Python模块搜索路径:
"""
(1)程序的主目录 (2)PYTHONPATH目录 (3)标准链接库目录 (4)任何.pth文件的内容
"""
#-- 查看全部的模块搜索路径
import sys
sys.path
sys.argv # 获得脚本的参数
sys.builtin_module_names # 查找内建模块
sys.platform # 返回当前平台 出现如： "win32" "linux" "darwin"等
sys.modules # 查找已导入的模块
sys.modules.keys()
sys.stdout # stdout 和 stderr 都是类文件对象，但是它们都是只写的。它们都没有 read 方法，只有 write 方法
sys.stdout.write("hello")
sys.stderr
sys.stdin
#-- 模块的使用代码
import module1, module2 # 导入module1 使用module1.printer()
from module1 import printer # 导入module1中的printer变量 使用printer()
from module1 import * # 导入module1中的全部变量 使用不必添加module1前缀
#-- 重载模块reload: 这是一个内置函数 而不是一条语句
from imp import reload
reload(module)
#-- 模块的包导入:使用点号(.)而不是路径(dir1\dir2)进行导入
import dir1.dir2.mod # d导入包(目录)dir1中的包dir2中的mod模块 此时dir1必须在Python可搜索路径中
from dir1.dir2.mod import * # from语法的包导入
#-- __init__.py包文件:每个导入的包中都应该包含这么一个文件
"""
该文件可以为空
首次进行包导入时 该文件会自动执行
高级功能:在该文件中使用__all__列表来定义包(目录)以from*的形式导入时 需要导入什么
"""
#-- 包相对导入:使用点号(.) 只能使用from语句
from . import spam # 导入当前目录下的spam模块（Python2: 当前目录下的模块, 直接导入即可）
from .spam import name # 导入当前目录下的spam模块的name属性（Python2: 当前目录下的模块, 直接导入即可，不用加.）
from .. import spam # 导入当前目录的父目录下的spam模块
#-- 包相对导入与普通导入的区别
from string import * # 这里导入的string模块为sys.path路径上的 而不是本目录下的string模块(如果存在也不是)
from .string import * # 这里导入的string模块为本目录下的(不存在则导入失败) 而不是sys.path路径上的
#-- 模块数据隐藏:最小化from*的破坏
_X # 变量名前加下划线可以防止from*导入时该变量名被复制出去
__all__ = ['x', 'x1', 'x2'] # 使用__all__列表指定from*时复制出去的变量名(变量名在列表中为字符串形式)
#-- 可以使用__name__进行模块的单元测试:当模块为顶层执行文件时值为'__main__' 当模块被导入时为模块名
if __name__ == '__main__':
doSomething
# 模块属性中还有其他属性，例如：
__doc__ # 模块的说明文档
__file__ # 模块文件的文件名，包括全路径
__name__ # 主文件或者被导入文件
__package__ # 模块所在的包
#-- import语句from语句的as扩展
import modulename as name
from modulename import attrname as name
#-- 得到模块属性的几种方法 假设为了得到name属性的值
M.name
M.__dict__['name']
sys.modules['M'].name
getattr(M, 'name')
"""类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象----类与面向对象"""
#-- 最普通的类
class C1(C2, C3):
spam = 42 # 数据属性
def __init__(self, name): # 函数属性:构造函数
self.name = name
def __del__(self): # 函数属性:析构函数
print("goodbey ", self.name)
I1 = C1('bob')
#-- Python的类没有基于参数的函数重载
class FirstClass(object):
def test(self, string):
print(string)
def test(self): # 此时类中只有一个test函数 即后者test(self) 它覆盖掉前者带参数的test函数
print("hello world")
#-- 子类扩展超类: 尽量调用超类的方法
class Manager(Person):
def giveRaise(self, percent, bonus = .10):
self.pay = int(self.pay*(1 + percent + bonus)) # 不好的方式 复制粘贴超类代码
Person.giveRaise(self, percent + bonus) # 好的方式 尽量调用超类方法
#-- 类内省工具
bob = Person('bob')
bob.__class__ #
bob.__class__.__name__ # 'Person'
bob.__dict__ # {'pay':0, 'name':'bob', 'job':'Manager'}
#-- 返回1中 数据属性spam是属于类 而不是对象
I1 = C1('bob'); I2 = C2('tom') # 此时I1和I2的spam都为42 但是都是返回的C1的spam属性
C1.spam = 24 # 此时I1和I2的spam都为24
I1.spam = 3 # 此时I1新增自有属性spam 值为3 I2和C1的spam还都为24
#-- 类方法调用的两种方式
instance.method(arg...)
class.method(instance, arg...)
#-- 抽象超类的实现方法
# (1)某个函数中调用未定义的函数 子类中定义该函数
def delegate(self):
self.action() # 本类中不定义action函数 所以使用delegate函数时就会出错
# (2)定义action函数 但是返回异常
def action(self):
raise NotImplementedError("action must be defined")
# (3)上述的两种方法还都可以定义实例对象 实际上可以利用@装饰器语法生成不能定义的抽象超类
from abc import ABCMeta, abstractmethod
class Super(metaclass = ABCMeta):
@abstractmethod
def action(self): pass
x = Super() # 返回 TypeError: Can't instantiate abstract class Super with abstract methods action
#-- # OOP和继承: "is-a"的关系
class A(B):
pass
a = A()
isinstance(a, B) # 返回True, A是B的子类 a也是B的一种
# OOP和组合: "has-a"的关系
pass
# OOP和委托: "包装"对象 在Python中委托通常是以"__getattr__"钩子方法实现的, 这个方法会拦截对不存在属性的读取
# 包装类(或者称为代理类)可以使用__getattr__把任意读取转发给被包装的对象
class wrapper(object):
def __init__(self, object):
self.wrapped = object
def __getattr(self, attrname):
print('Trace: ', attrname)
return getattr(self.wrapped, attrname)
# 注:这里使用getattr(X, N)内置函数以变量名字符串N从包装对象X中取出属性 类似于X.__dict__[N]
x = wrapper([1, 2, 3])
x.append(4) # 返回 "Trace: append" [1, 2, 3, 4]
x = wrapper({'a':1, 'b':2})
list(x.keys()) # 返回 "Trace: keys" ['a', 'b']
#-- 类的伪私有属性:使用__attr
class C1(object):
def __init__(self, name):
self.__name = name # 此时类的__name属性为伪私有属性 原理 它会自动变成self._C1__name = name
def __str__(self):
return 'self.name = %s' % self.__name
I = C1('tom')
print(I) # 返回 self.name = tom
I.__name = 'jeey' # 这里无法访问 __name为伪私有属性
I._C1__name = 'jeey' # 这里可以修改成功 self.name = jeey
#-- 类方法是对象:无绑定类方法对象 / 绑定实例方法对象
class Spam(object):
def doit(self, message):
print(message)
def selfless(message)
print(message)
obj = Spam()
x = obj.doit # 类的绑定方法对象 实例 + 函数
x('hello world')
x = Spam.doit # 类的无绑定方法对象 类名 + 函数
x(obj, 'hello world')
x = Spam.selfless # 类的无绑定方法函数 在3.0之前无效
x('hello world')
#-- 获取对象信息: 属性和方法
a = MyObject()
dir(a) # 使用dir函数
hasattr(a, 'x') # 测试是否有x属性或方法 即a.x是否已经存在
setattr(a, 'y', 19) # 设置属性或方法 等同于a.y = 19
getattr(a, 'z', 0) # 获取属性或方法 如果属性不存在 则返回默认值0
#这里有个小技巧，setattr可以设置一个不能访问到的属性，即只能用getattr获取
setattr(a, "can't touch", 100) # 这里的属性名带有空格，不能直接访问
getattr(a, "can't touch", 0) # 但是可以用getattr获取
#-- 为类动态绑定属性或方法: MethodType方法
# 一般创建了一个class的实例后, 可以给该实例绑定任何属性和方法, 这就是动态语言的灵活性
class Student(object):
pass
s = Student()
s.name = 'Michael' # 动态给实例绑定一个属性
def set_age(self, age): # 定义一个函数作为实例方法
self.age = age
from types import MethodType
s.set_age = MethodType(set_age, s) # 给实例绑定一个方法 类的其他实例不受此影响
s.set_age(25) # 调用实例方法
Student.set_age = MethodType(set_age, Student) # 为类绑定一个方法 类的所有实例都拥有该方法
"""类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题----类的高级话题"""
#-- 多重继承: "混合类", 搜索方式"从下到上 从左到右 广度优先"
class A(B, C):
pass
#-- 类的继承和子类的初始化
# 1.子类定义了__init__方法时，若未显示调用基类__init__方法，python不会帮你调用。
# 2.子类未定义__init__方法时，python会自动帮你调用首个基类的__init__方法，注意是首个。
# 3.子类显示调用基类的初始化函数：
class FooParent(object):
def __init__(self, a):
self.parent = 'I\'m the Parent.'
print('Parent:a=' + str(a))
def bar(self, message):
print(message + ' from Parent')
class FooChild(FooParent):
def __init__(self, a):
FooParent.__init__(self, a)
print('Child:a=' + str(a))
def bar(self, message):
FooParent.bar(self, message)
print(message + ' from Child')
fooChild = FooChild(10)
fooChild.bar('HelloWorld')
#-- #实例方法 / 静态方法 / 类方法
class Methods(object):
def imeth(self, x): print(self, x) # 实例方法：传入的是实例和数据，操作的是实例的属性
def smeth(x): print(x) # 静态方法：只传入数据 不传入实例，操作的是类的属性而不是实例的属性
def cmeth(cls, x): print(cls, x) # 类方法：传入的是类对象和数据
smeth = staticmethod(smeth) # 调用内置函数，也可以使用@staticmethod
cmeth = classmethod(cmeth) # 调用内置函数，也可以使用@classmethod
obj = Methods()
obj.imeth(1) # 实例方法调用 <__main__.Methods object...> 1
Methods.imeth(obj, 2) # <__main__.Methods object...> 2
Methods.smeth(3) # 静态方法调用 3
obj.smeth(4) # 这里可以使用实例进行调用
Methods.cmeth(5) # 类方法调用 5
obj.cmeth(6) # 6
#-- 函数装饰器:是它后边的函数的运行时的声明 由@符号以及后边紧跟的"元函数"(metafunction)组成
@staticmethod
def smeth(x): print(x)
# 等同于:
def smeth(x): print(x)
smeth = staticmethod(smeth)
# 同理
@classmethod
def cmeth(cls, x): print(x)
# 等同于
def cmeth(cls, x): print(x)
cmeth = classmethod(cmeth)
#-- 类修饰器:是它后边的类的运行时的声明 由@符号以及后边紧跟的"元函数"(metafunction)组成
def decorator(aClass):.....
@decorator
class C(object):....
# 等同于:
class C(object):....
C = decorator(C)
#-- 限制class属性: __slots__属性
class Student(object):
__slots__ = ('name', 'age') # 限制Student及其实例只能拥有name和age属性
# __slots__属性只对当前类起作用, 对其子类不起作用
# __slots__属性能够节省内存
# __slots__属性可以为列表list，或者元组tuple
#-- 类属性高级话题: @property
# 假设定义了一个类:C，该类必须继承自object类，有一私有变量_x
class C(object):
def __init__(self):
self.__x = None
# 第一种使用属性的方法
def getx(self):
return self.__x
def setx(self, value):
self.__x = value
def delx(self):
del self.__x
x = property(getx, setx, delx, '')
# property函数原型为property(fget=None,fset=None,fdel=None,doc=None)
# 使用
c = C()
c.x = 100 # 自动调用setx方法
y = c.x # 自动调用getx方法
del c.x # 自动调用delx方法
# 第二种方法使用属性的方法
@property
def x(self):
return self.__x
@x.setter
def x(self, value):
self.__x = value
@x.deleter
def x(self):
del self.__x
# 使用
c = C()
c.x = 100 # 自动调用setter方法
y = c.x # 自动调用x方法
del c.x # 自动调用deleter方法
#-- 定制类: 重写类的方法
# (1)__str__方法、__repr__方法: 定制类的输出字符串
# (2)__iter__方法、next方法: 定制类的可迭代性
class Fib(object):
def __init__(self):
self.a, self.b = 0, 1 # 初始化两个计数器a，b
def __iter__(self):
return self # 实例本身就是迭代对象，故返回自己
def next(self):
self.a, self.b = self.b, self.a + self.b
if self.a > 100000: # 退出循环的条件
raise StopIteration()
return self.a # 返回下一个值
for n in Fib():
print(n) # 使用
# (3)__getitem__方法、__setitem__方法: 定制类的下标操作[] 或者切片操作slice
class Indexer(object):
def __init__(self):
self.data = {}
def __getitem__(self, n): # 定义getitem方法
print('getitem:', n)
return self.data[n]
def __setitem__(self, key, value): # 定义setitem方法
print('setitem:key = {0}, value = {1}'.format(key, value))
self.data[key] = value
test = Indexer()
test[0] = 1; test[3] = '3' # 调用setitem方法
print(test[0]) # 调用getitem方法
# (4)__getattr__方法: 定制类的属性操作
class Student(object):
def __getattr__(self, attr): # 定义当获取类的属性时的返回值
if attr=='age':
return 25 # 当获取age属性时返回25
raise AttributeError('object has no attribute: %s' % attr)
# 注意: 只有当属性不存在时 才会调用该方法 且该方法默认返回None 需要在函数最后引发异常
s = Student()
s.age # s中age属性不存在 故调用__getattr__方法 返回25
# (5)__call__方法: 定制类的'可调用'性
class Student(object):
def __call__(self): # 也可以带参数
print('Calling......')
s = Student()
s() # s变成了可调用的 也可以带参数
callable(s) # 测试s的可调用性 返回True
# (6)__len__方法：求类的长度
def __len__(self):
return len(self.data)
#-- 动态创建类type()
# 一般创建类 需要在代码中提前定义
class Hello(object):
def hello(self, name='world'):
print('Hello, %s.' % name)
h = Hello()
h.hello() # Hello, world
type(Hello) # Hello是一个type类型 返回
type(h) # h是一个Hello类型 返回
# 动态类型语言中 类可以动态创建 type函数可用于创建新类型
def fn(self, name='world'): # 先定义函数
print('Hello, %s.' % name)
Hello = type('Hello', (object,), dict(hello=fn)) # 创建Hello类 type原型: type(name, bases, dict)
h = Hello() # 此时的h和上边的h一致
"""异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关----异常相关"""
#-- #捕获异常:
try:
except: # 捕获所有的异常 等同于except Exception:
except name: # 捕获指定的异常
except name, value: # 捕获指定的异常和额外的数据(实例)
except (name1, name2):
except (name1, name2), value:
except name4 as X:
else: # 如果没有发生异常
finally: # 总会执行的部分
# 引发异常: raise子句(raise IndexError)
raise # raise instance of a class, raise IndexError()
raise # make and raise instance of a class, raise IndexError
raise # reraise the most recent exception
#-- Python3.x中的异常链: raise exception from otherException
except Exception as X:
raise IndexError('Bad') from X
#-- assert子句: assert ,
assert x < 0, 'x must be negative'
#-- with/as环境管理器:作为常见的try/finally用法模式的替代方案
with expression [as variable], expression [as variable]:
# 例子:
with open('test.txt') as myfile:
for line in myfile: print(line)
# 等同于:
myfile = open('test.txt')
try:
for line in myfile: print(line)
finally:
myfile.close()
#-- 用户自定义异常: class Bad(Exception):.....
"""
Exception超类 / except基类即可捕获到其所有子类
Exception超类有默认的打印消息和状态 当然也可以定制打印显示:
"""
class MyBad(Exception):
def __str__(self):
return '定制的打印消息'
try:
MyBad()
except MyBad as x:
print(x)
#-- 用户定制异常数据
class FormatError(Exception):
def __init__(self, line ,file):
self.line = line
self.file = file
try:
raise FormatError(42, 'test.py')
except FormatError as X:
print('Error at ', X.file, X.line)
# 用户定制异常行为(方法):以记录日志为例
class FormatError(Exception):
logfile = 'formaterror.txt'
def __init__(self, line ,file):
self.line = line
self.file = file
def logger(self):
open(self.logfile, 'a').write('Error at ', self.file, self.line)
try:
raise FormatError(42, 'test.py')
except FormatError as X:
X.logger()
#-- 关于sys.exc_info:允许一个异常处理器获取对最近引发的异常的访问
try:
......
except:
# 此时sys.exc_info()返回一个元组(type, value, traceback)
# type:正在处理的异常的异常类型
# value:引发的异常的实例
# traceback:堆栈信息
#-- 异常层次
BaseException
+-- SystemExit
+-- KeyboardInterrupt
+-- GeneratorExit
+-- Exception
+-- StopIteration
+-- ArithmeticError
+-- AssertionError
+-- AttributeError
+-- BufferError
+-- EOFError
+-- ImportError
+-- LookupError
+-- MemoryError
+-- NameError
+-- OSError
+-- ReferenceError
+-- RuntimeError
+-- SyntaxError
+-- SystemError
+-- TypeError
+-- ValueError
+-- Warning
"""Unicode和字节字符串---Unicode和字节字符串----Unicode和字节字符串----Unicode和字节字符串----Unicode和字节字符串----Unicode和字节字符串----Unicode和字节字符串"""
#-- Python的字符串类型
"""Python2.x"""
# 1.str表示8位文本和二进制数据
# 2.unicode表示宽字符Unicode文本
"""Python3.x"""
# 1.str表示Unicode文本（8位或者更宽）
# 2.bytes表示不可变的二进制数据
# 3.bytearray是一种可变的bytes类型
#-- 字符编码方法
"""ASCII""" # 一个字节，只包含英文字符，0到127，共128个字符，利用函数可以进行字符和数字的相互转换
ord('a') # 字符a的ASCII码为97，所以这里返回97
chr(97) # 和上边的过程相反，返回字符'a'
"""Latin-1""" # 一个字节，包含特殊字符，0到255，共256个字符，相当于对ASCII码的扩展
chr(196) # 返回一个特殊字符：Ä
"""Unicode""" # 宽字符，一个字符包含多个字节，一般用于亚洲的字符集，比如中文有好几万字
"""UTF-8""" # 可变字节数，小于128的字符表示为单个字节，128到0X7FF之间的代码转换为两个字节，0X7FF以上的代码转换为3或4个字节
# 注意：可以看出来，ASCII码是Latin-1和UTF-8的一个子集
# 注意：utf-8是unicode的一种实现方式，unicode、gbk、gb2312是编码字符集
#-- 查看Python中的字符串编码名称，查看系统的编码
import encodings
help(encoding)
import sys
sys.platform # 'win64'
sys.getdefaultencoding() # 'utf-8'
sys.getdefaultencoding() # 返回当前系统平台的编码类型
sys.getsizeof(object) # 返回object占有的bytes的大小
#-- 源文件字符集编码声明: 添加注释来指定想要的编码形式 从而改变默认值 注释必须出现在脚本的第一行或者第二行
"""说明：其实这里只会检查#和coding:utf-8，其余的字符都是为了美观加上的"""
# _*_ coding: utf-8 _*_
# coding = utf-8
#-- #编码: 字符串 --> 原始字节 #解码: 原始字节 --> 字符串
#-- Python3.x中的字符串应用
s = '...' # 构建一个str对象，不可变对象
b = b'...' # 构建一个bytes对象，不可变对象
s[0], b[0] # 返回('.', 113)
s[1:], b[1:] # 返回('..', b'..')
B = B"""
xxxx
yyyy
"""
# B = b'\nxxxx\nyyyy\n'
# 编码，将str字符串转化为其raw bytes形式：
str.encode(encoding = 'utf-8', errors = 'strict')
bytes(str, encoding)
# 编码例子：
S = 'egg'
S.encode() # b'egg'
bytes(S, encoding = 'ascii') # b'egg'
# 解码，将raw bytes字符串转化为str形式：
bytes.decode(encoding = 'utf-8', errors = 'strict')
str(bytes_or_buffer[, encoding[, errors]])
# 解码例子：
B = b'spam'
B.decode() # 'spam'
str(B) # "b'spam'"，不带编码的str调用，结果为打印该bytes对象
str(B, encoding = 'ascii')# 'spam'，带编码的str调用，结果为转化该bytes对象
#-- Python2.x的编码问题
u = u'汉'
print repr(u) # u'\xba\xba'
s = u.encode('UTF-8')
print repr(s) # '\xc2\xba\xc2\xba'
u2 = s.decode('UTF-8')
print repr(u2) # u'\xba\xba'
# 对unicode进行解码是错误的
s2 = u.decode('UTF-8') # UnicodeEncodeError: 'ascii' codec can't encode characters in position 0-1: ordinal not in range(128)
# 同样，对str进行编码也是错误的
u2 = s.encode('UTF-8') # UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 0: ordinal not in range(128)
#-- bytes对象
B = b'abc'
B = bytes('abc', 'ascii')
B = bytes([97, 98, 99])
B = 'abc'.encode()
# bytes对象的方法调用基本和str类型一致 但:B[0]返回的是ASCII码值97, 而不是b'a'
#-- #文本文件: 根据Unicode编码来解释文件内容，要么是平台的默认编码，要么是指定的编码类型
# 二进制文件：表示字节值的整数的一个序列 open('bin.txt', 'rb')
#-- Unicode文件
s = 'A\xc4B\xe8C' # s = 'A?BèC' len(s) = 5
#手动编码
l = s.encode('latin-1') # l = b'A\xc4B\xe8C' len(l) = 5
u = s.encode('utf-8') # u = b'A\xc3\x84B\xc3\xa8C' len(u) = 7
#文件输出编码
open('latindata', 'w', encoding = 'latin-1').write(s)
l = open('latindata', 'rb').read() # l = b'A\xc4B\xe8C' len(l) = 5
open('uft8data', 'w', encoding = 'utf-8').write(s)
u = open('uft8data', 'rb').read() # u = b'A\xc3\x84B\xc3\xa8C' len(u) = 7
#文件输入编码
s = open('latindata', 'r', encoding = 'latin-1').read() # s = 'A?BèC' len(s) = 5
s = open('latindata', 'rb').read().decode('latin-1') # s = 'A?BèC' len(s) = 5
s = open('utf8data', 'r', encoding = 'utf-8').read() # s = 'A?BèC' len(s) = 5
s = open('utf8data', 'rb').read().decode('utf-8') # s = 'A?BèC' len(s) = 5
"""其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他----其他"""
#-- Python实现任意深度的赋值 例如a[0] = 'value1'; a[1][2] = 'value2'; a[3][4][5] = 'value3'
class MyDict(dict):
def __setitem__(self, key, value): # 该函数不做任何改动 这里只是为了输出
print('setitem:', key, value, self)
super().__setitem__(key, value)
def __getitem__(self, item): # 主要技巧在该函数
print('getitem:', item, self) # 输出信息
# 基本思路: a[1][2]赋值时 需要先取出a[1] 然后给a[1]的[2]赋值
if item not in self: # 如果a[1]不存在 则需要新建一个dict 并使得a[1] = dict
temp = MyDict() # 新建的dict: temp
super().__setitem__(item, temp) # 赋值a[1] = temp
return temp # 返回temp 使得temp[2] = value有效
return super().__getitem__(item) # 如果a[1]存在 则直接返回a[1]
# 例子:
test = MyDict()
test[0] = 'test'
print(test[0])
test[1][2] = 'test1'
print(test[1][2])
test[1][3] = 'test2'
print(test[1][3])
#-- Python中的多维数组
lists = [0] * 3 # 扩展list，结果为[0, 0, 0]
lists = [[]] * 3 # 多维数组，结果为[[], [], []]，但有问题，往下看
lists[0].append(3) # 期望看到的结果[[3], [], []]，实际结果[[3], [3], [3]]，原因：list*n操作，是浅拷贝，如何避免？往下看
lists = [[] for i in range(3)] # 多维数组，结果为[[], [], []]
lists[0].append(3) # 结果为[[3], [], []]
lists[1].append(6) # 结果为[[3], [6], []]
lists[2].append(9) # 结果为[[3], [6], [9]]
lists = [[[] for j in range(4)] for i in range(3)] # 3行4列，且每一个元素为[]
================================================
FILE: docs/Algorithm/DataStructure/Summarization/python的各种pass.md
================================================
#Python的各种Pass
感觉最近对于pass by reference 和 pass by value又有了一点/一些认识
1. python不允许程序员选择采用传值还是传引用。Python参数传递采用的肯定是“传对象引用”的方式。实际上，这种方式相当于传值和传引用的一种综合。如果函数收到的是一个可变对象（比如字典或者列表）的引用，就能修改对象的原始值——相当于通过“传引用”来传递对象。如果函数收到的是一个不可变对象（比如数字、字符或者元组）的引用，就不能直接修改原始对象——相当于通过“传值'来传递对象。
2. 当人们复制列表或字典时，就复制了对象列表的引用同，如果改变引用的值，则修改了原始的参数。
3. 为了简化内存管理，Python通过引用计数机制实现自动垃圾回收功能，Python中的每个对象都有一个引用计数，用来计数该对象在不同场所分别被引用了多少次。每当引用一次Python对象，相应的引用计数就增1，每当消毁一次Python对象，则相应的引用就减1，只有当引用计数为零时，才真正从内存中删除Python对象。
##### Linked List的例子
```
class ListNode(object):
def __init__(self, x):
self.val = x
self.next = None
node1 = ListNode(1)
node2 = ListNode(2)
node3 = ListNode(3)
node4 = ListNode(4)
node5 = ListNode(5)
node1.next = node2
node2.next = node3
node3.next = node4
node4.next = node5
```
来改变head
```
def testWithPointers1(head):
head.next = None
```
运行 testWithPointers1(node1)
然后node1.next 为None了
// 可以理解，因为传进去的是head这个可变对象。
```
def testWithPointers2(head):
cur = head
cur.next = None
```
运行 testWithPointers2(node1)
// node1.next 同样为None了
Python的object，list都是pass by reference，所以是改变的
看另外一个例子：
```
def printLinkedList(head):
while head:
print(head)
head = head.next
```
输出
```
printLinkedList(head)
<__main__.ListNode object at 0x1044c0e10>
1
<__main__.ListNode object at 0x1044c0fd0>
2
<__main__.ListNode object at 0x1044c0c88>
3
<__main__.ListNode object at 0x1044c0be0>
4
<__main__.ListNode object at 0x1044c0780>
5
head
Out[39]: <__main__.ListNode at 0x1044c0e10>
```
其实这里的head为什么没有改变有点疑惑
##### String看一下
a = "abc"
def changeA(s):
s = ""
changeA(a)
a 并不会改变，依旧为'abc'
================================================
FILE: docs/Algorithm/DataStructure/Summarization/slide_windows_template.md
================================================
能用此模板解决的题目目前有如下：
[leetcode 003](https://github.com/Lisanaaa/thinking_in_lc/blob/master/003._longest_substring_without_repeating_characters.md),
[leetcode 030](https://github.com/Lisanaaa/thinking_in_lc/edit/master/30._Substring_with_Concatenation_of_All_Words.md),
[leetcode 076](https://github.com/Lisanaaa/thinking_in_lc/blob/master/076._Minimum_Window_Substring.md),
[leetcode 159](https://github.com/Lisanaaa/thinking_in_lc/blob/master/159._Longest_Substring_with_At_Most_Two_Distinct_Characters.md),
[leetcode 438](https://github.com/Lisanaaa/thinking_in_lc/blob/master/438._Find_All_Anagrams_in_a_String.md)
带注释python版本
```python
class Solution(object):
def slideWindowTemplateByLisanaaa(self, s, t):
"""
:type s: str
:type t: str
:rtype: 具体题目具体分析
"""
# init a collection or int value to save the result according the question.
res = []
if len(t) > len(s):
return res
# create a hashmap to save the Characters of the target substring.
# (K, V) = (Character, Frequence of the Characters)
maps = collections.Counter(t)
# maintain a counter to check whether match the target string.
# must be the map size, NOT the string size because the char may be duplicate.
counter = len(maps.keys())
# Two Pointers: begin - left pointer of the window; end - right pointer of the window
begin, end = 0, 0
# the length of the substring which match the target string.
length = sys.maxint
# loop at the begining of the source string
while end < len(s):
if s[end] in maps:
maps[s[end]] -= 1 # plus or minus one
if maps[s[end]] == 0:
counter -= 1 # modify the counter according the requirement(different condition).
end += 1
# increase begin pointer to make it invalid/valid again
while counter == 0: # counter condition. different question may have different condition
if s[begin] in maps:
maps[s[begin]] += 1 # plus or minus one
if maps[s[begin]] > 0:
counter += 1 # modify the counter according the requirement(different condition).
begin += 1
'''
type your code here according to the question
1. save / update(min/max) the result if find a target
2. result: collections or int value
'''
return res
```
无注释python版本：
```python
class Solution(object):
def slideWindowTemplateByLisanaaa(self, s, t):
res = []
if len(t) > len(s):
return res
maps = collections.Counter(t)
counter = len(maps.keys())
begin, end = 0, 0
length = sys.maxint
while end < len(s):
if s[end] in maps:
maps[s[end]] -= 1
if maps[s[end]] == 0:
counter -= 1
end += 1
while counter == 0:
if s[begin] in maps:
maps[s[begin]] += 1
if maps[s[begin]] > 0:
counter += 1
begin += 1
'''
1. save / update(min/max) the result if find a target
2. result: collections or int value
'''
return res
```
带注释java版本
```java
public class Solution {
public List slidingWindowTemplateByHarryChaoyangHe(String s, String t) {
//init a collection or int value to save the result according the question.
List result = new LinkedList<>();
if(t.length()> s.length()) return result;
//create a hashmap to save the Characters of the target substring.
//(K, V) = (Character, Frequence of the Characters)
Map map = new HashMap<>();
for(char c : t.toCharArray()){
map.put(c, map.getOrDefault(c, 0) + 1);
}
//maintain a counter to check whether match the target string.
int counter = map.size();//must be the map size, NOT the string size because the char may be duplicate.
//Two Pointers: begin - left pointer of the window; end - right pointer of the window
int begin = 0, end = 0;
//the length of the substring which match the target string.
int len = Integer.MAX_VALUE;
//loop at the begining of the source string
while(end < s.length()){
char c = s.charAt(end);//get a character
if( map.containsKey(c) ){
map.put(c, map.get(c)-1);// plus or minus one
if(map.get(c) == 0) counter--;//modify the counter according the requirement(different condition).
}
end++;
//increase begin pointer to make it invalid/valid again
while(counter == 0 /* counter condition. different question may have different condition */){
char tempc = s.charAt(begin);//***be careful here: choose the char at begin pointer, NOT the end pointer
if(map.containsKey(tempc)){
map.put(tempc, map.get(tempc) + 1);//plus or minus one
if(map.get(tempc) > 0) counter++;//modify the counter according the requirement(different condition).
}
/* save / update(min/max) the result if find a target*/
// result collections or result int value
begin++;
}
}
return result;
}
}
```
无注释java版本：
```java
public class Solution {
public List slidingWindowTemplateByHarryChaoyangHe(String s, String t) {
List result = new LinkedList<>();
if(t.length()> s.length()) return result;
Map map = new HashMap<>();
for(char c : t.toCharArray()){
map.put(c, map.getOrDefault(c, 0) + 1);
}
int counter = map.size();
int begin = 0, end = 0;
int len = Integer.MAX_VALUE;
while(end < s.length()){
char c = s.charAt(end);
if( map.containsKey(c) ){
map.put(c, map.get(c)-1);
if(map.get(c) == 0) counter--;
}
end++;
while(counter == 0){
char tempc = s.charAt(begin);
if(map.containsKey(tempc)){
map.put(tempc, map.get(tempc) + 1);
if(map.get(tempc) > 0) counter++;
}
/*
save / update(min/max) the result if find a target
result collections or result int value
*/
begin++;
}
}
return result;
}
}
```
================================================
FILE: docs/Algorithm/DataStructure/Summarization/union_find.md
================================================
## 并查集（参考leetcode323题）
### 应用场景：
动态联通性
- 网络连接判断：
如果每个pair中的两个整数用来表示这两个节点是需要连通的，那么为所有的pairs建立了动态连通图后，就能够尽可能少的减少布线的需要，
因为已经连通的两个节点会被直接忽略掉。例如[1,2]代表节点1和节点2是联通的，如果再次出现[2,1]我们就不需要再连接他们了，因为已经连接过一次了。
- 变量名等同性(类似于指针的概念)：
在程序中，可以声明多个引用来指向同一对象，这个时候就可以通过为程序中声明的引用和实际对象建立动态连通图来判断哪些引用实际上是指向同一对象。
### 对问题建模：
在对问题进行建模的时候，我们应该尽量想清楚需要解决的问题是什么。因为模型中选择的数据结构和算法显然会根据问题的不同而不同，
就动态连通性这个场景而言，我们需要解决的问题可能是：
1. 给出两个节点，判断它们是否连通，如果连通，不需要给出具体的路径
2. 给出两个节点，判断它们是否连通，如果连通，需要给出具体的路径
就上面两种问题而言，虽然只有是否能够给出具体路径的区别，但是这个区别导致了选择算法的不同
### 建模思路：
最简单而直观的假设是，对于连通的所有节点，我们可以认为它们属于一个组，因此不连通的节点必然就属于不同的组。
随着Pair的输入，我们需要首先判断输入的两个节点是否连通。如何判断呢？按照上面的假设，我们可以通过判断它们属于的组，然后看看这两个组是否相同。
如果相同，那么这两个节点连通，反之不连通。为简单起见，我们将所有的节点以整数表示，即对N个节点使用0到N-1的整数表示。
而在处理输入的Pair之前，每个节点必然都是孤立的，即他们分属于不同的组，可以使用数组来表示这一层关系。
数组的index是节点的整数表示，而相应的值就是该节点的组号了。该数组可以初始化为：```uf = [i for i in range(n)]```
初始化完毕之后，对该动态连通图有几种可能的操作：
1. 查询节点属于的组
数组对应位置的值即为组号
2. 判断两个节点是否属于同一个组
分别得到两个节点的组号，然后判断组号是否相等
3. 连接两个节点，使之属于同一个组
分别得到两个节点的组号，组号相同时操作结束，不同时，将其中的一个节点的组号换成另一个节点的组号
4. 获取组的数目
初始化为节点的数目，然后每次成功连接两个节点之后，递减1
### API：
我们可以设置对应的API
```python
def uf(n) # 初始化uf数组和组数目
def union(x, y) # 连接两个节点
def find(x) # 判断节点所属于的组
def connected(x, y) # 判断两个节点是否联通
def count(x) # 返回所有组的数目
```
注意其中使用整数来表示节点，如果需要使用其他的数据类型表示节点，比如使用字符串，那么可以用哈希表来进行映射，即将String映射成这里需要的Integer类型。
分析以上的API，方法connected和union都依赖于find，connected对两个参数调用两次find方法，而union在真正执行union之前也需要判断是否连通，
这又是两次调用find方法。因此我们需要把find方法的实现设计的尽可能的高效。所以就有了下面的Quick-Find实现。
### Quick-Find实现
```
class Solution(object):
uf = [] # access to component id (site indexed)
count = 0 # number of components
def uf(n): # 初始化uf数组和组数目
self.count = n
self.uf = [i for i in range(n)]
def find(self, x): # 判断节点所属于的组
return uf[x]
def union(self, x, y): # 连接两个节点
x_root = find(x)
y_root = find(y)
if x_root == y_root:
return
for i in range(len(self.uf)):
if uf[i] == x_root:
uf[i] = y_root
count -= 1
def connected(self, x, y): # 判断两个节点是否联通
return find(x) == find(y)
def count(x): # 返回所有组的数目
return count
```
上述代码的find方法十分高效，因为仅仅需要一次数组读取操作就能够找到该节点的组号。
但是问题随之而来，对于需要添加新路径的情况，就涉及到对于组号的修改，因为并不能确定哪些节点的组号需要被修改，
因此就必须对整个数组进行遍历，找到需要修改的节点，逐一修改，这一下每次添加新路径带来的复杂度就是线性关系了，
如果要添加的新路径的数量是M，节点数量是N，那么最后的时间复杂度就是MN，显然是一个平方阶的复杂度，对于大规模的数据而言，平方阶的算法是存在问题的，
这种情况下，每次添加新路径就是“牵一发而动全身”，想要解决这个问题，关键就是要提高union方法的效率，让它不再需要遍历整个数组。
### Quick-Union 算法：
考虑一下，为什么以上的解法会造成“牵一发而动全身”？因为每个节点所属的组号都是单独记录，各自为政的，没有将它们以更好的方式组织起来，
当涉及到修改的时候，除了逐一通知、修改，别无他法。所以现在的问题就变成了，如何将节点以更好的方式组织起来，组织的方式有很多种，
但是最直观的还是将组号相同的节点组织在一起，想想所学的数据结构，什么样子的数据结构能够将一些节点给组织起来？
常见的就是链表，图，树，什么的了。但是哪种结构对于查找和修改的效率最高？毫无疑问是树，因此考虑如何将节点和组的关系以树的形式表现出来。
如果不改变底层数据结构，即不改变使用数组的表示方法的话。可以采用parent-link的方式将节点组织起来，
举例而言，uf[p]的值就是p节点的父节点的序号，如果p是树根的话，uf[p]的值就是p，因此最后经过若干次查找，一个节点总是能够找到它的根节点，
即满足uf[root] = root的节点也就是组的根节点了，然后就可以使用根节点的序号来表示组号。所以在处理一个pair的时候，
将首先找到pair中每一个节点的组号(即它们所在树的根节点的序号)，如果属于不同的组的话，就将其中一个根节点的父节点设置为另外一个根节点，
相当于将一颗独立的树编程另一颗独立的树的子树。直观的过程如下图所示。但是这个时候又引入了问题。
树这种数据结构容易出现极端情况，因为在建树的过程中，树的最终形态严重依赖于输入数据本身的性质，比如数据是否排序，是否随机分布等等。
比如在输入数据是有序的情况下，构造的BST会退化成一个链表。在我们这个问题中，也是会出现的极端情况的。
```
class Solution(object):
uf = [] # access to component id (site indexed)
count = 0 # number of components
def uf(n): # 初始化uf数组和组数目
self.count = n
self.uf = [i for i in range(n)]
def find(self, x): # 判断节点所属于的组
# if uf[x] != x: ## 这种方式也可以，但是递归次数多了容易出问题
# uf[x] = find(uf[x])
while x != uf[x]:
x = uf[x]
return uf[x]
def union(self, x, y): # 连接两个节点
x_root = find(x)
y_root = find(y)
uf[x_root] = y_root
count -= 1
def connected(self, x, y): # 判断两个节点是否联通
return find(x) == find(y)
def count(x): # 返回所有组的数目
return count
```
为了克服这个问题，BST可以演变成为红黑树或者AVL树等等。
然而，在我们考虑的这个应用场景中，每对节点之间是不具备可比性的。因此需要想其它的办法。在没有什么思路的时候，多看看相应的代码可能会有一些启发，
考虑一下Quick-Union算法中的union方法实现：
```
def union(self, x, y): # 连接两个节点
x_root = find(x)
y_root = find(y)
uf[x_root] = y_root
count -= 1
```
上面 id[pRoot] = qRoot 这行代码看上去似乎不太对劲。因为这也属于一种“硬编码”，这样实现是基于一个约定，即p所在的树总是会被作为q所在树的子树，
从而实现两颗独立的树的融合。那么这样的约定是不是总是合理的呢？显然不是，比如p所在的树的规模比q所在的树的规模大的多时，
p和q结合之后形成的树就是十分不和谐的一头轻一头重的”畸形树“了。
所以我们应该考虑树的大小，然后再来决定到底是调用 uf[x_root] = y_root 或者是 uf[y_root] = x_root
即总是size小的树作为子树和size大的树进行合并。这样就能够尽量的保持整棵树的平衡。
所以现在的问题就变成了：树的大小该如何确定？
我们回到最初的情形，即每个节点最一开始都是属于一个独立的组，通过下面的代码进行初始化：
```
tree_size = [1 for i in range(n)]
```
然后：
```
def union(self, x, y): # 连接两个节点
x_root = find(x)
y_root = find(y)
if tree_size[x_root] <= tree_size[y_root]:
uf[x_root] = y_root
tree_size[y_root] += tree_size[x_root]
else:
uf[y_root] = x_root
tree_size[x_root] += tree_size[y_root]
count -= 1
```
可以发现，通过tree_size数组决定如何对两棵树进行合并之后，最后得到的树的高度大幅度减小了。这是十分有意义的。
因为在Quick-Union算法中的任何操作，都不可避免的需要调用find方法，而该方法的执行效率依赖于树的高度。树的高度减小了，
find方法的效率就增加了，从而也就增加了整个Quick-Union算法的效率。
上面的论证其实还可以给我们一些启示，即对于Quick-Union算法而言，节点组织的理想情况应该是一颗十分扁平的树，所有的孩子节点应该都在height为1的地方，
即所有的孩子都直接连接到根节点。这样的组织结构能够保证find操作的最高效率。
那么如何构造这种理想结构呢？
在find方法的执行过程中，不是需要进行一个while循环找到根节点嘛？如果保存所有路过的中间节点到一个数组中，然后在while循环结束之后，
将这些中间节点的父节点指向根节点，不就行了么？但是这个方法也有问题，因为find操作的频繁性，会造成频繁生成中间节点数组，
相应的分配销毁的时间自然就上升了。那么有没有更好的方法呢？还是有的，即将节点的父节点指向该节点的爷爷节点，这一点很巧妙，
十分方便且有效，相当于在寻找根节点的同时，对路径进行了压缩，使整个树结构扁平化。相应的实现如下，实际上只需要添加一行代码：
```
def find(self, x): # 判断节点所属于的组
uf[x] = uf[uf[x]]
return uf[x]
```
综上，我决定以后解决问题的时候用这个模版就行了：
```python
class Solution(object):
uf = [] # access to component id (site indexed)
count = 0 # number of components
def uf(n): # 初始化uf数组和组数目
self.count = n
self.uf = [i for i in range(n)]
def find(self, x): # 判断节点所属于的组
while x != uf[x]:
uf[x] = uf[uf[x]]
x = uf[x]
return uf[x]
def union(self, x, y): # 连接两个节点
x_root = find(x)
y_root = find(y)
uf[x_root] = y_root
count -= 1
def connected(self, x, y): # 判断两个节点是否联通
return find(x) == find(y)
def count(x): # 返回所有组的数目
return count
```
至此，动态连通性相关的Union-Find算法基本上就介绍完了，从容易想到的Quick-Find到相对复杂但是更加高效的Quick-Union，然后到对Quick-Union的几项改进，
让我们的算法的效率不断的提高。
这几种算法的时间复杂度如下所示：
对大规模数据进行处理，使用平方阶的算法是不合适的，比如简单直观的Quick-Find算法，通过发现问题的更多特点，找到合适的数据结构，然后有针对性的进行改进，
得到了Quick-Union算法及其多种改进算法，最终使得算法的复杂度降低到了近乎线性复杂度。
如果需要的功能不仅仅是检测两个节点是否连通，还需要在连通时得到具体的路径，那么就需要用到别的算法了，比如DFS或者BFS。
并查集的应用，可以参考另外一篇文章[并查集应用举例](https://blog.csdn.net/dm_vincent/article/details/7769159)
## References
1. [并查集(Union-Find)算法介绍](https://blog.csdn.net/dm_vincent/article/details/7655764)
================================================
FILE: docs/Algorithm/DataStructure/Summarization/二叉树的一些操作.md
================================================
### 1. 二叉搜索树（BSTree）的概念
二叉搜索树又被称为二叉排序树，那么它本身也是一棵二叉树，那么满足以下性质的二叉树就是二叉搜索树，如图：
- 若左子树不为空，则左子树上所有节点的值都小于根节点的值；
- 若它的右子树不为空，则它的右子树上所有节点的值都大于根节点的值；
- 它的左右子树也要分别是二叉搜索树。
### 2. 二叉搜索树的插入
- 如果插入值已经存在，则不插，return False
- 如果not root，则返回TreeNode(val)
- 根据与root.val的比较，在左右子树中进行递归操作
代码中保证插入的值不存在，也是[leetcode第701题](https://github.com/apachecn/LeetCode/blob/master/docs/Leetcode_Solutions/701._Insert_into_a_Binary_Search_Tree.md)中所gurantee的
```python
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution(object):
def insertIntoBST(self, root, val):
"""
:type root: TreeNode
:type val: int
:rtype: TreeNode
"""
if not root:
return TreeNode(val)
if val < root.val:
root.left = self.insertIntoBST(root.left, val)
if val > root.val:
root.right = self.insertIntoBST(root.right, val)
return root
```
### 3. 二叉搜索树的搜索
* 搜索节点
```java
public TreeNode search(int key) {
TreeNode pNode = root;
while (pNode != null) {
if (key == pNode.key) {
return pNode;
} else if (key > pNode.key) {
pNode = pNode.rchild;
} else if (key < pNode.key) {
pNode = pNode.lchild;
}
}
return null;// 如果没有搜索到结果那么就只能返回空值了
}
```
* 获取最小节点
```java
public TreeNode minElemNode(TreeNode node) throws Exception {
if (node == null) {
throw new Exception("此树为空树！");
}
TreeNode pNode = node;
while (pNode.lchild != null) {
pNode = pNode.lchild;
}
return pNode;
}
```
* 获取最大节点
```java
public TreeNode maxElemNode(TreeNode node) throws Exception {
if (node == null) {
throw new Exception("此树为空树！");
}
TreeNode pNode = node;
while (pNode.rchild != null) {
pNode = pNode.rchild;
}
return pNode;
}
```
* 获取给定节点在中序遍历下的后续第一个节点（即找到该节点的右子树中的最左孩子）
```java
public TreeNode successor(TreeNode node) throws Exception {
if (node == null) {
throw new Exception("此树为空树！");
}
// 分两种情况考虑，此节点是否有右子树
// 当这个节点有右子树的情况下，那么右子树的最小关键字节点就是这个节点的后续节点
if (node.rchild != null) {
return minElemNode(node.rchild);
}
// 当这个节点没有右子树的情况下,即 node.rchild == null
// 如果这个节点是它父节点的左子树的话，那么就说明这个父节点就是后续节点了
TreeNode parentNode = node.parent;
while (parentNode != null && parentNode.rchild == node) {
node = parentNode;
parentNode = parentNode.parent;
}
return parentNode;
}
```
* 获取给定节点在中序遍历下的前趋结点
```java
public TreeNode precessor(TreeNode node) throws Exception {
// 查找前趋节点也是分两种情况考虑
// 如果这个节点存在左子树，那么这个左子树的最大关键字就是这个节点的前趋节点
if (node.lchild != null) {
return maxElemNode(node.lchild);
}
// 如果这个节点不存在左子树，那么这个节点的父节点
TreeNode parentNode = node.parent;
while (parentNode != null && parentNode.lchild == node) {
node = parentNode;
parentNode = parentNode.lchild;
}
return parentNode;
}
```
### 4. 二叉搜索树的删除
- 要删除的节点不存在，return False
- 要删除的节点没有子节点，直接删
- 要删除的节点只有一个子节点（即只有一个左子节点或者一个右子节点），让被删除节点的父亲节点指向其子节点即可
- 要删除的节点target有两个子节点（即左右均存在），则首先找到该节点的右子树中的最左孩子（也就是右子树中序遍历的第一个节点，分两种情况），
然后将两者互换，再删除target即可
```java
// 从二叉树当中删除指定的节点
public void delete(int key) throws Exception {
TreeNode pNode = search(key);
if (pNode == null) {
throw new Exception("此树中不存在要删除的这个节点！");
}
delete(pNode);
}
private void delete(TreeNode pNode) throws Exception {
// 第一种情况:删除没有子节点的节点
if (pNode.lchild == null && pNode.rchild == null) {
if (pNode == root) {// 如果是根节点，那么就删除整棵树
root = null;
} else if (pNode == pNode.parent.lchild) {
// 如果这个节点是父节点的左节点，则将父节点的左节点设为空
pNode.parent.lchild = null;
} else if (pNode == pNode.parent.rchild) {
// 如果这个节点是父节点的右节点，则将父节点的右节点设为空
pNode.parent.rchild = null;
}
}
// 第二种情况： （删除有一个子节点的节点）
// 如果要删除的节点只有右节点
if (pNode.lchild == null && pNode.rchild != null) {
if (pNode == root) {
root = pNode.rchild;
} else if (pNode == pNode.parent.lchild) {
pNode.parent.lchild = pNode.rchild;
pNode.rchild.parent = pNode.parent;
} else if (pNode == pNode.parent.rchild) {
pNode.parent.rchild = pNode.rchild;
pNode.rchild.parent = pNode.parent;
}
}
// 如果要删除的节点只有左节点
if (pNode.lchild != null && pNode.rchild == null) {
if (pNode == root) {
root = pNode.lchild;
} else if (pNode == pNode.parent.lchild) {
pNode.parent.lchild = pNode.lchild;
pNode.lchild.parent = pNode.parent;
} else if (pNode == pNode.parent.rchild) {
pNode.parent.rchild = pNode.lchild;
pNode.lchild.parent = pNode.parent;
}
}
// 第三种情况： （删除有两个子节点的节点，即左右子节点都非空）
// 方法是用要删除的节点的后续节点代替要删除的节点，并且删除后续节点（删除后续节点的时候需要递归操作）
// 解析：这里要用到的最多也就会发生两次，即后续节点不会再继续递归的删除下一个后续节点了，
// 因为，要删除的节点的后续节点肯定是:要删除的那个节点的右子树的最小关键字，而这个最小关键字肯定不会有左节点;
// 所以，在删除后续节点的时候肯定不会用到（两个节点都非空的判断 ），如有有子节点，肯定就是有一个右节点。
if (pNode.lchild != null && pNode.rchild != null) {
// 先找出后续节点
TreeNode successorNode = successor(pNode);
if (pNode == root) {
root.key = successorNode.key;
} else {
pNode.key = successorNode.key;// 赋值，将后续节点的值赋给要删除的那个节点
}
delete(successorNode);// 递归的删除后续节点
}
}
```
### 5. 二叉搜索树的遍历
前序遍历：[leetcode第144题](https://github.com/apachecn/LeetCode/blob/master/docs/Leetcode_Solutions/144._binary_tree_preorder_traversal.md)
中序遍历：[leetcode第94题](https://github.com/apachecn/LeetCode/blob/master/docs/Leetcode_Solutions/094._binary_tree_inorder_traversal.md)
后序遍历：[leetcode第145题](https://github.com/apachecn/LeetCode/blob/master/docs/Leetcode_Solutions/145._binary_tree_postorder_traversal.md)
层次遍历：[leetcode第102题](https://github.com/apachecn/LeetCode/blob/master/docs/Leetcode_Solutions/102._binary_tree_level_order_traversal.md)
## References
[数据结构与算法之二叉搜索树插入、查询与删除](https://blog.csdn.net/chenliguan/article/details/52956546)
[二叉搜索树的插入与删除图解](http://www.cnblogs.com/MrListening/p/5782752.html)
[二叉树算法删除代码实现](https://blog.csdn.net/tayanxunhua/article/details/11100113)
[二叉树的Java实现及特点总结](http://www.cnblogs.com/lzq198754/p/5857597.html)
================================================
FILE: docs/Algorithm/DataStructure/Summarization/位运算.md
================================================
### 位运算
位运算包括: 加 减 乘 取反 and 异或
- 0110 + 0110 = 0110 * 2 ，也就是0110左移1位
- 0011 * 0100 0100 = 4， 一个数乘以 2^n 即是将这个数左移n
- a ^(~a) = 0
- x & (~0 << n ) 这样来看，0取反全部为1，然后将其右移n位，后面的全是0，x & (~0 <>：右移
Bit Facts and Tricks
```
x ^ 0s = x x & 0s = 0 x | 0s = x
x ^ 1s = ~x x & 1s = x x | 1s = 1s
x ^ x = 0 x & x = x x | x = x
```
================================================
FILE: docs/Algorithm/DataStructure/Summarization/全排列算法.md
================================================
### 全排列算法
#### 46. Permutations
Given a collection of distinct numbers, return all possible permutations.
For example,
[1,2,3] have the following permutations:
[
[1,2,3],
[1,3,2],
[2,1,3],
[2,3,1],
[3,1,2],
[3,2,1]
]
#####从空开始加
先跳离开这道题，来看类似的'ABC',我们要求它的全排列
```
def recPermute(sofar, rest):
if rest == '':
print sofar
else:
for i in range(len(rest)):
nxt = sofar + rest[i]
remaining = rest[:i] + rest[i+1:]
recPermute(nxt, remaining)
// "wrapper" function
def listPermute(s):
recPermute('',s)
```
会正确输出`ABC ACB BAC BCA CAB CBA`,题目依靠的是每次我们从余下的字母中选一个，如果画图则会是这样:
```
A B C
B C A C A B
C B C A B A
```
时间复杂度应该是O(n!)
- n choose 1
- n-1 choose 1
- ...
#####另一种市面上常见思路是交换：
思路是这样的,同样看上面的图:
- n个元素的全排列 = （n-1）个元素的全排列 + 另一个元素作为前缀
- 如果只有一个元素，那么这个元素本身就是它的全排列
- 不断将每个元素放作第一个元素，然后将这个元素作为前缀，并将其余元素继续全排列，等到出口，出口出去后还需要还原数组
这个用数组来测试更容易写代码和直观：
```
def recPermute(nums,begin):
n = len(nums)
if begin == n:
print nums,
for i in range(begin,n):
nums[begin], nums[i] = nums[i],nums[begin]
recPermute(nums,begin+1)
nums[begin],nums[i] = nums[i],nums[begin]
recPermute(['A','B','C'],0)
```
这样的写法更容易理解：
```python
class Solution:
# @param num, a list of integer
# @return a list of lists of integers
def permute(self, num):
if len(num) == 0: return []
if len(num) == 1: return [num]
res = []
for i in range(len(num)):
x = num[i]
xs = num[:i] + num[i+1:]
for j in self.permute(xs):
res.append([x] + j)
return res
```
每次用一个没有用过的头元素，然后加上全排列产生的结果.
如果分析复杂度，应该也是O(n!)
#### 47. Permutations II
最简单的想法：
- 排序
- 如果碰到重复的就继续处理下一个
```
class Solution(object):
def permuteUnique(self, nums):
"""
:type nums: List[int]
:rtype: List[List[int]]
"""
if len(nums) == 0: return []
if len(nums) == 1: return [nums]
res = []
nums.sort()
for i in range(len(nums)):
if i > 0 and nums[i] == nums[i-1]: continue
for j in self.permuteUnique(nums[:i] + nums[i+1:]):
res.append([nums[i]] + j)
return res
```
#### 31. Next Permutation
实际上这个题目也就是Generation in lexicographic order,
wikipedia 和 [这里](https://www.nayuki.io/page/next-lexicographical-permutation-algorithm) 有很好，很精妙的算法，也有点two pointer的意思
```
1. Find the highest index i such that s[i] < s[i+1]. If no such index exists, the permutation is the last permutation.
2. Find the highest index j > i such that s[j] > s[i]. Such a j must exist, since i+1 is such an index.
3. Swap s[i] with s[j].
4. Reverse the order of all of the elements after index i till the last element.
```
看例子：
125430
- 从末尾开始，找到decreasing subsequence，5430，因为来调5330无论怎么调，都不可能有比它更小的，数也被自然的分成两部分(1,2) 和 （5，4，3，0)
- 下一步是找这个sequence里面第一个比前面部分，比2大的，3，也很容易理解，因为下一个必定是(1,3)打头
- 交换 3和2 ，变成 (1,3,5,4,2,0),再把后面的部分reverse，得到后面部分可得到的最小的
这个时候，得到下一个sequence 130245
```
class Solution(object):
def nextPermutation(self, nums):
"""
:type nums: List[int]
:rtype: void Do not return anything, modify nums in-place instead.
"""
m, n = 0, 0
for i in range(len(nums) - 2, 0 , -1):
if nums[i] < nums[i+1]:
m = i
break
for i in range(len(nums) - 1, 0 , -1):
if nums[i] > nums[m]:
n = i
break
if m < n :
nums[m], nums[n] = nums[n], nums[m]
nums[m+1:] = nums[len(nums):m:-1]
else:
nums = nums.reverse()
```
所以可以用这个next permutation来解46/47也可以，然后我兴奋了一下，这个算法很快的！然后我又冷静了，因为permutation的个数是O(n!)个啊|||,所以也不可能有啥大的提升吧
================================================
FILE: docs/Algorithm/DataStructure/Summarization/八排序.md
================================================
## 前言
八大排序，三大查找是《数据结构》当中非常基础的知识点，在这里为了复习顺带总结了一下常见的八种排序算法。
常见的八大排序算法，他们之间关系如下：
他们的性能比较：
### 直接插入排序 (Insertion sort)
直接插入排序的核心思想就是：将数组中的所有元素依次跟前面已经排好的元素相比较，如果选择的元素比已排序的元素小，则交换，直到全部元素都比较过。
因此，从上面的描述中我们可以发现，直接插入排序可以用两个循环完成：
1. 第一层循环：遍历待比较的所有数组元素
2. 第二层循环：将本轮选择的元素(selected)与已经排好序的元素(ordered)相比较。
- 如果```selected > ordered```，那么将二者交换
```python
#直接插入排序
def insert_sort(L):
#遍历数组中的所有元素，其中0号索引元素默认已排序，因此从1开始
for x in range(1,len(L)):
#将该元素与已排序好的前序数组依次比较，如果该元素小，则交换
#range(x-1,-1,-1):从x-1倒序循环到0
for i in range(x-1,-1,-1):
#判断：如果符合条件则交换
if L[i] > L[i+1]:
L[i], L[i+1] = L[i+1], L[i]
```
### 希尔排序 (Shell sort)
希尔排序的算法思想：将待排序数组按照步长gap进行分组，然后将每组的元素利用直接插入排序的方法进行排序；每次将gap折半减小，循环上述操作；当gap=1时，利用直接插入，完成排序。
同样的：从上面的描述中我们可以发现：希尔排序的总体实现应该由三个循环完成：
1. 第一层循环：将gap依次折半，对序列进行分组，直到gap=1
2. 第二、三层循环：也即直接插入排序所需要的两次循环。具体描述见上。
```python
#希尔排序
def insert_shell(L):
#初始化gap值，此处利用序列长度的一半为其赋值
gap = int(len(L)/2)
#第一层循环：依次改变gap值对列表进行分组
while (gap >= 1):
#下面：利用直接插入排序的思想对分组数据进行排序
#range(gap,len(L)):从gap开始
for x in range(gap,len(L)):
#range(x-gap,-1,-gap):从x-gap开始与选定元素开始倒序比较，每个比较元素之间间隔gap
for i in range(x-gap,-1,-gap):
#如果该组当中两个元素满足交换条件，则进行交换
if L[i] > L[i+gap]:
L[i], L[i+gap] = L[i+gap], L[i]
#while循环条件折半
gap = int((gap/2))
```
### 简单选择排序 (Selection sort)
简单选择排序的基本思想：比较+交换。
1. 从待排序序列中，找到关键字最小的元素；
2. 如果最小元素不是待排序序列的第一个元素，将其和第一个元素互换；
3. 从余下的 N - 1 个元素中，找出关键字最小的元素，重复(1)、(2)步，直到排序结束。
因此我们可以发现，简单选择排序也是通过两层循环实现。
- 第一层循环：依次遍历序列当中的每一个元素
- 第二层循环：将遍历得到的当前元素依次与余下的元素进行比较，符合最小元素的条件，则交换。
```python
# 简单选择排序
def select_sort(L):
#依次遍历序列中的每一个元素
for x in range(0,len(L)):
#将当前位置的元素定义此轮循环当中的最小值
minimum = L[x]
#将该元素与剩下的元素依次比较寻找最小元素
for i in range(x+1,len(L)):
if L[i] < minimum:
L[i], minimum = minimum, L[i]
#将比较后得到的真正的最小值赋值给当前位置
L[x] = minimum
```
### 堆排序 (Heap sort)
#### 堆的概念
堆：本质是一种数组对象。特别重要的一点性质：任意的叶子节点小于（或大于）它所有的父节点。对此，又分为大顶堆和小顶堆，大顶堆要求节点的元素都要大于其孩子，小顶堆要求节点元素都小于其左右孩子，两者对左右孩子的大小关系不做任何要求。
利用堆排序，就是基于大顶堆或者小顶堆的一种排序方法。下面，我们通过大顶堆来实现。
基本思想：
堆排序可以按照以下步骤来完成：
1. 首先将序列构建称为大顶堆；
（这样满足了大顶堆那条性质：位于根节点的元素一定是当前序列的最大值）
2. 取出当前大顶堆的根节点，将其与序列末尾元素进行交换；
（此时：序列末尾的元素为已排序的最大值；由于交换了元素，当前位于根节点的堆并不一定满足大顶堆的性质）
3. 对交换后的n-1个序列元素进行调整，使其满足大顶堆的性质；
4. 重复2.3步骤，直至堆中只有1个元素为止
```python
#-------------------------堆排序--------------------------------
#**********获取左右叶子节点**********
def LEFT(i):
return 2*i + 1
def RIGHT(i):
return 2*i + 2
#********** 调整大顶堆 **********
#L:待调整序列 length: 序列长度 i:需要调整的结点
def adjust_max_heap(L, length, i):
#定义一个int值保存当前序列最大值的下标
largest = i
#获得序列左右叶子节点的下标
left, right = LEFT(i), RIGHT(i)
#当左叶子节点的下标小于序列长度 并且 左叶子节点的值大于父节点时，将左叶子节点的下标赋值给largest
if (left < length) and (L[left] > L[i]):
largest = left
#当右叶子节点的下标小于序列长度 并且 右叶子节点的值大于父节点时，将右叶子节点的下标值赋值给largest
if (right < length) and (L[right] > L[largest]):
largest = right
#如果largest不等于i 说明当前的父节点不是最大值，需要交换值
if (largest != i):
L[i], L[largest] = L[largest], L[i]
# 执行递归操作：两个任务：1 寻找最大值的下标；2.最大值与父节点交换
adjust_max_heap(L, length, largest)
#********** 建立大顶堆 **********
def build_max_heap(L):
length = len(L)
for x in range(int((length-1)/2), -1, -1):
adjust_max_heap(L, length, x)
#********** 堆排序 **********
def heap_sort(L):
#先建立大顶堆，保证最大值位于根节点；并且父节点的值大于叶子结点
build_max_heap(L)
#i：当前堆中序列的长度.初始化为序列的长度
i = len(L)
#执行循环：1. 每次取出堆顶元素置于序列的最后(len-1,len-2,len-3...)
# 2. 调整堆，使其继续满足大顶堆的性质，注意实时修改堆中序列的长度
while (i > 0):
L[i-1], L[0] = L[0], L[i-1]
#堆中序列长度减1
i -= 1
#调整大顶堆
adjust_max_heap(L, i, 0)
```
### 冒泡排序 (Bubble sort)
冒泡排序思路比较简单：
1. 将序列当中的左右元素，依次比较，保证右边的元素始终大于左边的元素；
（ 第一轮结束后，序列最后一个元素一定是当前序列的最大值；）
2. 对序列当中剩下的n-1个元素再次执行步骤1。
3. 对于长度为n的序列，一共需要执行n-1轮比较
（利用while循环可以减少执行次数）
```python
#冒泡排序
def bubble_sort(L):
length = len(L)
#序列长度为length，需要执行length-1轮交换
for x in range(1, length):
#对于每一轮交换，都将序列当中的左右元素进行比较
#每轮交换当中，由于序列最后的元素一定是最大的，因此每轮循环到序列未排序的位置即可
for i in range(0, length-x):
if L[i] > L[i+1]:
L[i], L[i+1] = L[i+1], L[i]
```
### 快速排序 (Quick sort)
快速排序的基本思想：挖坑填数+分治法
1. 从序列当中选择一个基准数(pivot)
在这里我们选择序列当中第一个数作为基准数
2. 将序列当中的所有数依次遍历，比基准数大的位于其右侧，比基准数小的位于其左侧
3. 重复步骤1.2，直到所有子集当中只有一个元素为止。
用伪代码描述如下：
- i =L; j = R; 将基准数挖出形成第一个坑a[i]。
- j--由后向前找比它小的数，找到后挖出此数填前一个坑a[i]中。
- i++由前向后找比它大的数，找到后也挖出此数填到前一个坑a[j]中。
- 再重复执行2，3二步，直到i==j，将基准数填入a[i]中
```python
#快速排序
#L：待排序的序列；start排序的开始index,end序列末尾的index
#对于长度为length的序列：start = 0;end = length-1
def quick_sort(L, start, end):
if start < end:
i, j, pivot = start, end, L[start]
while i < j:
#从右开始向左寻找第一个小于pivot的值
while (i < j) and (L[j] >= pivot):
j -= 1
#将小于pivot的值移到左边
if (i < j):
L[i] = L[j]
i += 1
#从左开始向右寻找第一个大于pivot的值
while (i < j) and (L[i] <= pivot):
i += 1
#将大于pivot的值移到右边
if (i < j):
L[j] = L[i]
j -= 1
#循环结束后，说明 i=j，此时左边的值全都小于pivot,右边的值全都大于pivot
#pivot的位置移动正确，那么此时只需对左右两侧的序列调用此函数进一步排序即可
#递归调用函数：依次对左侧序列：从0 ~ i-1//右侧序列：从i+1 ~ end
L[i] = pivot
#左侧序列继续排序
quick_sort(L, start, i-1)
#右侧序列继续排序
quick_sort(L, i+1, end)
```
### 归并排序 (Merge sort)
1. 归并排序是建立在归并操作上的一种有效的排序算法，该算法是采用分治法的一个典型的应用。它的基本操作是：将已有的子序列合并，达到完全有序的序列；即先使每个子序列有序，再使子序列段间有序。
2. 归并排序其实要做两件事：
- 分解----将序列每次折半拆分
- 合并----将划分后的序列段两两排序合并
因此，归并排序实际上就是两个操作，拆分+合并
3. 如何合并？
- L[first...mid]为第一段，L[mid+1...last]为第二段，并且两端已经有序，现在我们要将两端合成达到L[first...last]并且也有序。
- 首先依次从第一段与第二段中取出元素比较，将较小的元素赋值给temp[]
- 重复执行上一步，当某一段赋值结束，则将另一段剩下的元素赋值给temp[]
- 此时将temp[]中的元素复制给L[]，则得到的L[first...last]有序
4. 如何分解？
- 在这里，我们采用递归的方法，首先将待排序列分成A,B两组；
- 然后重复对A、B序列分组；
- 直到分组后组内只有一个元素，此时我们认为组内所有元素有序，则分组结束。
```python
# 归并排序
#这是合并的函数
# 将序列L[first...mid]与序列L[mid+1...last]进行合并
def mergearray(L, first, mid, last, temp):
#对i,j,k分别进行赋值
i, j, k = first, mid+1, 0
#当左右两边都有数时进行比较，取较小的数
while (i <= mid) and (j <= last):
if L[i] <= L[j]:
temp[k] = L[i]
i += 1
k += 1
else:
temp[k] = L[j]
j += 1
k += 1
#如果左边序列还有数
while (i <= mid):
temp[k] = L[i]
i += 1
k += 1
#如果右边序列还有数
while (j <= last):
temp[k] = L[j]
j += 1
k += 1
#将temp当中该段有序元素赋值给L待排序列使之部分有序
for x in range(0, k):
L[first+x] = temp[x]
# 这是分组的函数
def merge_sort(L, first, last, temp):
if first < last:
mid = int(((first + last) / 2))
#使左边序列有序
merge_sort(L, first, mid, temp)
#使右边序列有序
merge_sort(L, mid+1, last, temp)
#将两个有序序列合并
mergearray(L, first, mid, last, temp)
# 归并排序的函数
def merge_sort_array(L):
#声明一个长度为len(L)的空列表
temp = len(L)*[None]
#调用归并排序
merge_sort(L, 0, len(L)-1, temp)
```
### 基数排序 (Radix sort)
1. 基数排序：通过序列中各个元素的值，对排序的N个元素进行若干趟的“分配”与“收集”来实现排序。
- 分配：我们将L[i]中的元素取出，首先确定其个位上的数字，根据该数字分配到与之序号相同的桶中
- 收集：当序列中所有的元素都分配到对应的桶中，再按照顺序依次将桶中的元素收集形成新的一个待排序列L[ ]
- 对新形成的序列L[]重复执行分配和收集元素中的十位、百位...直到分配完该序列中的最高位，则排序结束
2. 根据上述“基数排序”的展示，我们可以清楚的看到整个实现的过程
```python
#************************基数排序****************************
#确定排序的次数
#排序的顺序跟序列中最大数的位数相关
def radix_sort_nums(L):
maxNum = L[0]
#寻找序列中的最大数
for x in L:
if maxNum < x:
maxNum = x
#确定序列中的最大元素的位数
times = 0
while (maxNum > 0):
maxNum = int((maxNum/10))
times += 1
return times
#找到num从低到高第pos位的数据
def get_num_pos(num, pos):
return (int((num/(10**(pos-1))))) % 10
#基数排序
def radix_sort(L):
count = 10 * [None] #存放各个桶的数据统计个数
bucket = len(L) * [None] #暂时存放排序结果
#从低位到高位依次执行循环
for pos in range(1, radix_sort_nums(L)+1):
#置空各个桶的数据统计
for x in range(0, 10):
count[x] = 0
#统计当前该位(个位，十位，百位....)的元素数目
for x in range(0, len(L)):
#统计各个桶将要装进去的元素个数
j = get_num_pos(int(L[x]), pos)
count[j] += 1
#count[i]表示第i个桶的右边界索引
for x in range(1,10):
count[x] += count[x-1]
#将数据依次装入桶中
for x in range(len(L)-1, -1, -1):
#求出元素第K位的数字
j = get_num_pos(L[x], pos)
#放入对应的桶中，count[j]-1是第j个桶的右边界索引
bucket[count[j]-1] = L[x]
#对应桶的装入数据索引-1
count[j] -= 1
# 将已分配好的桶中数据再倒出来，此时已是对应当前位数有序的表
for x in range(0, len(L)):
L[x] = bucket[x]
```
## 运行时间实测
10w数据
```
直接插入排序:1233.581131
希尔排序:1409.8012320000003
简单选择排序:466.66974500000015
堆排序:1.2036720000000969
冒泡排序:751.274449
#****************************************************
快速排序:1.0000003385357559e-06
#快速排序有误：实际上并未执行
#RecursionError: maximum recursion depth exceeded in comparison
#****************************************************
归并排序:0.8262230000000272
基数排序:1.1162899999999354
```
从运行结果上来看，堆排序、归并排序、基数排序真的快。
对于快速排序迭代深度超过的问题，可以将考虑将快排通过非递归的方式进行实现。
## Resources
1. [算法导论》笔记汇总](http://mindlee.com/2011/08/21/study-notes-directory/)
2. [八大排序算法的 Python 实现](http://python.jobbole.com/82270/)
3. [数据结构常见的八大排序算法（详细整理）](https://www.jianshu.com/p/7d037c332a9d)
================================================
FILE: docs/Algorithm/DataStructure/Summarization/子集合问题.md
================================================
###子集合问题
####78. Subsets
子集合是全排列的好朋友,也是combination组合的好朋友，排列·组合·子集，他们三个都是好朋友.
#####从空开始加
同样先来看'ABC'
```
def recsubsets(sofar, rest):
if rest == '':
print sofar,
else:
recsubsets(sofar, rest[1:])
recsubsets(sofar + rest[0], rest[1:])
def listsubsets(s):
recsubsets('',s)
listsubsets('ABC')
```
##### 市面流行思路
市面上流行的思路：
- [[],[1]] 是 [1] 的子集合
- [[],[1],[2],[1,2]] 是 [1,2] 的子集合，实际上就是1的子集合们加了一个2
所以用python写起来也很简单/精美
```
def subsets(nums):
"""
:type nums: List[int]
:rtype: List[List[int]]
"""
results = [[]]
for num in nums:
results.extend([result + [num] for result in results])
return results
```
我在这里犯过错，所以这一句
`results.extend([result + [num] for result in results])` 实际上等于：
```
tmp = []
for result in results:
tmp.append(result + [num])
results.extend(tmp)
```
#### 90. Subsets II
要去重了,比如如果有 [1,2,2]，那么解答为：
[
[2],
[1],
[1,2,2],
[2,2],
[1,2],
[]
]
现在来观察规律，与之前有不同之处是我们需要一个位置来mark，因为不再需要往之前出现过的地方再加了，看这个:
```
[[],[1]] 是 [1] 的子集合
[[],[1],[2],[1,2]] 是 [1,2] 的子集合，实际上就是1的子集合们加了一个2
新来的2不能再从头开始加了，它需要从[ .., [2],[1,2] ]加 才是合理的
```
所以看到非常精妙的代码
```
def subsets(nums):
"""
:type nums: List[int]
:rtype: List[List[int]]
"""
nums.sort()
result = [[]]
temp_size = 0
for i in range(len(nums)):
start = temp_size if i >= 1 and nums[i] == nums[i-1] else 0
temp_size = len(result)
#print start,temp_size,result
for j in range(start, temp_size):
result.append(result[j] + [nums[i]])
print result
subsets([1,2,2])
```
这里这个start是来记录了之前一次数组的长度，temp_size记住目前数组的长度，然后用这个来达到去重的目的，非常聪明
================================================
FILE: docs/Algorithm/DataStructure/Summarization/总结.md
================================================
# 1
```solution```下自定义函数```func(self, fargs, *args, **kwargs)```, 调用时使用```self.func()```的格式
# 2
```not fargs``` 和 ```fargs == None```不一样，前者```fargs```可能为[], '', 0, etc
# 3
递归问题
Any problem can be solved using dp. Solving using a greedy strategy is harder though, since you need to prove that greedy will work for that problem. There are some tell-tale signs of a problem where greedy may be applicable, but isn’t immediately apparent. Example:
- Choice of an element depends only on its immediate neighbours (wiggle sort).
- Answer is monotonically non-decreasing or non-increasing (sorting). This is also applicable for LIS for example.
- Anything that requires lexicographically largest or smallest of something.
- Anything where processing the input in sorted order will help.
- Anything where processing the input in forward or reverse (as given) will help.
- Anything which requires you to track the minimum or maximum of something (think of sliding window problems).
There’s matroid theory which deal with greedy algorithms, but I don’t really understand it. If someone does, I’ll be super grateful to them to explain it to me in simple language!
In general, try to see if for a problem, the solution doesn’t depend on a lot of history about the solution itself, but the next part of the solution is somewhat independent from the rest of the solution. These are all indicative of the fact that a greedy strategy could be applicable.
# 4
[Counter.elements()](https://docs.python.org/2/library/collections.html)
# 5
测试案例写法
```python
import unittest
class Solution(object):
def isMatch(self, s, p):
"""
:type s: str
:type p: str
:rtype: bool
"""
m, n = len(s), len(p)
dp = [ [0 for i in range(n+1)] for j in range(m+1)]
dp[0][0] = 1
# init the first line
for i in range(2,n+1):
if p[i-1] == '*':
dp[0][i] = dp[0][i-2]
for i in range(1,m+1):
for j in range(1,n+1):
if p[j-1] == '*':
if p[j-2] != s[i-1] and p[j-2] != '.':
dp[i][j] = dp[i][j-2]
elif p[j-2] == s[i-1] or p[j-2] == '.':
dp[i][j] = dp[i-1][j] or dp[i][j-2]
elif s[i-1] == p[j-1] or p[j-1] == '.':
dp[i][j] = dp[i-1][j-1]
return dp[m][n] == 1
class TestSolution(unittest.TestCase):
def test_none_0(self):
s = ""
p = ""
self.assertTrue(Solution().isMatch(s, p))
def test_none_1(self):
s = ""
p = "a"
self.assertFalse(Solution().isMatch(s, p))
def test_no_symbol_equal(self):
s = "abcd"
p = "abcd"
self.assertTrue(Solution().isMatch(s, p))
def test_no_symbol_not_equal_0(self):
s = "abcd"
p = "efgh"
self.assertFalse(Solution().isMatch(s, p))
def test_no_symbol_not_equal_1(self):
s = "ab"
p = "abb"
self.assertFalse(Solution().isMatch(s, p))
def test_symbol_0(self):
s = ""
p = "a*"
self.assertTrue(Solution().isMatch(s, p))
def test_symbol_1(self):
s = "a"
p = "ab*"
self.assertTrue(Solution().isMatch(s, p))
def test_symbol_2(self):
# E.g.
# s a b b
# p 1 0 0 0
# a 0 1 0 0
# b 0 0 1 0
# * 0 1 1 1
s = "abb"
p = "ab*"
self.assertTrue(Solution().isMatch(s, p))
if __name__ == "__main__":
unittest.main()
输出：
........
Ran 8 tests in 0.001s
OK
```
================================================
FILE: docs/Algorithm/DataStructure/Summarization/组合问题.md
================================================
### 组合问题
#### 77.Combinations
##### 会超时的recursion
```
class Solution(object):
def combine(self, n, k):
"""
:type n: int
:type k: int
:rtype: List[List[int]]
"""
ans = []
self.dfs(n, k, 1, [], ans)
return ans
def dfs(self, n, k ,start, lst, ans):
if k == 0 :
ans.append(lst)
return
for i in range(start, n+1):
self.dfs(n, k - 1, i + 1,lst +[i], ans)
```
理解方式
```
1 2 3
12 13 14 23 24 34
```
可以参照这里
![ApacheCN - 面试求职群[724187166]](http://data.apachecn.org/img/logo/ApacheCN-group.png "ApacheCN - 面试求职群[724187166]")
**
## 关于刷题
```
刷题可能是目前来说：最有用，也是最没用的东西
有用只是指：面试最快捷的一种方式
最没用是指：基本上在工作中用不上
简单来说；形式主义为主，技术提升为辅，目的就是为了驯服和奴役思维
会刷题和当年会考试没有本质区别
我并没有觉得这个是一件值得骄傲的事情
相反，这恰恰是普通人没用选择的事情
我吐槽一下：
工作5年，面试还要刷题，写排序，聊一些优化细节和技巧，感觉比较惭愧
你能写出一个高性能的代码吗？（例如：处理10G，做排序）
你反问：为什么不用GPU和Spark.
他说：。。（他无语）
我觉得大家可能都很无语吧，最无语的应该是前沿的技术吧
很多时候，基本上你遇到问题百度一下答案就出来了
工作中你可能没遇到，但是面试你必须背下来，不管会不会
一个人的好坏，我觉得是在人的性格和搜索能力。
但是在各大公司的HR和不入流的面试官面前：高学历和强刷题
技术高低和花的时间有关系，而工作是否录取和高学历和强刷题有关
```
---
当然我们得先战胜市场，才能改变市场！
下面正式开始我们刷题教程入门 -- 你准备好了吗？
## 数据结构 - 排序
* 二分查找
* 冒泡排序
* 插入排序
* 选择排序
* 快速排序
* 希尔排序
* 归并排序
* 基数排序
实战入口:
## [算法刷题](https://github.com/apachecn/Interview/tree/master/docs/Algorithm/README.md)
1. [Leetcode](/docs/Algorithm/Leetcode)
- [Python](/docs/Algorithm/Leetcode/Python)
- [Java](/docs/Algorithm/Leetcode/Java)
- [JavaScript](/docs/Algorithm/Leetcode/JavaScript)
- [C++](/docs/Algorithm/Leetcode/C++)
- [ipynb](/docs/Algorithm/Leetcode/ipynb)
- [GO](https://github.com/aQuaYi/LeetCode-in-Go)
- [Golang](https://github.com/kylesliu/awesome-golang-leetcode)
2. [剑指 Offer](/docs/Algorithm/剑指offer)
- [Python](/docs/Algorithm/剑指offer/Python)
- [Scala](/docs/Algorithm/剑指offer/Scala)
- [Java](/docs/Algorithm/剑指offer/Java)
- [JavaScript](/docs/Algorithm/剑指offer/JavaScript)
- [C++](/docs/Algorithm/剑指offer/C++)
3. [数据结构](/docs/Algorithm/DataStructure)
- [Wikipedia: List of Algorithms](https://en.wikipedia.org/wiki/List_of_algorithms)
## 参与方式
> 提交PR基本要求（满足任意一种即可）
* 不一样的思路
* 优化时间复杂度和空间复杂度，或者解决题目的Follow up
* 有意义的简化代码
* 未提交过的题目
> **案例模版**
[模版 md: 001. Two Sum 两数之和](docs/Algorithm/Leetcode/Python/001._two_sum.md)
[模版页面效果: 001. Two Sum 两数之和](https://interview.apachecn.org/docs/Algorithm/Leetcode/Python/001._two_sum.html)
## 推荐 LeetCode 网站
- [KrisYu的Github](https://github.com/KrisYu/LeetCode-CLRS-Python)
- [kamyu104的Github](https://github.com/kamyu104/LeetCode)
- [数据结构与算法/leetcode/lintcode题解](https://algorithm.yuanbin.me/zh-hans/)
- [Leetcode 讨论区](https://discuss.leetcode.com/)
- [visualgo算法可视化网站](https://visualgo.net/en)
- [Data Structure Visualization](https://www.cs.usfca.edu/~galles/visualization/Algorithms.html)
- [我的算法学习之路 - Lucida](http://zh.lucida.me/blog/on-learning-algorithms/)
- [HiredInTech](https://www.hiredintech.com/) System Design 的总结特别适合入门
- [mitcc的Github](https://github.com/mitcc/AlgoSolutions)
- [小土刀的面试刷题笔记](http://wdxtub.com/interview/14520594642530.html)
- [nonstriater/Learn-Algorithms](https://github.com/nonstriater/Learn-Algorithms)
- [剑指 Offer 题解](https://github.com/gatieme/CodingInterviews)
- https://github.com/liuchuo/LeetCode
- https://github.com/anxiangSir/SwordforOffer
- https://www.nowcoder.com/ta/coding-interviews?page=1
- [【小姐姐】刷题博客](https://www.liuchuo.net/about)
- [公瑾的Github](https://github.com/yuzhoujr/leetcode)
- [shejie1993](https://shenjie1993.gitbooks.io/leetcode-python/content/096%20Unique%20Binary%20Search%20Trees.html)
- [编程之法：面试和算法心得](https://legacy.gitbook.com/book/wizardforcel/the-art-of-programming-by-july/details)
- [算法/NLP/深度学习/机器学习面试笔记](https://github.com/imhuay/Interview_Notes-Chinese)
================================================
FILE: docs/GitHub/README.md
================================================
# GitHub 入门须知
## 基本介绍
* git 是一个版本控制工具.
* Github 是一个基于 git 的社会化代码分享社区, 所谓 social coding.
*开源项目是可以免费托管到GitHub上面，在GitHub上你基本上可以找到任何你想要的代码。
## 基本用途
| 名称 | 描述 |
| - | - |
| 个人写作 | 个人笔记（支持 .md 格式）
Note: 写书，可以去 GitBook |
| 多人写作 | 1.适合内容协同和迭代
2.适合即将步入职场的人熟悉职场操作 |
| 搭建网站 | GitHub Pages 用于免费搭建个人网站，支持绑定个人域名 |
| 个人简历 | 你会发现，GitHub 是一个不错的求职敲门砖，可以理解为程序员的个人简历 |
| 开源项目 | 开源社区流行着一句话叫「不要重复发明轮子」，也就是站在巨人的肩膀上看得更远 |
## 下载地址
### 1.客户端安装(例如：Mac 三种方式都可行)
> (一 客户端操作: https://desktop.github.com/ 【不推荐，程序员建议命令行】
> (二 通过 homebrew 安装 git
* 1.安装homebrew
```
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
```
* 2.安装 git
```
brew install git
```
> (三 通过 Xcode 安装
* 直接从 AppStore 安装 Xcode, Xcode 集成了 Git.
* 不过默认没有安装, 你需要运行 Xcode, 选择菜单 Xcode -> Preferences.
* 在弹出窗口中找到 Downloads, 选择 Command Line Tools, 点 Install 就可以完成安装了.
### 2.创建 ssh key、 配置 git
> 1、设置 username 和 email（github 每次 commit 都会记录他们）
```
git config --global user.name "jiangzhonglian"
git config --global user.email "jiang-s@163.com"
```
> 2、通过终端命令创建 ssh key
```
ssh-keygen -t rsa -C "jiang-s@163.com"
```
`jiang-s@163.com` 是我的邮件名，回车会有以下输出
```
Last login: Sat Jan 6 14:12:16 on ttys000
jiangzl@jiangzhongliandeMacBook-Pro:~$ ssh-keygen -t rsa -C "jiang-s@163.com"
Generating public/private rsa key pair.
Enter file in which to save the key (/Users/jiangzl/.ssh/id_rsa):
/Users/jiangzl/.ssh/id_rsa already exists.
Overwrite (y/n)? n
jiangzl@jiangzhongliandeMacBook-Pro:~$
```
* 这里我原来已经创建过，这里我选 n.
* 没有创建过的就选择 y, 我们默认的一路回车就行（有特殊癖好: 可以确认路径和输入密码） .
* 成功的话会在 ~/ 下生成 .ssh 文件夹, 进去打开 id_rsa.pub, 复制里面的key.
* 终端查看 `.ssh/id_rsa.pub` 文件, 然后复制里面的key.
### 3.上传 ssh key 到 GitHub 服务器上面
> 1.注册 GitHub 帐号， 登录GitHub
> 2.点击 Settings, 添加ssh key
> 3.点击 New SSH key
> 4.链接验证
```
ssh -T git@github.com
```
终端输出结果
```
Last login: Sat Jan 6 14:42:55 on ttys000
jiangzl@jiangzhongliandeMacBook-Pro:~$ ssh -T git@github.com
Hi jiangzhonglian! You've successfully authenticated, but GitHub does not provide shell access.
jiangzl@jiangzhongliandeMacBook-Pro:~$
```
说明已经链接成功。
## 基本命令
此图片链接为 bilibili 视频地址: (视频图片下面为文本操作指南)
全局概况图(看不懂就多看几遍)
> 一) fork apachecn/kaggle 项目
> 二) jiangzhonglian/kaggle 第一次初始化
可以使用 vscode 进行可视化操作
```
clone 自己的 repo 仓库 （这个是自己的地址， jiangzhonglian是我的，别弄错了）
$ git clone https://github.com/jiangzhonglian/kaggle.git
## 进入该仓库的文件夹
$ cd kaggle
## 查看该仓库远程 repo
$ git remote
## 添加 apachecn 的远程 repo 仓库（添加一次以后就不用使用了）
$ git remote add origin_online https://github.com/apachecn/kaggle.git
```
> 三) jiangzhonglian/kaggle 文件更新（修改文件后，第二次要进行提交）
```
# 用于 pull 保持和 apachecn 同步
$ git pull origin_online master
#上传到 自己的 repo 仓库
$ git push origin master
```
> 四) pull requests 到 apachecn/kaggle
================================================
FILE: docs/Kaggle/README.md
================================================
# Kaggle
> **你已经抓住了石头，现在是挥舞它的时候了！**
* [ApacheCN 组织资源](https://docs.apachecn.org/):
* [关于 ApacheCN](https://home.apachecn.org/about/): 一边学习和整理，一边录制项目视频，希望能组建一个开源的公益团队对国内机器学习社区做一些贡献，同时也为装逼做准备!!
## 直播系列
* https://space.bilibili.com/97678687/channel/detail?cid=76173
> kaggle入门系列
* [Kaggle系列-数字识别](https://www.bilibili.com/video/av53119200)
* [Kaggle系列-泰坦尼克号](https://www.bilibili.com/video/av65679428)
> 比赛直播系列
* [视频: 2019ICME 抖音视频理解 top2 solution 分享及 数据比赛入门讲解](https://www.bilibili.com/video/av57385532)
* [文档: icme2019-top2.pptx](/docs/简历指南/icme2019-top2.pptx)
* [昊神GitHub地址: https://github.com/Smilexuhc](https://github.com/Smilexuhc)
* [昊神整理比赛系列: https://github.com/Smilexuhc/Data-Competition-TopSolution](https://github.com/Smilexuhc/Data-Competition-TopSolution)
## Kaggle 官方教程
> 机器学习入门
* [**1. 模型是怎样工作的**](learn/intro-to-machine-learning/1.md)
* [**2. 数据探索**](learn/intro-to-machine-learning/2.md)
* [**3. 你的第一个机器学习模型**](learn/intro-to-machine-learning/3.md)
* [**4. 模型验证**](learn/intro-to-machine-learning/4.md)
* [**5. 欠拟合与过拟合**](learn/intro-to-machine-learning/5.md)
* [**6. 随机森林**](learn/intro-to-machine-learning/6.md)
* [**7. 继续你的征程**](learn/intro-to-machine-learning/7.md)
> 补充
* [**Embedding**](learn/embeddings)
## [竞赛](https://www.kaggle.com/competitions)
* 【推荐】特征工程全过程: https://www.cnblogs.com/jasonfreak/p/5448385.html
> train loss 与 test loss 结果分析
* train loss 不断下降，test loss不断下降，说明网络仍在学习;
* train loss 不断下降，test loss趋于不变，说明网络过拟合;
* train loss 趋于不变，test loss不断下降，说明数据集100%有问题;
* train loss 趋于不变，test loss趋于不变，说明学习遇到瓶颈，需要减小学习率或批量数目;
* train loss 不断上升，test loss不断上升，说明网络结构设计不当，训练超参数设置不当，数据集经过清洗等问题。
```python
机器学习比赛，奖金很高，业界承认分数。
现在我们已经准备好尝试 Kaggle 竞赛了，这些竞赛分成以下几个类别。
```
### [第1部分：课业比赛 InClass](https://www.kaggle.com/competitions?sortBy=deadne&group=all&page=1&pageSize=20&segment=inClass)
`课业比赛 InClass` 是学校教授机器学习的老师留作业的地方，这里的竞赛有些会向public开放参赛，也有些仅仅是学校内部教学使用。
### [第2部分：入门比赛 Getting Started](https://www.kaggle.com/competitions?sortBy=deadline&group=all&page=1&pageSize=20&segment=gettingStarted)
`入门比赛 Getting Started` 给萌新们一个试水的机会，没有奖金，但有非常多的前辈经验可供学习。很久以前Kaggle这个栏目名称是101的时候，比赛题目还很多，但是现在只保留了9个最经典的入门竞赛：手写数字识别、沉船事故幸存估计、脸部识别、Julia语言入门。
* [**数字识别**](competitions/getting-started/digit-recognizer)
* [**泰坦尼克**](competitions/getting-started/titanic)
* [**房价预测**](competitions/getting-started/house-price)
* [**nlp-情感分析**](competitions/getting-started/word2vec-nlp-tutorial)
### [第3部分：训练场 Playground](https://www.kaggle.com/competitions?sortBy=deadline&group=all&page=1&pageSize=20&segment=playground)
`训练场 Playground`里的题目以有趣为主，比如猫狗照片分类的问题。现在这个分类下的题目不算多，但是热度很高。
* [**猫狗识别**](competitions/playground/dogs-vs-cats)
### [第4部分： 研究项目(少奖金) Research](https://www.kaggle.com/competitions?sortBy=prize&group=active&page=1&pageSize=20&segment=research)
`研究型 Research` 竞赛通常是机器学习前沿技术或者公益性质的题目。竞赛奖励可能是现金，也有一部分以会议邀请、发表论文的形式奖励。
### [第5部分：人才征募 Recruitment](https://www.kaggle.com/competitions?sortBy=prize&group=active&page=1&pageSize=20&segment=recruitment)
`人才征募 Recruitment` 竞赛是赞助企业寻求数据科学家、算法设计人才的渠道。只允许个人参赛，不接受团队报名。
### [第6部分： 大型组织比赛(大奖金) Featured](https://www.kaggle.com/competitions?sortBy=prize&group=active&page=1&pageSize=20&segment=featured)
`推荐比赛 Featured` 是瞄准商业问题带有奖金的公开竞赛。如果有幸赢得比赛，不但可以获得奖金，模型也可能会被竞赛赞助商应用到商业实践中呢。
* [**Mercari 价格推荐挑战**](competitions/featured/mercari-price-suggestion-challenge)
* [**Home Credit Default Risk**](competitions/featured/home-credit-default-risk)
### [第7部分： 限量邀请赛 Masters（新）](https://www.kaggle.com/competitions?sortBy=grouped&group=general&page=1&pageSize=20&category=masters)
`Masters（新）` 限量参与比赛（受邀）
### [第8部分： 多评估标准赛 Analytics（新）](https://www.kaggle.com/competitions?sortBy=grouped&group=general&page=1&pageSize=20&category=analytics)
`Analytics（新）` 选择最优评估标准来排名的比赛
### 天池
* [**天池入门教程: O2O优惠券-使用新人赛**](https://tianchi.aliyun.com/notebook/detail.html?spm=5176.11409386.4851167.7.65c91d07FiVHVN&id=4796)
* [**天池第一名: O2O优惠券-预测用户领取优惠劵后是否核销**](https://github.com/wepe/O2O-Coupon-Usage-Forecast)
## 其他部分
* [数据集](https://www.kaggle.com/datasets): 数据集，可直接用于机器学习。
* [核心思想](https://www.kaggle.com/kernels): 在线编程。（猜测，基于 jupyter 实现）
* [论坛](https://www.kaggle.com/discussion): 发帖回帖讨论的平台
* [**学习 - 新**](https://www.kaggle.com/learn/overview): 最新发布的学习教程
* [招聘](https://www.kaggle.com/jobs): 企业招聘数据科学家的位置
## 解决方案列表
* [解决方案列表](/docs/writeup-list.md)
如果解决方案太大，可以先放在这个列表中。以后再逐步整合到这个仓库。
## 机器学习算法
> 常用算法选择
> 常用工具选择
> 解决问题的流程
1. 链接场景和目标
2. 链接评估准则
3. 认识数据
4. 数据预处理（清洗、调权）
5. 特征工程
6. 模型调参
7. 模型状态分析
8. 模型融合
> 数据预处理
* 数据清洗
* 去掉样本数据的异常数据。（比如连续型数据中的离群点）
* 去除缺失大量特征的数据
* 数据采样
* 下/上采样（假设正负样本比例1:100，把正样本的数量重复100次，这就叫上采样，也就是把比例小的样本放大。下采样同理，把比例大的数据抽取一部分，从而使比例变得接近于1；1）
* 保证样本均衡
* 工具 sql、pandas等
> 特征工程
> 特征处理
- 数值型：连续型数据离散化或者归一化、数据变化（log、指数、box-cox）
- 类别型：做编码，eg：one-hot编码，如果类别数据有缺失，把缺失也作为一个类别即可。
- 时间类：间隔化（距离某个节日多少天）、与其他特征（eg：次数）融合，变成一周登陆几次、离散化（eg：外卖，把时间分为【饭店、非饭店】）
- 文本类：N-gram、Bag-of-words、TF-IDF
- 统计型：与业务强关联
- 组合特征
## 贡献指南
> **欢迎任何人参与和完善：一个人可以走的很快，但是一群人却可以走的更远**
本项目接受大家提交 WriteUp（题解）。
WriteUp 需要带有预处理过程，从你能下载到的原始数据开始，并且带有验证过程和评价指标。
请放在`/competitions/{分类}/{名称}`目录下。
其中分类一共有六个，请见上面，名称是 URL 中`/c/`后面的部分。
================================================
FILE: docs/Kaggle/competitions/featured/home-credit-default-risk/ManualFeatureEngineering_P1.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 简介：手动特征工程 part 1\n",
"\n",
"如果你是初次参与这个竞赛，我强烈建议你先阅读这个文档https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction/\n",
"\n",
"在这份文档中，我们首先会研究如何通过手工的方式，处理捷信公司贷款违约风险问题的相关特征。在更早之前的一篇文章中，我们仅仅使用了贷款申请数据来建模。我们利用该数据所建立的最佳模型在排行榜上的分数大约是0.74。为了提高这个分数，我们需要从其他数据中引入更多的信息，包括bureau和bureau_balance数据。数据的定义如下：\n",
"bureau:捷信公司(Home Credit，下同)所掌握的客户从其他金融机构中贷款的历史信息。其中每一行代表一条历史贷款信息\n",
"bureau_balance:历史贷款的月度信息。其中每一行代表一条月度信息\n",
"\n",
"手动特征工程是一个很无聊的过程（这就是我们为什么需要使用自动化的特征处理工具来处理我们的数据），而且该过程经常需要依靠该领域的专家知识来辅助处理。由于我对贷款行业和客户违约原因知之甚少，所以我的关注点集中在如何收集尽量多的数据来训练模型，原因在于模型会代替我们来评估哪些数据比较重要。简而言之，我们的方法就是获得尽量多的数据提供给模型使用！接着我们可以利用特征的重要性或是PCA降维等方法来进行特征约减\n",
"\n",
"进行手动特征工程的过程需要大量Pandas代码，一点点耐心和许多操作数据的实践。尽管自动化的特性工程工具已经十分遍历，但是特性工程仍然需要使用大量数据清洗重组(data wrangle)一段时间才能完成"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#使用 pandas 和 numpy 进行数据处理\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"#使用 matplotlib 和 seaborn 进行绘制\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"#禁止 pandas 的编译警告\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"plt.style.use('fivethirtyeight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 案例：计算客户历史贷款总数\n",
"\n",
"为了解释常见的手工特征工程流程，我们首先计算一个客户在其他金融机构的历史贷款信息总和，这一过程会用到许多我们接下去会频繁使用到的Pandas操作：\n",
"\n",
"* groupby: 按列对数据进行分组。在这个案例中我们根据具有唯一性的SK_ID_CURR列进行分组【按列进行分组？不是按行吗？】\n",
"* agg: 对分组后的数据进行计算，例如列均值。我们可以直接使用方程(grouped_df.mean())或者将agg方程与转换后的列表一起使用(grouped_df.agg([mean, max, min, sum]))\n",
"* merge: 将聚合后的数据与对应的客户匹配。我们需要将原始训练数据与计算后的SK_ID_CURR列数据结合在一起，如果SK_ID_CURR列没有对应的用户数据，则置为NaN。\n",
"\n",
"我们还经常使用（重命名）函数来将指定的列(columns)重命名为字典(dictionary)，这有利于对新创建的特征保持观察\n",
"\n",
"这看起来有点复杂，所以最终需要构造一个函数来代替我们执行这个过程。但首先，我们需要了解如何手工实现这个过程"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" SK_ID_CURR \n",
" SK_ID_BUREAU \n",
" CREDIT_ACTIVE \n",
" CREDIT_CURRENCY \n",
" DAYS_CREDIT \n",
" CREDIT_DAY_OVERDUE \n",
" DAYS_CREDIT_ENDDATE \n",
" DAYS_ENDDATE_FACT \n",
" AMT_CREDIT_MAX_OVERDUE \n",
" CNT_CREDIT_PROLONG \n",
" AMT_CREDIT_SUM \n",
" AMT_CREDIT_SUM_DEBT \n",
" AMT_CREDIT_SUM_LIMIT \n",
" AMT_CREDIT_SUM_OVERDUE \n",
" CREDIT_TYPE \n",
" DAYS_CREDIT_UPDATE \n",
" AMT_ANNUITY \n",
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" 0 \n",
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" Consumer credit \n",
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"
\n",
""
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"text/plain": [
" SK_ID_CURR SK_ID_BUREAU CREDIT_ACTIVE CREDIT_CURRENCY DAYS_CREDIT \\\n",
"0 215354 5714462 Closed currency 1 -497 \n",
"1 215354 5714463 Active currency 1 -208 \n",
"2 215354 5714464 Active currency 1 -203 \n",
"3 215354 5714465 Active currency 1 -203 \n",
"4 215354 5714466 Active currency 1 -629 \n",
"\n",
" CREDIT_DAY_OVERDUE DAYS_CREDIT_ENDDATE DAYS_ENDDATE_FACT \\\n",
"0 0 -153.0 -153.0 \n",
"1 0 1075.0 NaN \n",
"2 0 528.0 NaN \n",
"3 0 NaN NaN \n",
"4 0 1197.0 NaN \n",
"\n",
" AMT_CREDIT_MAX_OVERDUE CNT_CREDIT_PROLONG AMT_CREDIT_SUM \\\n",
"0 NaN 0 91323.0 \n",
"1 NaN 0 225000.0 \n",
"2 NaN 0 464323.5 \n",
"3 NaN 0 90000.0 \n",
"4 77674.5 0 2700000.0 \n",
"\n",
" AMT_CREDIT_SUM_DEBT AMT_CREDIT_SUM_LIMIT AMT_CREDIT_SUM_OVERDUE \\\n",
"0 0.0 NaN 0.0 \n",
"1 171342.0 NaN 0.0 \n",
"2 NaN NaN 0.0 \n",
"3 NaN NaN 0.0 \n",
"4 NaN NaN 0.0 \n",
"\n",
" CREDIT_TYPE DAYS_CREDIT_UPDATE AMT_ANNUITY \n",
"0 Consumer credit -131 NaN \n",
"1 Credit card -20 NaN \n",
"2 Consumer credit -16 NaN \n",
"3 Credit card -16 NaN \n",
"4 Consumer credit -21 NaN "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 读入bureau属性\n",
"bureau = pd.read_csv('bureau.csv')\n",
"bureau.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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" previous_loan_counts \n",
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" 100001 \n",
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"text/plain": [
" SK_ID_CURR previous_loan_counts\n",
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"4 100005 3"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 按照客户id (SK_ID_CURR) 进行Groupeby操作，计算历史贷款总数，并将SK_ID_BUREAU列重命名为previous_loan_counts\n",
"previous_loan_counts = bureau.groupby('SK_ID_CURR', as_index = False)['SK_ID_BUREAU'].count().rename(columns = {'SK_ID_BUREAU': 'previous_loan_counts'})\n",
"previous_loan_counts.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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" N \n",
" Y \n",
" 0 \n",
" 121500.0 \n",
" 513000.0 \n",
" 21865.5 \n",
" ... \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 1.0 \n",
" \n",
" \n",
"
\n",
"5 rows × 123 columns
\n",
""
],
"text/plain": [
" SK_ID_CURR TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR \\\n",
"0 100002 1 Cash loans M N \n",
"1 100003 0 Cash loans F N \n",
"2 100004 0 Revolving loans M Y \n",
"3 100006 0 Cash loans F N \n",
"4 100007 0 Cash loans M N \n",
"\n",
" FLAG_OWN_REALTY CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY \\\n",
"0 Y 0 202500.0 406597.5 24700.5 \n",
"1 N 0 270000.0 1293502.5 35698.5 \n",
"2 Y 0 67500.0 135000.0 6750.0 \n",
"3 Y 0 135000.0 312682.5 29686.5 \n",
"4 Y 0 121500.0 513000.0 21865.5 \n",
"\n",
" ... FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 \\\n",
"0 ... 0 0 0 \n",
"1 ... 0 0 0 \n",
"2 ... 0 0 0 \n",
"3 ... 0 0 0 \n",
"4 ... 0 0 0 \n",
"\n",
" AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 NaN NaN \n",
"4 0.0 0.0 \n",
"\n",
" AMT_REQ_CREDIT_BUREAU_WEEK AMT_REQ_CREDIT_BUREAU_MON \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 NaN NaN \n",
"4 0.0 0.0 \n",
"\n",
" AMT_REQ_CREDIT_BUREAU_QRT AMT_REQ_CREDIT_BUREAU_YEAR previous_loan_counts \n",
"0 0.0 1.0 8.0 \n",
"1 0.0 0.0 4.0 \n",
"2 0.0 0.0 2.0 \n",
"3 NaN NaN 0.0 \n",
"4 0.0 0.0 1.0 \n",
"\n",
"[5 rows x 123 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 将计算得到的previous_loan_counts列加入原先的训练数据application_train.csv\n",
"train = pd.read_csv('application_train.csv')\n",
"train = train.merge(previous_loan_counts, on = 'SK_ID_CURR', how = 'left')# 左连接，左侧DataFrame取全部，右侧DataFrame取部分\n",
"\n",
"# 填补缺失值\n",
"train['previous_loan_counts'] = train['previous_loan_counts'].fillna(0)\n",
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"向左滑动查看新插入的列"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 使用r值检验方法评估新属性（变量）\n",
"为了确定新加入的变量是否有用，我们计算了该变量与目标值之间的皮尔森相关系数(Pearson Correlation Coeffcient, r-value)。该系数可以评估两个变量之间的线性关系强弱，范围在[-1, 1]之间。r值检验方法并不是检验新变量“有效性”最好的方法，但是它可以给出一个新变量对于机器学习模型是否有帮助的初步估计。新变量r值检验的值越大，那么该变量的改变对于目标值的影响就越大。因此,我们希望寻找到与目标值的之间，皮尔森相关系数绝对值最大的新变量"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 核密度估计图\n",
"核密度估计图展示的是一个单独变量的分布（可以将其看做一个平滑的直方图），为了看到某个分类中变量的值对分布函数的影响，我们根据类别对不同的分布使用不同的颜色标注。例如，我们可以通过目标值为1或0来判断previous_loan_count变量的核密度估计，所得到的KDE可以显示出未偿还贷款的人(TARGET==1)和偿还贷款的人(TARGET==0)之间变量分布的所有显著性差异，这将作为变量是否与机器学习模型相关的指标\n",
"\n",
"我们将把这个绘图功能放在一个函数中，以便对任何变量重复使用"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# 绘制根据不同目标值着色的变量分布\n",
"def kde_target(var_name, df):\n",
" \n",
" # 计算变量与目标值之间的皮尔森相关系数\n",
" corr = df['TARGET'].corr(df[var_name])\n",
" \n",
" # 计算偿还或未偿还贷款数据的中位数\n",
" avg_repaid = df.ix[df['TARGET'] == 0, var_name].median()# df.ix：索引函数，既可以通过行号索引，也可以通过行标签索引\n",
" avg_not_repaid = df.ix[df['TARGET'] == 1, var_name].median()\n",
" \n",
" plt.figure(figsize = (12, 6))\n",
" \n",
" # 绘制偿还或未偿还贷款数据的分布\n",
" sns.kdeplot(df.ix[df['TARGET'] == 0, var_name], label = 'TARGET == 0')\n",
" sns.kdeplot(df.ix[df['TARGET'] == 1, var_name], label = 'TARGET == 1')\n",
" \n",
" # 标签绘制\n",
" plt.xlabel(var_name); plt.ylabel('Density'); plt.title('%s Distribution' % var_name)\n",
" plt.legend();# 显示图例\n",
" \n",
" # 输出皮尔森相关系数\n",
" print('The correlation between %s and the TARGET is %0.4f' % (var_name, corr))\n",
" # 输出均值\n",
" print('Median value for loan that was not repaid = %0.4f' % avg_not_repaid)\n",
" print('Median value for loan that was repaid = %0.4f' % avg_repaid)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们可以利用EXT_SOURCE_3变量来测试该方程，这是根据随机森林和梯度提升机得出的最佳变量https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The correlation between EXT_SOURCE_3 and the TARGET is -0.1789\n",
"Median value for loan that was not repaid = 0.3791\n",
"Median value for loan that was repaid = 0.5460\n"
]
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"kde_target('EXT_SOURCE_3', train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"接下来对新变量previous_loan_counts进行同样的操作"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The correlation between previous_loan_counts and the TARGET is -0.0100\n",
"Median value for loan that was not repaid = 3.0000\n",
"Median value for loan that was repaid = 4.0000\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"kde_target('previous_loan_counts', train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"从图上看，很难说这个变量是否重要。相关系数很弱并且分布几乎没有什么显著差异\n",
"\n",
"让我们继续从bureau数据中尝试生成更多变量，我们考虑每个数值列的均值，最小值和最大值 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 聚合数值列\n",
"为了考虑bureau数据集中的数值信息，我们对所有数值列做一些计算处理。\n",
"为此，我们将数据按照用户id分组(groupby)，分组后的数据再进行聚合(agg)操作，最后将结果合并(merge)到训练数据集中。由于聚合(agg)操作只能在数值型数据中使用，所有我们只计算数值列的值。在这里我们依然使用均值(mean)、最大值(max)、最小值(min)和总和(sum)函数，但事实上任何函数都可以在agg函数中被调用，甚至包括自己编写的函数"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" SK_ID_CURR \n",
" DAYS_CREDIT \n",
" CREDIT_DAY_OVERDUE \n",
" ... \n",
" DAYS_CREDIT_UPDATE \n",
" AMT_ANNUITY \n",
" \n",
" \n",
" count \n",
" mean \n",
" max \n",
" min \n",
" sum \n",
" count \n",
" mean \n",
" max \n",
" min \n",
" ... \n",
" count \n",
" mean \n",
" max \n",
" min \n",
" sum \n",
" count \n",
" mean \n",
" max \n",
" min \n",
" sum \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 100001 \n",
" 7 \n",
" -735.000000 \n",
" -49 \n",
" -1572 \n",
" -5145 \n",
" 7 \n",
" 0.0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 7 \n",
" -93.142857 \n",
" -6 \n",
" -155 \n",
" -652 \n",
" 7 \n",
" 3545.357143 \n",
" 10822.5 \n",
" 0.0 \n",
" 24817.5 \n",
" \n",
" \n",
" 1 \n",
" 100002 \n",
" 8 \n",
" -874.000000 \n",
" -103 \n",
" -1437 \n",
" -6992 \n",
" 8 \n",
" 0.0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 8 \n",
" -499.875000 \n",
" -7 \n",
" -1185 \n",
" -3999 \n",
" 7 \n",
" 0.000000 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" \n",
" \n",
" 2 \n",
" 100003 \n",
" 4 \n",
" -1400.750000 \n",
" -606 \n",
" -2586 \n",
" -5603 \n",
" 4 \n",
" 0.0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 4 \n",
" -816.000000 \n",
" -43 \n",
" -2131 \n",
" -3264 \n",
" 0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0.0 \n",
" \n",
" \n",
" 3 \n",
" 100004 \n",
" 2 \n",
" -867.000000 \n",
" -408 \n",
" -1326 \n",
" -1734 \n",
" 2 \n",
" 0.0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 2 \n",
" -532.000000 \n",
" -382 \n",
" -682 \n",
" -1064 \n",
" 0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0.0 \n",
" \n",
" \n",
" 4 \n",
" 100005 \n",
" 3 \n",
" -190.666667 \n",
" -62 \n",
" -373 \n",
" -572 \n",
" 3 \n",
" 0.0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 3 \n",
" -54.333333 \n",
" -11 \n",
" -121 \n",
" -163 \n",
" 3 \n",
" 1420.500000 \n",
" 4261.5 \n",
" 0.0 \n",
" 4261.5 \n",
" \n",
" \n",
"
\n",
"5 rows × 61 columns
\n",
""
],
"text/plain": [
" SK_ID_CURR DAYS_CREDIT CREDIT_DAY_OVERDUE \\\n",
" count mean max min sum count \n",
"0 100001 7 -735.000000 -49 -1572 -5145 7 \n",
"1 100002 8 -874.000000 -103 -1437 -6992 8 \n",
"2 100003 4 -1400.750000 -606 -2586 -5603 4 \n",
"3 100004 2 -867.000000 -408 -1326 -1734 2 \n",
"4 100005 3 -190.666667 -62 -373 -572 3 \n",
"\n",
" ... DAYS_CREDIT_UPDATE \\\n",
" mean max min ... count mean max min sum \n",
"0 0.0 0 0 ... 7 -93.142857 -6 -155 -652 \n",
"1 0.0 0 0 ... 8 -499.875000 -7 -1185 -3999 \n",
"2 0.0 0 0 ... 4 -816.000000 -43 -2131 -3264 \n",
"3 0.0 0 0 ... 2 -532.000000 -382 -682 -1064 \n",
"4 0.0 0 0 ... 3 -54.333333 -11 -121 -163 \n",
"\n",
" AMT_ANNUITY \n",
" count mean max min sum \n",
"0 7 3545.357143 10822.5 0.0 24817.5 \n",
"1 7 0.000000 0.0 0.0 0.0 \n",
"2 0 NaN NaN NaN 0.0 \n",
"3 0 NaN NaN NaN 0.0 \n",
"4 3 1420.500000 4261.5 0.0 4261.5 \n",
"\n",
"[5 rows x 61 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 按照用户id分组并聚合数据\n",
"bureau_agg = bureau.drop(columns = ['SK_ID_BUREAU']).groupby('SK_ID_CURR', as_index = False).agg(['count', 'mean', 'max', 'min', 'sum']).reset_index()\n",
"bureau_agg.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们需要为每一列创建新的名称。下面的代码通过结合bureau字段生成新名称。但是利用上述方法生成数据文件具有多级索引，我发现这会让这些数据变得难以理解和处理，所以我想尽快将其削减为单级结构"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# 列表的列名称\n",
"columns = ['SK_ID_CURR']\n",
"\n",
"# 根据变量名迭代\n",
"for var in bureau_agg.columns.levels[0]:\n",
" # 跳过id名（因为id名的mean、max...统计量没有任何意义）\n",
" if var != 'SK_ID_CURR':\n",
" \n",
" #通过结合bureau字段生成新名称\n",
" for stat in bureau_agg.columns.levels[1][:-1]:# 遍历第二行的所有列\n",
" columns.append('bureau_%s_%s' % (var, stat))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" SK_ID_CURR \n",
" bureau_DAYS_CREDIT_count \n",
" bureau_DAYS_CREDIT_mean \n",
" bureau_DAYS_CREDIT_max \n",
" bureau_DAYS_CREDIT_min \n",
" bureau_DAYS_CREDIT_sum \n",
" bureau_CREDIT_DAY_OVERDUE_count \n",
" bureau_CREDIT_DAY_OVERDUE_mean \n",
" bureau_CREDIT_DAY_OVERDUE_max \n",
" bureau_CREDIT_DAY_OVERDUE_min \n",
" ... \n",
" bureau_DAYS_CREDIT_UPDATE_count \n",
" bureau_DAYS_CREDIT_UPDATE_mean \n",
" bureau_DAYS_CREDIT_UPDATE_max \n",
" bureau_DAYS_CREDIT_UPDATE_min \n",
" bureau_DAYS_CREDIT_UPDATE_sum \n",
" bureau_AMT_ANNUITY_count \n",
" bureau_AMT_ANNUITY_mean \n",
" bureau_AMT_ANNUITY_max \n",
" bureau_AMT_ANNUITY_min \n",
" bureau_AMT_ANNUITY_sum \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 100001 \n",
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"5 rows × 61 columns
\n",
""
],
"text/plain": [
" SK_ID_CURR bureau_DAYS_CREDIT_count bureau_DAYS_CREDIT_mean \\\n",
"0 100001 7 -735.000000 \n",
"1 100002 8 -874.000000 \n",
"2 100003 4 -1400.750000 \n",
"3 100004 2 -867.000000 \n",
"4 100005 3 -190.666667 \n",
"\n",
" bureau_DAYS_CREDIT_max bureau_DAYS_CREDIT_min bureau_DAYS_CREDIT_sum \\\n",
"0 -49 -1572 -5145 \n",
"1 -103 -1437 -6992 \n",
"2 -606 -2586 -5603 \n",
"3 -408 -1326 -1734 \n",
"4 -62 -373 -572 \n",
"\n",
" bureau_CREDIT_DAY_OVERDUE_count bureau_CREDIT_DAY_OVERDUE_mean \\\n",
"0 7 0.0 \n",
"1 8 0.0 \n",
"2 4 0.0 \n",
"3 2 0.0 \n",
"4 3 0.0 \n",
"\n",
" bureau_CREDIT_DAY_OVERDUE_max bureau_CREDIT_DAY_OVERDUE_min \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" ... bureau_DAYS_CREDIT_UPDATE_count \\\n",
"0 ... 7 \n",
"1 ... 8 \n",
"2 ... 4 \n",
"3 ... 2 \n",
"4 ... 3 \n",
"\n",
" bureau_DAYS_CREDIT_UPDATE_mean bureau_DAYS_CREDIT_UPDATE_max \\\n",
"0 -93.142857 -6 \n",
"1 -499.875000 -7 \n",
"2 -816.000000 -43 \n",
"3 -532.000000 -382 \n",
"4 -54.333333 -11 \n",
"\n",
" bureau_DAYS_CREDIT_UPDATE_min bureau_DAYS_CREDIT_UPDATE_sum \\\n",
"0 -155 -652 \n",
"1 -1185 -3999 \n",
"2 -2131 -3264 \n",
"3 -682 -1064 \n",
"4 -121 -163 \n",
"\n",
" bureau_AMT_ANNUITY_count bureau_AMT_ANNUITY_mean bureau_AMT_ANNUITY_max \\\n",
"0 7 3545.357143 10822.5 \n",
"1 7 0.000000 0.0 \n",
"2 0 NaN NaN \n",
"3 0 NaN NaN \n",
"4 3 1420.500000 4261.5 \n",
"\n",
" bureau_AMT_ANNUITY_min bureau_AMT_ANNUITY_sum \n",
"0 0.0 24817.5 \n",
"1 0.0 0.0 \n",
"2 NaN 0.0 \n",
"3 NaN 0.0 \n",
"4 0.0 4261.5 \n",
"\n",
"[5 rows x 61 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 将生成的类名列表作为新的类名\n",
"bureau_agg.columns = columns\n",
"bureau_agg.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"现在我们只需要简单地将之前处理好的数据与训练数据结合在一起"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" SK_ID_CURR \n",
" TARGET \n",
" NAME_CONTRACT_TYPE \n",
" CODE_GENDER \n",
" FLAG_OWN_CAR \n",
" FLAG_OWN_REALTY \n",
" CNT_CHILDREN \n",
" AMT_INCOME_TOTAL \n",
" AMT_CREDIT \n",
" AMT_ANNUITY \n",
" ... \n",
" bureau_DAYS_CREDIT_UPDATE_count \n",
" bureau_DAYS_CREDIT_UPDATE_mean \n",
" bureau_DAYS_CREDIT_UPDATE_max \n",
" bureau_DAYS_CREDIT_UPDATE_min \n",
" bureau_DAYS_CREDIT_UPDATE_sum \n",
" bureau_AMT_ANNUITY_count \n",
" bureau_AMT_ANNUITY_mean \n",
" bureau_AMT_ANNUITY_max \n",
" bureau_AMT_ANNUITY_min \n",
" bureau_AMT_ANNUITY_sum \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 100002 \n",
" 1 \n",
" Cash loans \n",
" M \n",
" N \n",
" Y \n",
" 0 \n",
" 202500.0 \n",
" 406597.5 \n",
" 24700.5 \n",
" ... \n",
" 8.0 \n",
" -499.875 \n",
" -7.0 \n",
" -1185.0 \n",
" -3999.0 \n",
" 7.0 \n",
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" 0.0 \n",
" 0.0 \n",
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" \n",
" \n",
" 1 \n",
" 100003 \n",
" 0 \n",
" Cash loans \n",
" F \n",
" N \n",
" N \n",
" 0 \n",
" 270000.0 \n",
" 1293502.5 \n",
" 35698.5 \n",
" ... \n",
" 4.0 \n",
" -816.000 \n",
" -43.0 \n",
" -2131.0 \n",
" -3264.0 \n",
" 0.0 \n",
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" NaN \n",
" NaN \n",
" 0.0 \n",
" \n",
" \n",
" 2 \n",
" 100004 \n",
" 0 \n",
" Revolving loans \n",
" M \n",
" Y \n",
" Y \n",
" 0 \n",
" 67500.0 \n",
" 135000.0 \n",
" 6750.0 \n",
" ... \n",
" 2.0 \n",
" -532.000 \n",
" -382.0 \n",
" -682.0 \n",
" -1064.0 \n",
" 0.0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0.0 \n",
" \n",
" \n",
" 3 \n",
" 100006 \n",
" 0 \n",
" Cash loans \n",
" F \n",
" N \n",
" Y \n",
" 0 \n",
" 135000.0 \n",
" 312682.5 \n",
" 29686.5 \n",
" ... \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" \n",
" \n",
" 4 \n",
" 100007 \n",
" 0 \n",
" Cash loans \n",
" M \n",
" N \n",
" Y \n",
" 0 \n",
" 121500.0 \n",
" 513000.0 \n",
" 21865.5 \n",
" ... \n",
" 1.0 \n",
" -783.000 \n",
" -783.0 \n",
" -783.0 \n",
" -783.0 \n",
" 0.0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0.0 \n",
" \n",
" \n",
"
\n",
"5 rows × 183 columns
\n",
""
],
"text/plain": [
" SK_ID_CURR TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR \\\n",
"0 100002 1 Cash loans M N \n",
"1 100003 0 Cash loans F N \n",
"2 100004 0 Revolving loans M Y \n",
"3 100006 0 Cash loans F N \n",
"4 100007 0 Cash loans M N \n",
"\n",
" FLAG_OWN_REALTY CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY \\\n",
"0 Y 0 202500.0 406597.5 24700.5 \n",
"1 N 0 270000.0 1293502.5 35698.5 \n",
"2 Y 0 67500.0 135000.0 6750.0 \n",
"3 Y 0 135000.0 312682.5 29686.5 \n",
"4 Y 0 121500.0 513000.0 21865.5 \n",
"\n",
" ... bureau_DAYS_CREDIT_UPDATE_count \\\n",
"0 ... 8.0 \n",
"1 ... 4.0 \n",
"2 ... 2.0 \n",
"3 ... NaN \n",
"4 ... 1.0 \n",
"\n",
" bureau_DAYS_CREDIT_UPDATE_mean bureau_DAYS_CREDIT_UPDATE_max \\\n",
"0 -499.875 -7.0 \n",
"1 -816.000 -43.0 \n",
"2 -532.000 -382.0 \n",
"3 NaN NaN \n",
"4 -783.000 -783.0 \n",
"\n",
" bureau_DAYS_CREDIT_UPDATE_min bureau_DAYS_CREDIT_UPDATE_sum \\\n",
"0 -1185.0 -3999.0 \n",
"1 -2131.0 -3264.0 \n",
"2 -682.0 -1064.0 \n",
"3 NaN NaN \n",
"4 -783.0 -783.0 \n",
"\n",
" bureau_AMT_ANNUITY_count bureau_AMT_ANNUITY_mean bureau_AMT_ANNUITY_max \\\n",
"0 7.0 0.0 0.0 \n",
"1 0.0 NaN NaN \n",
"2 0.0 NaN NaN \n",
"3 NaN NaN NaN \n",
"4 0.0 NaN NaN \n",
"\n",
" bureau_AMT_ANNUITY_min bureau_AMT_ANNUITY_sum \n",
"0 0.0 0.0 \n",
"1 NaN 0.0 \n",
"2 NaN 0.0 \n",
"3 NaN NaN \n",
"4 NaN 0.0 \n",
"\n",
"[5 rows x 183 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 与训练数据结合\n",
"train = train.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\n",
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 聚合值和目标的相关性\n",
"我们可以计算新的变量与目标值之间的相关性，同样的，我们可以用该相关系数作为变量对于模型重要性的一个估计"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# 相关系数列表\n",
"new_corrs = []\n",
"\n",
"# 根据变量名迭代\n",
"for col in columns:\n",
" # 计算与目标值之间的相关系数\n",
" corr = train['TARGET'].corr(train[col])\n",
" \n",
" \n",
" # 以tuple的形式存入list中\n",
" \n",
" \n",
" new_corrs.append((col, corr))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"在下面的代码中，我们使用Python中的排序函数来对相关系数按照绝对值的大小进行排序。同时，我们还使用了Python中的另一个重要操作——匿名函数lambda"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('bureau_DAYS_CREDIT_mean', 0.089728967219981),\n",
" ('bureau_DAYS_CREDIT_min', 0.07524825103010374),\n",
" ('bureau_DAYS_CREDIT_UPDATE_mean', 0.06892735266968684),\n",
" ('bureau_DAYS_ENDDATE_FACT_min', 0.055887379843920795),\n",
" ('bureau_DAYS_CREDIT_ENDDATE_sum', 0.053734895601020585),\n",
" ('bureau_DAYS_ENDDATE_FACT_mean', 0.05319962585758622),\n",
" ('bureau_DAYS_CREDIT_max', 0.04978205463997309),\n",
" ('bureau_DAYS_ENDDATE_FACT_sum', 0.048853502611115936),\n",
" ('bureau_DAYS_CREDIT_ENDDATE_mean', 0.04698275433483553),\n",
" ('bureau_DAYS_CREDIT_UPDATE_min', 0.04286392247073023),\n",
" ('bureau_DAYS_CREDIT_sum', 0.04199982481484667),\n",
" ('bureau_DAYS_CREDIT_UPDATE_sum', 0.0414036353530601),\n",
" ('bureau_DAYS_CREDIT_ENDDATE_max', 0.03658963469632898),\n",
" ('bureau_DAYS_CREDIT_ENDDATE_min', 0.034281109921615996),\n",
" ('bureau_DAYS_ENDDATE_FACT_count', -0.03049230665332547)]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 对相关系数按照绝对值的大小进行排序\n",
"# 使用反转操作，确保最大值在list的最前面\n",
"new_corrs = sorted(new_corrs, key = lambda x: abs(x[1]), reverse = True)\n",
"new_corrs[:15] # 输出前15个数据"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"没有一个新变量与目标值TARGET有显著的相关性。我们可以从KDE图中看出，最高相关性的向量——bureau_DAYS_CREDIT_mean与目标值TARGET在绝对值方面的相关性"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The correlation between bureau_DAYS_CREDIT_mean and the TARGET is 0.0897\n",
"Median value for loan that was not repaid = -835.3333\n",
"Median value for loan that was repaid = -1067.0000\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"kde_target('bureau_DAYS_CREDIT_mean', train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这一列的定义是：客户前一次申请贷款距离现在的天数。我的解释是：这是在此次申请捷信公司贷款之前，距离上一次向其他金融机构申请贷款中间所隔的天数。因此，负数的绝对值越大，意味着前一次的贷款越久远(-835.333/-1067)。我们看到，这个变量的平均值与目标之间存在着极弱的正相关关系，这意味着更早之前申请贷款的客户更有可能向Home Credit偿还贷款（不违约），尽管这种微弱的相关性很可能是噪声"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 多重比较问题\n",
"让我们将之前的工作封装成一个函数，这样就可以在任何数值型数据中使用该方法。当我们想要对其他数据进行同样的操作时，只需要重用该函数"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def agg_numeric(df, group_var, df_name):\n",
" \"\"\"聚合数值型数据，可以用于为每个分组变量的实例创建新特征\n",
" \n",
" 参数\n",
" --------\n",
" df (dataframe): \n",
" 数据文件\n",
" group_var (string): \n",
" 用于将数据文件分组的变量\n",
" df_name (string): \n",
" 用于重命名列名的变量\n",
" \n",
" 返回值\n",
" --------\n",
" agg (dataframe): \n",
" 包含所有数值列聚合后统计量的数据文件，分组变量的每个实例都将计算出相应的统计量（平均值、最小值、最大值和总和）\n",
" 为了便于追踪所创建这些新变量，这些数值列同时会被重命名 \n",
" \"\"\"\n",
" # 除去分组变量以外的id变量\n",
" for col in df:\n",
" if col != group_var and 'SK_ID' in col:\n",
" df = df.drop(columns = col)\n",
" \n",
" group_ids = df[group_var]\n",
" numeric_df = df.select_dtypes('number') # 选择数值列【df只剩下id列和需要分组的列？】\n",
" numeric_df[group_var] = group_ids\n",
" \n",
" # 通过指定变量分组并计算统计数据\n",
" agg = numeric_df.groupby(group_var).agg(['count', 'mean', 'max', 'min', 'sum']).reset_index()\n",
" \n",
" # 需要新建一个列名\n",
" columns = [group_var]\n",
" \n",
" # 根据变量名迭代\n",
" for var in agg.columns.levels[0]:\n",
" # 跳过分组变量\n",
" if var != group_var:\n",
" #通过结合相关字段生成新名称\n",
" for stat in agg.columns.levels[1][:-1]:\n",
" columns.append('%s_%s_%s' % (df_name, var, stat))\n",
" \n",
" agg.columns = columns\n",
" return agg"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" SK_ID_CURR \n",
" bureau_DAYS_CREDIT_count \n",
" bureau_DAYS_CREDIT_mean \n",
" bureau_DAYS_CREDIT_max \n",
" bureau_DAYS_CREDIT_min \n",
" bureau_DAYS_CREDIT_sum \n",
" bureau_CREDIT_DAY_OVERDUE_count \n",
" bureau_CREDIT_DAY_OVERDUE_mean \n",
" bureau_CREDIT_DAY_OVERDUE_max \n",
" bureau_CREDIT_DAY_OVERDUE_min \n",
" ... \n",
" bureau_DAYS_CREDIT_UPDATE_count \n",
" bureau_DAYS_CREDIT_UPDATE_mean \n",
" bureau_DAYS_CREDIT_UPDATE_max \n",
" bureau_DAYS_CREDIT_UPDATE_min \n",
" bureau_DAYS_CREDIT_UPDATE_sum \n",
" bureau_AMT_ANNUITY_count \n",
" bureau_AMT_ANNUITY_mean \n",
" bureau_AMT_ANNUITY_max \n",
" bureau_AMT_ANNUITY_min \n",
" bureau_AMT_ANNUITY_sum \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 100001 \n",
" 7 \n",
" -735.000000 \n",
" -49 \n",
" -1572 \n",
" -5145 \n",
" 7 \n",
" 0.0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 7 \n",
" -93.142857 \n",
" -6 \n",
" -155 \n",
" -652 \n",
" 7 \n",
" 3545.357143 \n",
" 10822.5 \n",
" 0.0 \n",
" 24817.5 \n",
" \n",
" \n",
" 1 \n",
" 100002 \n",
" 8 \n",
" -874.000000 \n",
" -103 \n",
" -1437 \n",
" -6992 \n",
" 8 \n",
" 0.0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 8 \n",
" -499.875000 \n",
" -7 \n",
" -1185 \n",
" -3999 \n",
" 7 \n",
" 0.000000 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" \n",
" \n",
" 2 \n",
" 100003 \n",
" 4 \n",
" -1400.750000 \n",
" -606 \n",
" -2586 \n",
" -5603 \n",
" 4 \n",
" 0.0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 4 \n",
" -816.000000 \n",
" -43 \n",
" -2131 \n",
" -3264 \n",
" 0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0.0 \n",
" \n",
" \n",
" 3 \n",
" 100004 \n",
" 2 \n",
" -867.000000 \n",
" -408 \n",
" -1326 \n",
" -1734 \n",
" 2 \n",
" 0.0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 2 \n",
" -532.000000 \n",
" -382 \n",
" -682 \n",
" -1064 \n",
" 0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0.0 \n",
" \n",
" \n",
" 4 \n",
" 100005 \n",
" 3 \n",
" -190.666667 \n",
" -62 \n",
" -373 \n",
" -572 \n",
" 3 \n",
" 0.0 \n",
" 0 \n",
" 0 \n",
" ... \n",
" 3 \n",
" -54.333333 \n",
" -11 \n",
" -121 \n",
" -163 \n",
" 3 \n",
" 1420.500000 \n",
" 4261.5 \n",
" 0.0 \n",
" 4261.5 \n",
" \n",
" \n",
"
\n",
"5 rows × 61 columns
\n",
""
],
"text/plain": [
" SK_ID_CURR bureau_DAYS_CREDIT_count bureau_DAYS_CREDIT_mean \\\n",
"0 100001 7 -735.000000 \n",
"1 100002 8 -874.000000 \n",
"2 100003 4 -1400.750000 \n",
"3 100004 2 -867.000000 \n",
"4 100005 3 -190.666667 \n",
"\n",
" bureau_DAYS_CREDIT_max bureau_DAYS_CREDIT_min bureau_DAYS_CREDIT_sum \\\n",
"0 -49 -1572 -5145 \n",
"1 -103 -1437 -6992 \n",
"2 -606 -2586 -5603 \n",
"3 -408 -1326 -1734 \n",
"4 -62 -373 -572 \n",
"\n",
" bureau_CREDIT_DAY_OVERDUE_count bureau_CREDIT_DAY_OVERDUE_mean \\\n",
"0 7 0.0 \n",
"1 8 0.0 \n",
"2 4 0.0 \n",
"3 2 0.0 \n",
"4 3 0.0 \n",
"\n",
" bureau_CREDIT_DAY_OVERDUE_max bureau_CREDIT_DAY_OVERDUE_min \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" ... bureau_DAYS_CREDIT_UPDATE_count \\\n",
"0 ... 7 \n",
"1 ... 8 \n",
"2 ... 4 \n",
"3 ... 2 \n",
"4 ... 3 \n",
"\n",
" bureau_DAYS_CREDIT_UPDATE_mean bureau_DAYS_CREDIT_UPDATE_max \\\n",
"0 -93.142857 -6 \n",
"1 -499.875000 -7 \n",
"2 -816.000000 -43 \n",
"3 -532.000000 -382 \n",
"4 -54.333333 -11 \n",
"\n",
" bureau_DAYS_CREDIT_UPDATE_min bureau_DAYS_CREDIT_UPDATE_sum \\\n",
"0 -155 -652 \n",
"1 -1185 -3999 \n",
"2 -2131 -3264 \n",
"3 -682 -1064 \n",
"4 -121 -163 \n",
"\n",
" bureau_AMT_ANNUITY_count bureau_AMT_ANNUITY_mean bureau_AMT_ANNUITY_max \\\n",
"0 7 3545.357143 10822.5 \n",
"1 7 0.000000 0.0 \n",
"2 0 NaN NaN \n",
"3 0 NaN NaN \n",
"4 3 1420.500000 4261.5 \n",
"\n",
" bureau_AMT_ANNUITY_min bureau_AMT_ANNUITY_sum \n",
"0 0.0 24817.5 \n",
"1 0.0 0.0 \n",
"2 NaN 0.0 \n",
"3 NaN 0.0 \n",
"4 0.0 4261.5 \n",
"\n",
"[5 rows x 61 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bureau_agg_new = agg_numeric(bureau.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
"bureau_agg_new.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"为了保证该函数按照预期的效果执行，我们需要与手工创建的聚合数据做对比"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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" \n",
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" SK_ID_CURR \n",
" bureau_DAYS_CREDIT_count \n",
" bureau_DAYS_CREDIT_mean \n",
" bureau_DAYS_CREDIT_max \n",
" bureau_DAYS_CREDIT_min \n",
" bureau_DAYS_CREDIT_sum \n",
" bureau_CREDIT_DAY_OVERDUE_count \n",
" bureau_CREDIT_DAY_OVERDUE_mean \n",
" bureau_CREDIT_DAY_OVERDUE_max \n",
" bureau_CREDIT_DAY_OVERDUE_min \n",
" ... \n",
" bureau_DAYS_CREDIT_UPDATE_count \n",
" bureau_DAYS_CREDIT_UPDATE_mean \n",
" bureau_DAYS_CREDIT_UPDATE_max \n",
" bureau_DAYS_CREDIT_UPDATE_min \n",
" bureau_DAYS_CREDIT_UPDATE_sum \n",
" bureau_AMT_ANNUITY_count \n",
" bureau_AMT_ANNUITY_mean \n",
" bureau_AMT_ANNUITY_max \n",
" bureau_AMT_ANNUITY_min \n",
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],
"text/plain": [
" SK_ID_CURR bureau_DAYS_CREDIT_count bureau_DAYS_CREDIT_mean \\\n",
"0 100001 7 -735.000000 \n",
"1 100002 8 -874.000000 \n",
"2 100003 4 -1400.750000 \n",
"3 100004 2 -867.000000 \n",
"4 100005 3 -190.666667 \n",
"\n",
" bureau_DAYS_CREDIT_max bureau_DAYS_CREDIT_min bureau_DAYS_CREDIT_sum \\\n",
"0 -49 -1572 -5145 \n",
"1 -103 -1437 -6992 \n",
"2 -606 -2586 -5603 \n",
"3 -408 -1326 -1734 \n",
"4 -62 -373 -572 \n",
"\n",
" bureau_CREDIT_DAY_OVERDUE_count bureau_CREDIT_DAY_OVERDUE_mean \\\n",
"0 7 0.0 \n",
"1 8 0.0 \n",
"2 4 0.0 \n",
"3 2 0.0 \n",
"4 3 0.0 \n",
"\n",
" bureau_CREDIT_DAY_OVERDUE_max bureau_CREDIT_DAY_OVERDUE_min \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" ... bureau_DAYS_CREDIT_UPDATE_count \\\n",
"0 ... 7 \n",
"1 ... 8 \n",
"2 ... 4 \n",
"3 ... 2 \n",
"4 ... 3 \n",
"\n",
" bureau_DAYS_CREDIT_UPDATE_mean bureau_DAYS_CREDIT_UPDATE_max \\\n",
"0 -93.142857 -6 \n",
"1 -499.875000 -7 \n",
"2 -816.000000 -43 \n",
"3 -532.000000 -382 \n",
"4 -54.333333 -11 \n",
"\n",
" bureau_DAYS_CREDIT_UPDATE_min bureau_DAYS_CREDIT_UPDATE_sum \\\n",
"0 -155 -652 \n",
"1 -1185 -3999 \n",
"2 -2131 -3264 \n",
"3 -682 -1064 \n",
"4 -121 -163 \n",
"\n",
" bureau_AMT_ANNUITY_count bureau_AMT_ANNUITY_mean bureau_AMT_ANNUITY_max \\\n",
"0 7 3545.357143 10822.5 \n",
"1 7 0.000000 0.0 \n",
"2 0 NaN NaN \n",
"3 0 NaN NaN \n",
"4 3 1420.500000 4261.5 \n",
"\n",
" bureau_AMT_ANNUITY_min bureau_AMT_ANNUITY_sum \n",
"0 0.0 24817.5 \n",
"1 0.0 0.0 \n",
"2 NaN 0.0 \n",
"3 NaN 0.0 \n",
"4 0.0 4261.5 \n",
"\n",
"[5 rows x 61 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bureau_agg.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"观察这些值可以发现它们是等价的，所以我们可以在其他数据集上重用该函数。通过使用函数减少工作量，是我们未来需要不断努力的方向"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 相关系数函数\n",
"在继续下一步之前，我们同样可以封装计算变量与目标值之间相关系数的函数"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# 计算数据集中变量与目标值之间相关系数的函数\n",
"def target_corrs(df):\n",
" \n",
" # 相关系数数组\n",
" corrs = []\n",
" \n",
" # 按列迭代\n",
" for col in df.columns:\n",
" print(col)\n",
" # 跳过目标列\n",
" if col != 'TARGET':\n",
" # 计算相关系数\n",
" corr = df['TARGET'].corr(df[col])\n",
" \n",
" # 按tuple类型插入数组中\n",
" corrs.append((col, corr))\n",
" \n",
" # 按照相关系数绝对值大小排序\n",
" corrs = sorted(corrs, key = lambda x: abs(x[1]), reverse = True)\n",
" \n",
" return corrs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 类别变量(Categorical Variables)\n",
"现在我们从数值列转向分类列。这些是离散型的字符串变量，所以我们无法计算均值或者最大值之类的统计量。相反，我们需要计算每个类别变量中类别的数量。举个例子，如果我们有以下的数据：\n",
"SK_ID_CURR\tLoan type\n",
"1\thome\n",
"1\thome\n",
"1\thome\n",
"1\tcredit\n",
"2\tcredit\n",
"3\tcredit\n",
"3\tcash\n",
"3\tcash\n",
"4\tcredit\n",
"4\thome\n",
"4\thome\n",
"我们将使用这些信息，计算每个客户在每个类别的贷款数量\n",
"SK_ID_CURR\tcredit count\tcash count\thome count\ttotal count\n",
"1\t1\t0\t3\t4\n",
"2\t1\t0\t0\t1\n",
"3\t1\t2\t0\t3\n",
"4\t1\t0\t2\t3\n",
"然后，我们可以通过该分类变量出现的总数来标准化这些数据（这意味着每行规范化后的数据总和必须为1.0）\n",
"SK_ID_CURR\tcredit count\tcash count\thome count\ttotal count\tcredit count norm\tcash count norm\thome count norm\n",
"1\t1\t0\t3\t4\t0.25\t0\t0.75\n",
"2\t1\t0\t0\t1\t1.00\t0\t0\n",
"3\t1\t2\t0\t3\t0.33\t0.66\t0\n",
"4\t1\t0\t2\t3\t0.33\t0\t0.66\n",
"希望用这种方式对类别变量进行编码可以获得它们所包含的信息。如果你们对这个过程有更好的想法，请在评论中告诉我！我们一步步进行这个过程，最后我将把所有的代码打包成一个函数，用于不同数据集的重用\n",
"\n",
"首先我们对分类列（dType＝=“object）的数据文件进行oen-hot编码"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
"\n",
"\n",
" \n",
" \n",
" CREDIT_ACTIVE_Active \n",
" CREDIT_ACTIVE_Bad debt \n",
" CREDIT_ACTIVE_Closed \n",
" CREDIT_ACTIVE_Sold \n",
" CREDIT_CURRENCY_currency 1 \n",
" CREDIT_CURRENCY_currency 2 \n",
" CREDIT_CURRENCY_currency 3 \n",
" CREDIT_CURRENCY_currency 4 \n",
" CREDIT_TYPE_Another type of loan \n",
" CREDIT_TYPE_Car loan \n",
" ... \n",
" CREDIT_TYPE_Loan for business development \n",
" CREDIT_TYPE_Loan for purchase of shares (margin lending) \n",
" CREDIT_TYPE_Loan for the purchase of equipment \n",
" CREDIT_TYPE_Loan for working capital replenishment \n",
" CREDIT_TYPE_Microloan \n",
" CREDIT_TYPE_Mobile operator loan \n",
" CREDIT_TYPE_Mortgage \n",
" CREDIT_TYPE_Real estate loan \n",
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""
],
"text/plain": [
" CREDIT_ACTIVE_Active CREDIT_ACTIVE_Bad debt CREDIT_ACTIVE_Closed \\\n",
"0 0 0 1 \n",
"1 1 0 0 \n",
"2 1 0 0 \n",
"3 1 0 0 \n",
"4 1 0 0 \n",
"\n",
" CREDIT_ACTIVE_Sold CREDIT_CURRENCY_currency 1 CREDIT_CURRENCY_currency 2 \\\n",
"0 0 1 0 \n",
"1 0 1 0 \n",
"2 0 1 0 \n",
"3 0 1 0 \n",
"4 0 1 0 \n",
"\n",
" CREDIT_CURRENCY_currency 3 CREDIT_CURRENCY_currency 4 \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" CREDIT_TYPE_Another type of loan CREDIT_TYPE_Car loan ... \\\n",
"0 0 0 ... \n",
"1 0 0 ... \n",
"2 0 0 ... \n",
"3 0 0 ... \n",
"4 0 0 ... \n",
"\n",
" CREDIT_TYPE_Loan for business development \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" CREDIT_TYPE_Loan for purchase of shares (margin lending) \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" CREDIT_TYPE_Loan for the purchase of equipment \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" CREDIT_TYPE_Loan for working capital replenishment CREDIT_TYPE_Microloan \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" CREDIT_TYPE_Mobile operator loan CREDIT_TYPE_Mortgage \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" CREDIT_TYPE_Real estate loan CREDIT_TYPE_Unknown type of loan SK_ID_CURR \n",
"0 0 0 215354 \n",
"1 0 0 215354 \n",
"2 0 0 215354 \n",
"3 0 0 215354 \n",
"4 0 0 215354 \n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 使用get_dummies进行one-hot编码，该方法会为列中的每个字符串创建一个新列\n",
"categorical = pd.get_dummies(bureau.select_dtypes('object'))\n",
"categorical['SK_ID_CURR'] = bureau['SK_ID_CURR']\n",
"categorical.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
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" ... \n",
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" CREDIT_TYPE_Mobile operator loan \n",
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" CREDIT_TYPE_Real estate loan \n",
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" 2 \n",
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" 0 \n",
" 0.0 \n",
" 3 \n",
" 1.0 \n",
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""
],
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" CREDIT_ACTIVE_Active CREDIT_ACTIVE_Bad debt \\\n",
" sum mean sum mean \n",
"SK_ID_CURR \n",
"100001 3 0.428571 0 0.0 \n",
"100002 2 0.250000 0 0.0 \n",
"100003 1 0.250000 0 0.0 \n",
"100004 0 0.000000 0 0.0 \n",
"100005 2 0.666667 0 0.0 \n",
"\n",
" CREDIT_ACTIVE_Closed CREDIT_ACTIVE_Sold \\\n",
" sum mean sum mean \n",
"SK_ID_CURR \n",
"100001 4 0.571429 0 0.0 \n",
"100002 6 0.750000 0 0.0 \n",
"100003 3 0.750000 0 0.0 \n",
"100004 2 1.000000 0 0.0 \n",
"100005 1 0.333333 0 0.0 \n",
"\n",
" CREDIT_CURRENCY_currency 1 ... CREDIT_TYPE_Microloan \\\n",
" sum mean ... sum mean \n",
"SK_ID_CURR ... \n",
"100001 7 1.0 ... 0 0.0 \n",
"100002 8 1.0 ... 0 0.0 \n",
"100003 4 1.0 ... 0 0.0 \n",
"100004 2 1.0 ... 0 0.0 \n",
"100005 3 1.0 ... 0 0.0 \n",
"\n",
" CREDIT_TYPE_Mobile operator loan CREDIT_TYPE_Mortgage \\\n",
" sum mean sum mean \n",
"SK_ID_CURR \n",
"100001 0 0.0 0 0.0 \n",
"100002 0 0.0 0 0.0 \n",
"100003 0 0.0 0 0.0 \n",
"100004 0 0.0 0 0.0 \n",
"100005 0 0.0 0 0.0 \n",
"\n",
" CREDIT_TYPE_Real estate loan CREDIT_TYPE_Unknown type of loan \\\n",
" sum mean sum \n",
"SK_ID_CURR \n",
"100001 0 0.0 0 \n",
"100002 0 0.0 0 \n",
"100003 0 0.0 0 \n",
"100004 0 0.0 0 \n",
"100005 0 0.0 0 \n",
"\n",
" \n",
" mean \n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
"[5 rows x 46 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"categorical_grouped = categorical.groupby('SK_ID_CURR').agg(['sum', 'mean'])\n",
"categorical_grouped.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"'sum'列表示的是与该类别相关联的客户总数，'mean'列表示的是其归一化计数。one-hot编码函数使得这些数值的计算变得十分容易\n",
"\n",
"我们可以像之前一样对列名进行重命名，同样的，我们需要处理列的多层索引问题。我们将第一级（0层）的分类变量名与one-hot编码后的值结合，迭代进行该过程。例如：sum列将会变成 CREDIT_ACTIVE_Active_count"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['CREDIT_ACTIVE_Active', 'CREDIT_ACTIVE_Bad debt',\n",
" 'CREDIT_ACTIVE_Closed', 'CREDIT_ACTIVE_Sold',\n",
" 'CREDIT_CURRENCY_currency 1', 'CREDIT_CURRENCY_currency 2',\n",
" 'CREDIT_CURRENCY_currency 3', 'CREDIT_CURRENCY_currency 4',\n",
" 'CREDIT_TYPE_Another type of loan', 'CREDIT_TYPE_Car loan'],\n",
" dtype='object')"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"categorical_grouped.columns.levels[0][:10]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['sum', 'mean'], dtype='object')"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"categorical_grouped.columns.levels[1]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" CREDIT_ACTIVE_Active_count \n",
" CREDIT_ACTIVE_Active_count_norm \n",
" CREDIT_ACTIVE_Bad debt_count \n",
" CREDIT_ACTIVE_Bad debt_count_norm \n",
" CREDIT_ACTIVE_Closed_count \n",
" CREDIT_ACTIVE_Closed_count_norm \n",
" CREDIT_ACTIVE_Sold_count \n",
" CREDIT_ACTIVE_Sold_count_norm \n",
" CREDIT_CURRENCY_currency 1_count \n",
" CREDIT_CURRENCY_currency 1_count_norm \n",
" ... \n",
" CREDIT_TYPE_Microloan_count \n",
" CREDIT_TYPE_Microloan_count_norm \n",
" CREDIT_TYPE_Mobile operator loan_count \n",
" CREDIT_TYPE_Mobile operator loan_count_norm \n",
" CREDIT_TYPE_Mortgage_count \n",
" CREDIT_TYPE_Mortgage_count_norm \n",
" CREDIT_TYPE_Real estate loan_count \n",
" CREDIT_TYPE_Real estate loan_count_norm \n",
" CREDIT_TYPE_Unknown type of loan_count \n",
" CREDIT_TYPE_Unknown type of loan_count_norm \n",
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" \n",
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""
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"text/plain": [
" CREDIT_ACTIVE_Active_count CREDIT_ACTIVE_Active_count_norm \\\n",
"SK_ID_CURR \n",
"100001 3 0.428571 \n",
"100002 2 0.250000 \n",
"100003 1 0.250000 \n",
"100004 0 0.000000 \n",
"100005 2 0.666667 \n",
"\n",
" CREDIT_ACTIVE_Bad debt_count CREDIT_ACTIVE_Bad debt_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0 0.0 \n",
"100002 0 0.0 \n",
"100003 0 0.0 \n",
"100004 0 0.0 \n",
"100005 0 0.0 \n",
"\n",
" CREDIT_ACTIVE_Closed_count CREDIT_ACTIVE_Closed_count_norm \\\n",
"SK_ID_CURR \n",
"100001 4 0.571429 \n",
"100002 6 0.750000 \n",
"100003 3 0.750000 \n",
"100004 2 1.000000 \n",
"100005 1 0.333333 \n",
"\n",
" CREDIT_ACTIVE_Sold_count CREDIT_ACTIVE_Sold_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0 0.0 \n",
"100002 0 0.0 \n",
"100003 0 0.0 \n",
"100004 0 0.0 \n",
"100005 0 0.0 \n",
"\n",
" CREDIT_CURRENCY_currency 1_count \\\n",
"SK_ID_CURR \n",
"100001 7 \n",
"100002 8 \n",
"100003 4 \n",
"100004 2 \n",
"100005 3 \n",
"\n",
" CREDIT_CURRENCY_currency 1_count_norm \\\n",
"SK_ID_CURR \n",
"100001 1.0 \n",
"100002 1.0 \n",
"100003 1.0 \n",
"100004 1.0 \n",
"100005 1.0 \n",
"\n",
" ... \\\n",
"SK_ID_CURR ... \n",
"100001 ... \n",
"100002 ... \n",
"100003 ... \n",
"100004 ... \n",
"100005 ... \n",
"\n",
" CREDIT_TYPE_Microloan_count CREDIT_TYPE_Microloan_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0 0.0 \n",
"100002 0 0.0 \n",
"100003 0 0.0 \n",
"100004 0 0.0 \n",
"100005 0 0.0 \n",
"\n",
" CREDIT_TYPE_Mobile operator loan_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" CREDIT_TYPE_Mobile operator loan_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" CREDIT_TYPE_Mortgage_count CREDIT_TYPE_Mortgage_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0 0.0 \n",
"100002 0 0.0 \n",
"100003 0 0.0 \n",
"100004 0 0.0 \n",
"100005 0 0.0 \n",
"\n",
" CREDIT_TYPE_Real estate loan_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" CREDIT_TYPE_Real estate loan_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" CREDIT_TYPE_Unknown type of loan_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" CREDIT_TYPE_Unknown type of loan_count_norm \n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
"[5 rows x 46 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"group_var = 'SK_ID_CURR'\n",
"\n",
"# 新建的列名\n",
"columns = []\n",
"\n",
"# 迭代变量名\n",
"for var in categorical_grouped.columns.levels[0]:\n",
" # 忽略分组变量\n",
" if var != group_var:\n",
" # 迭代统计量名称sum、mean\n",
" for stat in ['count', 'count_norm']:\n",
" columns.append('%s_%s' % (var, stat))\n",
"\n",
"# 重命名列\n",
"categorical_grouped.columns = columns\n",
"\n",
"categorical_grouped.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"'sum'列记录了客户总数，'mean'列记录了归一化后的值\n",
"\n",
"我们可以将该数据合并到训练数据中"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" FLAG_OWN_CAR \n",
" FLAG_OWN_REALTY \n",
" CNT_CHILDREN \n",
" AMT_INCOME_TOTAL \n",
" AMT_CREDIT \n",
" AMT_ANNUITY \n",
" ... \n",
" CREDIT_TYPE_Microloan_count \n",
" CREDIT_TYPE_Microloan_count_norm \n",
" CREDIT_TYPE_Mobile operator loan_count \n",
" CREDIT_TYPE_Mobile operator loan_count_norm \n",
" CREDIT_TYPE_Mortgage_count \n",
" CREDIT_TYPE_Mortgage_count_norm \n",
" CREDIT_TYPE_Real estate loan_count \n",
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""
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"text/plain": [
" SK_ID_CURR TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR \\\n",
"0 100002 1 Cash loans M N \n",
"1 100003 0 Cash loans F N \n",
"2 100004 0 Revolving loans M Y \n",
"3 100006 0 Cash loans F N \n",
"4 100007 0 Cash loans M N \n",
"\n",
" FLAG_OWN_REALTY CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY \\\n",
"0 Y 0 202500.0 406597.5 24700.5 \n",
"1 N 0 270000.0 1293502.5 35698.5 \n",
"2 Y 0 67500.0 135000.0 6750.0 \n",
"3 Y 0 135000.0 312682.5 29686.5 \n",
"4 Y 0 121500.0 513000.0 21865.5 \n",
"\n",
" ... CREDIT_TYPE_Microloan_count \\\n",
"0 ... 0.0 \n",
"1 ... 0.0 \n",
"2 ... 0.0 \n",
"3 ... NaN \n",
"4 ... 0.0 \n",
"\n",
" CREDIT_TYPE_Microloan_count_norm CREDIT_TYPE_Mobile operator loan_count \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
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"4 0.0 0.0 \n",
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"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 NaN NaN \n",
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"\n",
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]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = train.merge(categorical_grouped, left_on = 'SK_ID_CURR', right_index =True, how = 'left')\n",
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
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"source": [
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]
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{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
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" -239.0 \n",
" -2882.0 \n",
" -22887.0 \n",
" 18.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" ... \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" \n",
" \n",
" 7 \n",
" 2.0 \n",
" -1939.500000 \n",
" -1138.0 \n",
" -2741.0 \n",
" -3879.0 \n",
" 2.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" ... \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" \n",
" \n",
" 8 \n",
" 4.0 \n",
" -1773.000000 \n",
" -1309.0 \n",
" -2508.0 \n",
" -7092.0 \n",
" 4.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" ... \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" 0.0 \n",
" \n",
" \n",
" 9 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" ... \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" \n",
" \n",
"
\n",
"10 rows × 106 columns
\n",
""
],
"text/plain": [
" bureau_DAYS_CREDIT_count bureau_DAYS_CREDIT_mean bureau_DAYS_CREDIT_max \\\n",
"0 8.0 -874.000000 -103.0 \n",
"1 4.0 -1400.750000 -606.0 \n",
"2 2.0 -867.000000 -408.0 \n",
"3 NaN NaN NaN \n",
"4 1.0 -1149.000000 -1149.0 \n",
"5 3.0 -757.333333 -78.0 \n",
"6 18.0 -1271.500000 -239.0 \n",
"7 2.0 -1939.500000 -1138.0 \n",
"8 4.0 -1773.000000 -1309.0 \n",
"9 NaN NaN NaN \n",
"\n",
" bureau_DAYS_CREDIT_min bureau_DAYS_CREDIT_sum \\\n",
"0 -1437.0 -6992.0 \n",
"1 -2586.0 -5603.0 \n",
"2 -1326.0 -1734.0 \n",
"3 NaN NaN \n",
"4 -1149.0 -1149.0 \n",
"5 -1097.0 -2272.0 \n",
"6 -2882.0 -22887.0 \n",
"7 -2741.0 -3879.0 \n",
"8 -2508.0 -7092.0 \n",
"9 NaN NaN \n",
"\n",
" bureau_CREDIT_DAY_OVERDUE_count bureau_CREDIT_DAY_OVERDUE_mean \\\n",
"0 8.0 0.0 \n",
"1 4.0 0.0 \n",
"2 2.0 0.0 \n",
"3 NaN NaN \n",
"4 1.0 0.0 \n",
"5 3.0 0.0 \n",
"6 18.0 0.0 \n",
"7 2.0 0.0 \n",
"8 4.0 0.0 \n",
"9 NaN NaN \n",
"\n",
" bureau_CREDIT_DAY_OVERDUE_max bureau_CREDIT_DAY_OVERDUE_min \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 NaN NaN \n",
"4 0.0 0.0 \n",
"5 0.0 0.0 \n",
"6 0.0 0.0 \n",
"7 0.0 0.0 \n",
"8 0.0 0.0 \n",
"9 NaN NaN \n",
"\n",
" bureau_CREDIT_DAY_OVERDUE_sum ... \\\n",
"0 0.0 ... \n",
"1 0.0 ... \n",
"2 0.0 ... \n",
"3 NaN ... \n",
"4 0.0 ... \n",
"5 0.0 ... \n",
"6 0.0 ... \n",
"7 0.0 ... \n",
"8 0.0 ... \n",
"9 NaN ... \n",
"\n",
" CREDIT_TYPE_Microloan_count CREDIT_TYPE_Microloan_count_norm \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 NaN NaN \n",
"4 0.0 0.0 \n",
"5 0.0 0.0 \n",
"6 0.0 0.0 \n",
"7 0.0 0.0 \n",
"8 0.0 0.0 \n",
"9 NaN NaN \n",
"\n",
" CREDIT_TYPE_Mobile operator loan_count \\\n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 NaN \n",
"4 0.0 \n",
"5 0.0 \n",
"6 0.0 \n",
"7 0.0 \n",
"8 0.0 \n",
"9 NaN \n",
"\n",
" CREDIT_TYPE_Mobile operator loan_count_norm CREDIT_TYPE_Mortgage_count \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 NaN NaN \n",
"4 0.0 0.0 \n",
"5 0.0 0.0 \n",
"6 0.0 0.0 \n",
"7 0.0 0.0 \n",
"8 0.0 0.0 \n",
"9 NaN NaN \n",
"\n",
" CREDIT_TYPE_Mortgage_count_norm CREDIT_TYPE_Real estate loan_count \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 NaN NaN \n",
"4 0.0 0.0 \n",
"5 0.0 0.0 \n",
"6 0.0 0.0 \n",
"7 0.0 0.0 \n",
"8 0.0 0.0 \n",
"9 NaN NaN \n",
"\n",
" CREDIT_TYPE_Real estate loan_count_norm \\\n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 NaN \n",
"4 0.0 \n",
"5 0.0 \n",
"6 0.0 \n",
"7 0.0 \n",
"8 0.0 \n",
"9 NaN \n",
"\n",
" CREDIT_TYPE_Unknown type of loan_count \\\n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 NaN \n",
"4 0.0 \n",
"5 0.0 \n",
"6 0.0 \n",
"7 0.0 \n",
"8 0.0 \n",
"9 NaN \n",
"\n",
" CREDIT_TYPE_Unknown type of loan_count_norm \n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 NaN \n",
"4 0.0 \n",
"5 0.0 \n",
"6 0.0 \n",
"7 0.0 \n",
"8 0.0 \n",
"9 NaN \n",
"\n",
"[10 rows x 106 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.iloc[:10, 123:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"类别变量处理函数\n",
"为了使代码更加高效，我们同样对上述方法进行封装，用于处理数据中的类别变量。与agg_numeric函数相同，该函数接受一个数据文件(dataframe)和分组变量(grouping variable)，然后计算数据文件中所有类别变量每个类别的总数和归一化后的数值"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"def count_categorical(df, group_var, df_name):\n",
" \"\"\"计算数据文件中所有类别变量每个类别的总数和归一化后的数值\n",
" \n",
" 参数\n",
" --------\n",
" df : dataframe \n",
" 数据文件\n",
" \n",
" group_var : string\n",
" 用于将数据文件分组的变量\n",
" 对于该变量中的每一个值，最终的数据集中都会存在一行\n",
" \n",
" df_name : string\n",
" 用于重命名列名的变量\n",
"\n",
" \n",
" 返回值\n",
" --------\n",
" categorical : dataframe\n",
" A返回一个包含类别变量每个类别的总数和归一化后的数值的数据集(dataframe)\n",
" \n",
" \"\"\"\n",
" \n",
" # 选择分类列，并对其做one-hot处理\n",
" categorical = pd.get_dummies(df.select_dtypes('object'))\n",
" \n",
" # 确保为分类列添加标识\n",
" categorical[group_var] = df[group_var]\n",
" \n",
" # 按照分类变量分类，计算总和sum和均值mean\n",
" categorical = categorical.groupby(group_var).agg(['sum', 'mean'])\n",
" \n",
" column_names = []\n",
" \n",
" # 迭代变量名\n",
" for var in categorical.columns.levels[0]:\n",
" # 迭代统计量名称sum、mean\n",
" for stat in ['count', 'count_norm']:\n",
" column_names.append('%s_%s_%s' % (df_name, var, stat))\n",
" \n",
" categorical.columns = column_names\n",
" \n",
" return categorical"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" bureau_CREDIT_ACTIVE_Active_count \n",
" bureau_CREDIT_ACTIVE_Active_count_norm \n",
" bureau_CREDIT_ACTIVE_Bad debt_count \n",
" bureau_CREDIT_ACTIVE_Bad debt_count_norm \n",
" bureau_CREDIT_ACTIVE_Closed_count \n",
" bureau_CREDIT_ACTIVE_Closed_count_norm \n",
" bureau_CREDIT_ACTIVE_Sold_count \n",
" bureau_CREDIT_ACTIVE_Sold_count_norm \n",
" bureau_CREDIT_CURRENCY_currency 1_count \n",
" bureau_CREDIT_CURRENCY_currency 1_count_norm \n",
" ... \n",
" bureau_CREDIT_TYPE_Microloan_count \n",
" bureau_CREDIT_TYPE_Microloan_count_norm \n",
" bureau_CREDIT_TYPE_Mobile operator loan_count \n",
" bureau_CREDIT_TYPE_Mobile operator loan_count_norm \n",
" bureau_CREDIT_TYPE_Mortgage_count \n",
" bureau_CREDIT_TYPE_Mortgage_count_norm \n",
" bureau_CREDIT_TYPE_Real estate loan_count \n",
" bureau_CREDIT_TYPE_Real estate loan_count_norm \n",
" bureau_CREDIT_TYPE_Unknown type of loan_count \n",
" bureau_CREDIT_TYPE_Unknown type of loan_count_norm \n",
" \n",
" \n",
" SK_ID_CURR \n",
" \n",
" \n",
" \n",
" \n",
" 100001 \n",
" 3 \n",
" 0.428571 \n",
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" 100004 \n",
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" \n",
" \n",
"
\n",
"5 rows × 46 columns
\n",
""
],
"text/plain": [
" bureau_CREDIT_ACTIVE_Active_count \\\n",
"SK_ID_CURR \n",
"100001 3 \n",
"100002 2 \n",
"100003 1 \n",
"100004 0 \n",
"100005 2 \n",
"\n",
" bureau_CREDIT_ACTIVE_Active_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.428571 \n",
"100002 0.250000 \n",
"100003 0.250000 \n",
"100004 0.000000 \n",
"100005 0.666667 \n",
"\n",
" bureau_CREDIT_ACTIVE_Bad debt_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_ACTIVE_Bad debt_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_ACTIVE_Closed_count \\\n",
"SK_ID_CURR \n",
"100001 4 \n",
"100002 6 \n",
"100003 3 \n",
"100004 2 \n",
"100005 1 \n",
"\n",
" bureau_CREDIT_ACTIVE_Closed_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.571429 \n",
"100002 0.750000 \n",
"100003 0.750000 \n",
"100004 1.000000 \n",
"100005 0.333333 \n",
"\n",
" bureau_CREDIT_ACTIVE_Sold_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_ACTIVE_Sold_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_CURRENCY_currency 1_count \\\n",
"SK_ID_CURR \n",
"100001 7 \n",
"100002 8 \n",
"100003 4 \n",
"100004 2 \n",
"100005 3 \n",
"\n",
" bureau_CREDIT_CURRENCY_currency 1_count_norm \\\n",
"SK_ID_CURR \n",
"100001 1.0 \n",
"100002 1.0 \n",
"100003 1.0 \n",
"100004 1.0 \n",
"100005 1.0 \n",
"\n",
" ... \\\n",
"SK_ID_CURR ... \n",
"100001 ... \n",
"100002 ... \n",
"100003 ... \n",
"100004 ... \n",
"100005 ... \n",
"\n",
" bureau_CREDIT_TYPE_Microloan_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_TYPE_Microloan_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_TYPE_Mobile operator loan_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_TYPE_Mobile operator loan_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_TYPE_Mortgage_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_TYPE_Mortgage_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_TYPE_Real estate loan_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_TYPE_Real estate loan_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_TYPE_Unknown type of loan_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_TYPE_Unknown type of loan_count_norm \n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
"[5 rows x 46 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bureau_counts = count_categorical(bureau, group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
"bureau_counts.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 操作bureau_balance数据文件\n",
"我们现在转向处理bureau balance文件。这个数据文件中包含了每个客户先前向其他金融机构贷款的月度信息。我们首先按SK_ID_BUREAU（先前的‘贷款ID’）对数据文件进行分组，而不是SK_ID_CURR（当前贷款的‘客户ID’）。通过这样的处理，每一条贷款记录都表示为单独的一行。然后我们可以通过SK_ID_CURR进行分组，并计算每个客户的贷款总额。最终处理的结果是生成一个数据集，其中一行表示一个客户及其贷款的相关统计数据\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" SK_ID_BUREAU \n",
" MONTHS_BALANCE \n",
" STATUS \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 5715448 \n",
" 0 \n",
" C \n",
" \n",
" \n",
" 1 \n",
" 5715448 \n",
" -1 \n",
" C \n",
" \n",
" \n",
" 2 \n",
" 5715448 \n",
" -2 \n",
" C \n",
" \n",
" \n",
" 3 \n",
" 5715448 \n",
" -3 \n",
" C \n",
" \n",
" \n",
" 4 \n",
" 5715448 \n",
" -4 \n",
" C \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" SK_ID_BUREAU MONTHS_BALANCE STATUS\n",
"0 5715448 0 C\n",
"1 5715448 -1 C\n",
"2 5715448 -2 C\n",
"3 5715448 -3 C\n",
"4 5715448 -4 C"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 读取bureau_balance数据文件\n",
"bureau_balance = pd.read_csv('bureau_balance.csv')\n",
"bureau_balance.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"首先，我们可以计算每个贷款每种状态的数量。幸运的是，我们已经有了这样一个函数"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" bureau_balance_STATUS_0_count \n",
" bureau_balance_STATUS_0_count_norm \n",
" bureau_balance_STATUS_1_count \n",
" bureau_balance_STATUS_1_count_norm \n",
" bureau_balance_STATUS_2_count \n",
" bureau_balance_STATUS_2_count_norm \n",
" bureau_balance_STATUS_3_count \n",
" bureau_balance_STATUS_3_count_norm \n",
" bureau_balance_STATUS_4_count \n",
" bureau_balance_STATUS_4_count_norm \n",
" bureau_balance_STATUS_5_count \n",
" bureau_balance_STATUS_5_count_norm \n",
" bureau_balance_STATUS_C_count \n",
" bureau_balance_STATUS_C_count_norm \n",
" bureau_balance_STATUS_X_count \n",
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" bureau_balance_STATUS_0_count \\\n",
"SK_ID_BUREAU \n",
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" bureau_balance_STATUS_0_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.000000 \n",
"5001710 0.060241 \n",
"5001711 0.750000 \n",
"5001712 0.526316 \n",
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"\n",
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"SK_ID_BUREAU \n",
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"5001711 0 \n",
"5001712 0 \n",
"5001713 0 \n",
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" bureau_balance_STATUS_1_count_norm \\\n",
"SK_ID_BUREAU \n",
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"5001710 0.0 \n",
"5001711 0.0 \n",
"5001712 0.0 \n",
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" bureau_balance_STATUS_2_count \\\n",
"SK_ID_BUREAU \n",
"5001709 0 \n",
"5001710 0 \n",
"5001711 0 \n",
"5001712 0 \n",
"5001713 0 \n",
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" bureau_balance_STATUS_2_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.0 \n",
"5001710 0.0 \n",
"5001711 0.0 \n",
"5001712 0.0 \n",
"5001713 0.0 \n",
"\n",
" bureau_balance_STATUS_3_count \\\n",
"SK_ID_BUREAU \n",
"5001709 0 \n",
"5001710 0 \n",
"5001711 0 \n",
"5001712 0 \n",
"5001713 0 \n",
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" bureau_balance_STATUS_3_count_norm \\\n",
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"5001710 0.0 \n",
"5001711 0.0 \n",
"5001712 0.0 \n",
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" bureau_balance_STATUS_4_count \\\n",
"SK_ID_BUREAU \n",
"5001709 0 \n",
"5001710 0 \n",
"5001711 0 \n",
"5001712 0 \n",
"5001713 0 \n",
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" bureau_balance_STATUS_4_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.0 \n",
"5001710 0.0 \n",
"5001711 0.0 \n",
"5001712 0.0 \n",
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"\n",
" bureau_balance_STATUS_5_count \\\n",
"SK_ID_BUREAU \n",
"5001709 0 \n",
"5001710 0 \n",
"5001711 0 \n",
"5001712 0 \n",
"5001713 0 \n",
"\n",
" bureau_balance_STATUS_5_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.0 \n",
"5001710 0.0 \n",
"5001711 0.0 \n",
"5001712 0.0 \n",
"5001713 0.0 \n",
"\n",
" bureau_balance_STATUS_C_count \\\n",
"SK_ID_BUREAU \n",
"5001709 86 \n",
"5001710 48 \n",
"5001711 0 \n",
"5001712 9 \n",
"5001713 0 \n",
"\n",
" bureau_balance_STATUS_C_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.886598 \n",
"5001710 0.578313 \n",
"5001711 0.000000 \n",
"5001712 0.473684 \n",
"5001713 0.000000 \n",
"\n",
" bureau_balance_STATUS_X_count \\\n",
"SK_ID_BUREAU \n",
"5001709 11 \n",
"5001710 30 \n",
"5001711 1 \n",
"5001712 0 \n",
"5001713 22 \n",
"\n",
" bureau_balance_STATUS_X_count_norm \n",
"SK_ID_BUREAU \n",
"5001709 0.113402 \n",
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]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 计算每个贷款每种状态的数量\n",
"bureau_balance_counts = count_categorical(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
"bureau_balance_counts.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"现在我们可以处理一个数值列。MONTHS_BALANCE列包含着相对于申请日期的'月度余额'，这并不一定是一个重要的数值变量，之后我们会考虑将它看做时间变量，但是现在，我们只能按照数值列对它进行与之前相同的聚合统计"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
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"text/plain": [
" SK_ID_BUREAU bureau_balance_MONTHS_BALANCE_count \\\n",
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"2 5001711 4 \n",
"3 5001712 19 \n",
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},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 根据'SK_ID_CURR’进行数值统计\n",
"bureau_balance_agg = agg_numeric(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
"bureau_balance_agg.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"上面的数据对每个贷款都进行了计算。现在需要将这些数据和相应的客户结合在一起。首先将数据文件合并在一起，因为所有变量都是数值的，所以只需按SK_ID_CURR分组，再次聚合统计数据"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
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"0 -48.0 0 \n",
"1 -41.0 0 \n",
"2 -1.5 0 \n",
"3 -9.0 0 \n",
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"0 -96 -4656 \n",
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"0 86 0.886598 \n",
"1 48 0.578313 \n",
"2 0 0.000000 \n",
"3 9 0.473684 \n",
"4 0 0.000000 \n",
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"0 11 0.113402 \n",
"1 30 0.361446 \n",
"2 1 0.250000 \n",
"3 0 0.000000 \n",
"4 22 1.000000 \n",
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]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 按照贷款将数据文件分组\n",
"bureau_by_loan = bureau_balance_agg.merge(bureau_balance_counts, right_index = True, left_on = 'SK_ID_BUREAU', how = 'outer')\n",
"\n",
"# 合并（包括SK_ID_CURR）\n",
"bureau_by_loan = bureau_by_loan.merge(bureau[['SK_ID_BUREAU', 'SK_ID_CURR']], on = 'SK_ID_BUREAU', how = 'left')\n",
"# how = left 只保留左表的所有数据，如果右表的SK_ID_BUREAU列中出现了左表中没有的值，那么该值所对应的行数据会被舍弃(right同理)\n",
"bureau_by_loan.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
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"2 13 \n",
"3 36 \n",
"4 69 \n",
"\n",
" client_bureau_balance_MONTHS_BALANCE_count_min \\\n",
"0 2 \n",
"1 4 \n",
"2 3 \n",
"3 36 \n",
"4 40 \n",
"\n",
" client_bureau_balance_MONTHS_BALANCE_count_sum \\\n",
"0 172 \n",
"1 110 \n",
"2 21 \n",
"3 72 \n",
"4 230 \n",
"\n",
" client_bureau_balance_MONTHS_BALANCE_mean_count \\\n",
"0 7 \n",
"1 8 \n",
"2 3 \n",
"3 2 \n",
"4 4 \n",
"\n",
" client_bureau_balance_MONTHS_BALANCE_mean_mean \\\n",
"0 -11.785714 \n",
"1 -21.875000 \n",
"2 -3.000000 \n",
"3 -46.000000 \n",
"4 -28.250000 \n",
"\n",
" client_bureau_balance_MONTHS_BALANCE_mean_max \\\n",
"0 -0.5 \n",
"1 -1.5 \n",
"2 -1.0 \n",
"3 -19.5 \n",
"4 -19.5 \n",
"\n",
" client_bureau_balance_MONTHS_BALANCE_mean_min \\\n",
"0 -25.5 \n",
"1 -39.5 \n",
"2 -6.0 \n",
"3 -72.5 \n",
"4 -34.0 \n",
"\n",
" ... \\\n",
"0 ... \n",
"1 ... \n",
"2 ... \n",
"3 ... \n",
"4 ... \n",
"\n",
" client_bureau_balance_STATUS_X_count_count \\\n",
"0 7 \n",
"1 8 \n",
"2 3 \n",
"3 2 \n",
"4 4 \n",
"\n",
" client_bureau_balance_STATUS_X_count_mean \\\n",
"0 4.285714 \n",
"1 1.875000 \n",
"2 0.666667 \n",
"3 0.000000 \n",
"4 10.250000 \n",
"\n",
" client_bureau_balance_STATUS_X_count_max \\\n",
"0 9 \n",
"1 3 \n",
"2 1 \n",
"3 0 \n",
"4 40 \n",
"\n",
" client_bureau_balance_STATUS_X_count_min \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" client_bureau_balance_STATUS_X_count_sum \\\n",
"0 30.0 \n",
"1 15.0 \n",
"2 2.0 \n",
"3 0.0 \n",
"4 41.0 \n",
"\n",
" client_bureau_balance_STATUS_X_count_norm_count \\\n",
"0 7 \n",
"1 8 \n",
"2 3 \n",
"3 2 \n",
"4 4 \n",
"\n",
" client_bureau_balance_STATUS_X_count_norm_mean \\\n",
"0 0.214590 \n",
"1 0.161932 \n",
"2 0.136752 \n",
"3 0.000000 \n",
"4 0.254545 \n",
"\n",
" client_bureau_balance_STATUS_X_count_norm_max \\\n",
"0 0.500000 \n",
"1 0.500000 \n",
"2 0.333333 \n",
"3 0.000000 \n",
"4 1.000000 \n",
"\n",
" client_bureau_balance_STATUS_X_count_norm_min \\\n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 0.0 \n",
"4 0.0 \n",
"\n",
" client_bureau_balance_STATUS_X_count_norm_sum \n",
"0 1.502129 \n",
"1 1.295455 \n",
"2 0.410256 \n",
"3 0.000000 \n",
"4 1.018182 \n",
"\n",
"[5 rows x 106 columns]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bureau_balance_by_client = agg_numeric(bureau_by_loan.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'client')\n",
"bureau_balance_by_client.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"总而言之，对于bureau_balance数据文件，我们做了以下处理：\n",
"\n",
"1、计算分组后贷款数据的相关统计量(各个status的贷款数量)\n",
"2、对按贷款分类后的每个类别变量(categorical variable)进行相关统计量计算\n",
"3、合并训练数据和计算得到的贷款统计量\n",
"4、对数据集按客户id进行分组，再计算数值型数据\n",
"\n",
"最终得到的数据，每一行代表一个客户，其中的统计信息是针对该客户所有贷款计算的，并带有月度余额信息\n",
"\n",
"一些变量有点混乱，所以需要解释一下：\n",
"client_bureau_balance_MONTHS_BALANCE_mean_mean：计算MONTHS_BALANCE中每一笔贷款的平均值。然后为每个客户计算其所有贷款中该数值的平均值\n",
"client_bureau_balance_STATUS_X_count_norm_sum：对每个贷款统计STATUS==X的发生次数，并除以贷款的总STATUS值。然后，为每个客户计算其所有贷款中该数值的总和\n",
"\n",
"在我们将所有变量组合到一个数据集之前，先不计算变量的相关系数"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 功能集成\n",
"我们已经准备好从其他金融机构的历史贷款数据和这些贷款的月偿还数据中获取信息，并将这些信息与训练数据集结合。我们重新设置所有变量，然后使用我们所构建的函数来完成该工作，这个过程展示了复用的好处"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"112"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 删除旧对象释放内存\n",
"import gc\n",
"gc.enable()\n",
"del train, bureau, bureau_balance, bureau_agg, bureau_agg_new, bureau_balance_agg, bureau_balance_counts, bureau_by_loan, bureau_balance_by_client, bureau_counts\n",
"gc.collect()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"# 从所有数据集中读取新的副本\n",
"train = pd.read_csv('application_train.csv')\n",
"bureau = pd.read_csv('bureau.csv')\n",
"bureau_balance = pd.read_csv('bureau_balance.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bureau数据集计数"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" bureau_CREDIT_ACTIVE_Active_count \n",
" bureau_CREDIT_ACTIVE_Active_count_norm \n",
" bureau_CREDIT_ACTIVE_Bad debt_count \n",
" bureau_CREDIT_ACTIVE_Bad debt_count_norm \n",
" bureau_CREDIT_ACTIVE_Closed_count \n",
" bureau_CREDIT_ACTIVE_Closed_count_norm \n",
" bureau_CREDIT_ACTIVE_Sold_count \n",
" bureau_CREDIT_ACTIVE_Sold_count_norm \n",
" bureau_CREDIT_CURRENCY_currency 1_count \n",
" bureau_CREDIT_CURRENCY_currency 1_count_norm \n",
" ... \n",
" bureau_CREDIT_TYPE_Microloan_count \n",
" bureau_CREDIT_TYPE_Microloan_count_norm \n",
" bureau_CREDIT_TYPE_Mobile operator loan_count \n",
" bureau_CREDIT_TYPE_Mobile operator loan_count_norm \n",
" bureau_CREDIT_TYPE_Mortgage_count \n",
" bureau_CREDIT_TYPE_Mortgage_count_norm \n",
" bureau_CREDIT_TYPE_Real estate loan_count \n",
" bureau_CREDIT_TYPE_Real estate loan_count_norm \n",
" bureau_CREDIT_TYPE_Unknown type of loan_count \n",
" bureau_CREDIT_TYPE_Unknown type of loan_count_norm \n",
" \n",
" \n",
" SK_ID_CURR \n",
" \n",
" \n",
" \n",
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" 100001 \n",
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" 100003 \n",
" 1 \n",
" 0.250000 \n",
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" 3 \n",
" 0.750000 \n",
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""
],
"text/plain": [
" bureau_CREDIT_ACTIVE_Active_count \\\n",
"SK_ID_CURR \n",
"100001 3 \n",
"100002 2 \n",
"100003 1 \n",
"100004 0 \n",
"100005 2 \n",
"\n",
" bureau_CREDIT_ACTIVE_Active_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.428571 \n",
"100002 0.250000 \n",
"100003 0.250000 \n",
"100004 0.000000 \n",
"100005 0.666667 \n",
"\n",
" bureau_CREDIT_ACTIVE_Bad debt_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_ACTIVE_Bad debt_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_ACTIVE_Closed_count \\\n",
"SK_ID_CURR \n",
"100001 4 \n",
"100002 6 \n",
"100003 3 \n",
"100004 2 \n",
"100005 1 \n",
"\n",
" bureau_CREDIT_ACTIVE_Closed_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.571429 \n",
"100002 0.750000 \n",
"100003 0.750000 \n",
"100004 1.000000 \n",
"100005 0.333333 \n",
"\n",
" bureau_CREDIT_ACTIVE_Sold_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_ACTIVE_Sold_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_CURRENCY_currency 1_count \\\n",
"SK_ID_CURR \n",
"100001 7 \n",
"100002 8 \n",
"100003 4 \n",
"100004 2 \n",
"100005 3 \n",
"\n",
" bureau_CREDIT_CURRENCY_currency 1_count_norm \\\n",
"SK_ID_CURR \n",
"100001 1.0 \n",
"100002 1.0 \n",
"100003 1.0 \n",
"100004 1.0 \n",
"100005 1.0 \n",
"\n",
" ... \\\n",
"SK_ID_CURR ... \n",
"100001 ... \n",
"100002 ... \n",
"100003 ... \n",
"100004 ... \n",
"100005 ... \n",
"\n",
" bureau_CREDIT_TYPE_Microloan_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_TYPE_Microloan_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_TYPE_Mobile operator loan_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_TYPE_Mobile operator loan_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_TYPE_Mortgage_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_TYPE_Mortgage_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_TYPE_Real estate loan_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_TYPE_Real estate loan_count_norm \\\n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
" bureau_CREDIT_TYPE_Unknown type of loan_count \\\n",
"SK_ID_CURR \n",
"100001 0 \n",
"100002 0 \n",
"100003 0 \n",
"100004 0 \n",
"100005 0 \n",
"\n",
" bureau_CREDIT_TYPE_Unknown type of loan_count_norm \n",
"SK_ID_CURR \n",
"100001 0.0 \n",
"100002 0.0 \n",
"100003 0.0 \n",
"100004 0.0 \n",
"100005 0.0 \n",
"\n",
"[5 rows x 46 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bureau_counts = count_categorical(bureau, group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
"bureau_counts.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bureau数据集聚合（计算mean、max、min、sum等统计量）"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" SK_ID_CURR \n",
" bureau_DAYS_CREDIT_count \n",
" bureau_DAYS_CREDIT_mean \n",
" bureau_DAYS_CREDIT_max \n",
" bureau_DAYS_CREDIT_min \n",
" bureau_DAYS_CREDIT_sum \n",
" bureau_CREDIT_DAY_OVERDUE_count \n",
" bureau_CREDIT_DAY_OVERDUE_mean \n",
" bureau_CREDIT_DAY_OVERDUE_max \n",
" bureau_CREDIT_DAY_OVERDUE_min \n",
" ... \n",
" bureau_DAYS_CREDIT_UPDATE_count \n",
" bureau_DAYS_CREDIT_UPDATE_mean \n",
" bureau_DAYS_CREDIT_UPDATE_max \n",
" bureau_DAYS_CREDIT_UPDATE_min \n",
" bureau_DAYS_CREDIT_UPDATE_sum \n",
" bureau_AMT_ANNUITY_count \n",
" bureau_AMT_ANNUITY_mean \n",
" bureau_AMT_ANNUITY_max \n",
" bureau_AMT_ANNUITY_min \n",
" bureau_AMT_ANNUITY_sum \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 100001 \n",
" 7 \n",
" -735.000000 \n",
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" \n",
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" \n",
" 2 \n",
" 100003 \n",
" 4 \n",
" -1400.750000 \n",
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" -2586 \n",
" -5603 \n",
" 4 \n",
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" \n",
" \n",
"
\n",
"5 rows × 61 columns
\n",
""
],
"text/plain": [
" SK_ID_CURR bureau_DAYS_CREDIT_count bureau_DAYS_CREDIT_mean \\\n",
"0 100001 7 -735.000000 \n",
"1 100002 8 -874.000000 \n",
"2 100003 4 -1400.750000 \n",
"3 100004 2 -867.000000 \n",
"4 100005 3 -190.666667 \n",
"\n",
" bureau_DAYS_CREDIT_max bureau_DAYS_CREDIT_min bureau_DAYS_CREDIT_sum \\\n",
"0 -49 -1572 -5145 \n",
"1 -103 -1437 -6992 \n",
"2 -606 -2586 -5603 \n",
"3 -408 -1326 -1734 \n",
"4 -62 -373 -572 \n",
"\n",
" bureau_CREDIT_DAY_OVERDUE_count bureau_CREDIT_DAY_OVERDUE_mean \\\n",
"0 7 0.0 \n",
"1 8 0.0 \n",
"2 4 0.0 \n",
"3 2 0.0 \n",
"4 3 0.0 \n",
"\n",
" bureau_CREDIT_DAY_OVERDUE_max bureau_CREDIT_DAY_OVERDUE_min \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" ... bureau_DAYS_CREDIT_UPDATE_count \\\n",
"0 ... 7 \n",
"1 ... 8 \n",
"2 ... 4 \n",
"3 ... 2 \n",
"4 ... 3 \n",
"\n",
" bureau_DAYS_CREDIT_UPDATE_mean bureau_DAYS_CREDIT_UPDATE_max \\\n",
"0 -93.142857 -6 \n",
"1 -499.875000 -7 \n",
"2 -816.000000 -43 \n",
"3 -532.000000 -382 \n",
"4 -54.333333 -11 \n",
"\n",
" bureau_DAYS_CREDIT_UPDATE_min bureau_DAYS_CREDIT_UPDATE_sum \\\n",
"0 -155 -652 \n",
"1 -1185 -3999 \n",
"2 -2131 -3264 \n",
"3 -682 -1064 \n",
"4 -121 -163 \n",
"\n",
" bureau_AMT_ANNUITY_count bureau_AMT_ANNUITY_mean bureau_AMT_ANNUITY_max \\\n",
"0 7 3545.357143 10822.5 \n",
"1 7 0.000000 0.0 \n",
"2 0 NaN NaN \n",
"3 0 NaN NaN \n",
"4 3 1420.500000 4261.5 \n",
"\n",
" bureau_AMT_ANNUITY_min bureau_AMT_ANNUITY_sum \n",
"0 0.0 24817.5 \n",
"1 0.0 0.0 \n",
"2 NaN 0.0 \n",
"3 NaN 0.0 \n",
"4 0.0 4261.5 \n",
"\n",
"[5 rows x 61 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bureau_agg = agg_numeric(bureau.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
"bureau_agg.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 按贷款对Bureau Balance数据集计数"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
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""
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"text/plain": [
" bureau_balance_STATUS_0_count \\\n",
"SK_ID_BUREAU \n",
"5001709 0 \n",
"5001710 5 \n",
"5001711 3 \n",
"5001712 10 \n",
"5001713 0 \n",
"\n",
" bureau_balance_STATUS_0_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.000000 \n",
"5001710 0.060241 \n",
"5001711 0.750000 \n",
"5001712 0.526316 \n",
"5001713 0.000000 \n",
"\n",
" bureau_balance_STATUS_1_count \\\n",
"SK_ID_BUREAU \n",
"5001709 0 \n",
"5001710 0 \n",
"5001711 0 \n",
"5001712 0 \n",
"5001713 0 \n",
"\n",
" bureau_balance_STATUS_1_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.0 \n",
"5001710 0.0 \n",
"5001711 0.0 \n",
"5001712 0.0 \n",
"5001713 0.0 \n",
"\n",
" bureau_balance_STATUS_2_count \\\n",
"SK_ID_BUREAU \n",
"5001709 0 \n",
"5001710 0 \n",
"5001711 0 \n",
"5001712 0 \n",
"5001713 0 \n",
"\n",
" bureau_balance_STATUS_2_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.0 \n",
"5001710 0.0 \n",
"5001711 0.0 \n",
"5001712 0.0 \n",
"5001713 0.0 \n",
"\n",
" bureau_balance_STATUS_3_count \\\n",
"SK_ID_BUREAU \n",
"5001709 0 \n",
"5001710 0 \n",
"5001711 0 \n",
"5001712 0 \n",
"5001713 0 \n",
"\n",
" bureau_balance_STATUS_3_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.0 \n",
"5001710 0.0 \n",
"5001711 0.0 \n",
"5001712 0.0 \n",
"5001713 0.0 \n",
"\n",
" bureau_balance_STATUS_4_count \\\n",
"SK_ID_BUREAU \n",
"5001709 0 \n",
"5001710 0 \n",
"5001711 0 \n",
"5001712 0 \n",
"5001713 0 \n",
"\n",
" bureau_balance_STATUS_4_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.0 \n",
"5001710 0.0 \n",
"5001711 0.0 \n",
"5001712 0.0 \n",
"5001713 0.0 \n",
"\n",
" bureau_balance_STATUS_5_count \\\n",
"SK_ID_BUREAU \n",
"5001709 0 \n",
"5001710 0 \n",
"5001711 0 \n",
"5001712 0 \n",
"5001713 0 \n",
"\n",
" bureau_balance_STATUS_5_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.0 \n",
"5001710 0.0 \n",
"5001711 0.0 \n",
"5001712 0.0 \n",
"5001713 0.0 \n",
"\n",
" bureau_balance_STATUS_C_count \\\n",
"SK_ID_BUREAU \n",
"5001709 86 \n",
"5001710 48 \n",
"5001711 0 \n",
"5001712 9 \n",
"5001713 0 \n",
"\n",
" bureau_balance_STATUS_C_count_norm \\\n",
"SK_ID_BUREAU \n",
"5001709 0.886598 \n",
"5001710 0.578313 \n",
"5001711 0.000000 \n",
"5001712 0.473684 \n",
"5001713 0.000000 \n",
"\n",
" bureau_balance_STATUS_X_count \\\n",
"SK_ID_BUREAU \n",
"5001709 11 \n",
"5001710 30 \n",
"5001711 1 \n",
"5001712 0 \n",
"5001713 22 \n",
"\n",
" bureau_balance_STATUS_X_count_norm \n",
"SK_ID_BUREAU \n",
"5001709 0.113402 \n",
"5001710 0.361446 \n",
"5001711 0.250000 \n",
"5001712 0.000000 \n",
"5001713 1.000000 "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bureau_balance_counts = count_categorical(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
"bureau_balance_counts.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 按贷款对Bureau Balance数据集聚合"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" SK_ID_BUREAU \n",
" bureau_balance_MONTHS_BALANCE_count \n",
" bureau_balance_MONTHS_BALANCE_mean \n",
" bureau_balance_MONTHS_BALANCE_max \n",
" bureau_balance_MONTHS_BALANCE_min \n",
" bureau_balance_MONTHS_BALANCE_sum \n",
" \n",
" \n",
" \n",
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\n",
""
],
"text/plain": [
" SK_ID_BUREAU bureau_balance_MONTHS_BALANCE_count \\\n",
"0 5001709 97 \n",
"1 5001710 83 \n",
"2 5001711 4 \n",
"3 5001712 19 \n",
"4 5001713 22 \n",
"\n",
" bureau_balance_MONTHS_BALANCE_mean bureau_balance_MONTHS_BALANCE_max \\\n",
"0 -48.0 0 \n",
"1 -41.0 0 \n",
"2 -1.5 0 \n",
"3 -9.0 0 \n",
"4 -10.5 0 \n",
"\n",
" bureau_balance_MONTHS_BALANCE_min bureau_balance_MONTHS_BALANCE_sum \n",
"0 -96 -4656 \n",
"1 -82 -3403 \n",
"2 -3 -6 \n",
"3 -18 -171 \n",
"4 -21 -231 "
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bureau_balance_agg = agg_numeric(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
"bureau_balance_agg.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 按客户对Bureau Balance数据集聚合"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"# 按贷款对数据集分组\n",
"bureau_by_loan = bureau_balance_agg.merge(bureau_balance_counts, right_index = True, left_on = 'SK_ID_BUREAU', how = 'outer')\n",
"\n",
"# 将SK_ID_CURR结合\n",
"bureau_by_loan = bureau[['SK_ID_BUREAU', 'SK_ID_CURR']].merge(bureau_by_loan, on = 'SK_ID_BUREAU', how = 'left')\n",
"\n",
"# 将每个客户的数据聚合\n",
"bureau_balance_by_client = agg_numeric(bureau_by_loan.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'client')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 在训练数据中插入计算处理后的特征"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original Number of Feature: 122\n"
]
}
],
"source": [
"original_features = list(train.columns)\n",
"print('Original Number of Feature: ', len(original_features))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"# 结合Bureau数据集计数（这里有些同学可能会报key error，原因在于没有更新pandas。方法： 命令行输入'pip install -U pandas '）\n",
"train = train.merge(bureau_counts, on = 'SK_ID_CURR', how = 'left')\n",
"\n",
"# 结合Bureau数据集\n",
"train = train.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\n",
"\n",
"# 按客户对月度信息分组\n",
"train = train.merge(bureau_balance_by_client, on = 'SK_ID_CURR', how = 'left')"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of features using previous loans from other institutions data: 333\n"
]
}
],
"source": [
"new_features = list(train.columns)\n",
"print('Number of features using previous loans from other institutions data: ', len(new_features))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 特征工程结果\n",
"在完成上述所有工作之后，现在我们来看看我们所创建的变量。首先可以查看缺失值的百分比、变量与目标的相关性以及变量与其他变量的相关性。变量之间的相关性可以表明是否有共线变量，即彼此高度相关的变量。通常我们要删除一对共线变量中的一个，因为该变量是多余的。我们还可以使用缺失值的百分比来删除没有表征足够信息的一些特征。特征选择是接下来工作的一个重点，通过减少特征的数量，可以帮助模型在训练期间有效学习，并且还可以更好地对测试数据进行信息概括。“维数灾难”是指由太多特征（太高的维度）引起的问题的名称。随着变量数量的增加，学习这些变量和目标值之间的关系所需的数据量呈指数增长\n",
"特征选择是删除变量的过程，以帮助我们的模型更好地学习和推广到测试集。目的是去除无用/冗余变量，同时保留有用的变量。有许多工具可用于此过程，但是在本文中，我们将主要删除具有高缺失率和高相关性的变量的列。之后我们可以了解如何使用从诸如梯度增强机或随机森林模型之类的模型中返回的特征来执行特征选择。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 缺失值\n",
"主要考虑丢弃高缺失率的列"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"# 列缺失值计算函数\n",
"def missing_values_table(df):\n",
" # 总缺失值\n",
" mis_val = df.isnull().sum()\n",
" \n",
" # 缺失值百分比\n",
" mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
" \n",
" # 对结果建表\n",
" mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)# concat函数可以将数据根据不同的轴作简单的融合连接\n",
" \n",
" # 列重命名\n",
" mis_val_table_ren_columns = mis_val_table.rename(\n",
" columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
" \n",
" # 按缺失率递减排序\n",
" mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
" mis_val_table_ren_columns.iloc[:, 1] != 0].sort_values(\n",
" '% of Total Values', ascending=False).round(1)\n",
" \n",
" # 输出总信息\n",
" print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\" \n",
" \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
" \" columns that have missing values.\") \n",
" \n",
" # 返回缺失信息\n",
" return mis_val_table_ren_columns"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your selected dataframe has 333 columns.\n",
"There are 278 columns that have missing values.\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Missing Values \n",
" % of Total Values \n",
" \n",
" \n",
" \n",
" \n",
" bureau_AMT_ANNUITY_min \n",
" 227502 \n",
" 74.0 \n",
" \n",
" \n",
" bureau_AMT_ANNUITY_max \n",
" 227502 \n",
" 74.0 \n",
" \n",
" \n",
" bureau_AMT_ANNUITY_mean \n",
" 227502 \n",
" 74.0 \n",
" \n",
" \n",
" client_bureau_balance_STATUS_4_count_min \n",
" 215280 \n",
" 70.0 \n",
" \n",
" \n",
" client_bureau_balance_STATUS_3_count_norm_mean \n",
" 215280 \n",
" 70.0 \n",
" \n",
" \n",
" client_bureau_balance_MONTHS_BALANCE_count_min \n",
" 215280 \n",
" 70.0 \n",
" \n",
" \n",
" client_bureau_balance_STATUS_4_count_max \n",
" 215280 \n",
" 70.0 \n",
" \n",
" \n",
" client_bureau_balance_STATUS_4_count_mean \n",
" 215280 \n",
" 70.0 \n",
" \n",
" \n",
" client_bureau_balance_STATUS_3_count_norm_min \n",
" 215280 \n",
" 70.0 \n",
" \n",
" \n",
" client_bureau_balance_STATUS_3_count_norm_max \n",
" 215280 \n",
" 70.0 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Missing Values \\\n",
"bureau_AMT_ANNUITY_min 227502 \n",
"bureau_AMT_ANNUITY_max 227502 \n",
"bureau_AMT_ANNUITY_mean 227502 \n",
"client_bureau_balance_STATUS_4_count_min 215280 \n",
"client_bureau_balance_STATUS_3_count_norm_mean 215280 \n",
"client_bureau_balance_MONTHS_BALANCE_count_min 215280 \n",
"client_bureau_balance_STATUS_4_count_max 215280 \n",
"client_bureau_balance_STATUS_4_count_mean 215280 \n",
"client_bureau_balance_STATUS_3_count_norm_min 215280 \n",
"client_bureau_balance_STATUS_3_count_norm_max 215280 \n",
"\n",
" % of Total Values \n",
"bureau_AMT_ANNUITY_min 74.0 \n",
"bureau_AMT_ANNUITY_max 74.0 \n",
"bureau_AMT_ANNUITY_mean 74.0 \n",
"client_bureau_balance_STATUS_4_count_min 70.0 \n",
"client_bureau_balance_STATUS_3_count_norm_mean 70.0 \n",
"client_bureau_balance_MONTHS_BALANCE_count_min 70.0 \n",
"client_bureau_balance_STATUS_4_count_max 70.0 \n",
"client_bureau_balance_STATUS_4_count_mean 70.0 \n",
"client_bureau_balance_STATUS_3_count_norm_min 70.0 \n",
"client_bureau_balance_STATUS_3_count_norm_max 70.0 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_train = missing_values_table(train)\n",
"missing_train.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们看到有许多列的缺失值的百分比很高。缺失阈值的选定需要根据问题本身来考虑。这里为了减少特征的数量，我们将删除训练或测试数据中任何大于90%缺失值的列。"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_train_vars = list(missing_train.index[missing_train['% of Total Values'] > 90])\n",
"len(missing_train_vars)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"在删除缺失值之前，首先在测试数据中统计缺失值百分比。然后在训练或测试数据中删除超过90%个缺失值的列。现在重新查看测试数据，执行相同的操作，并查看测试数据中的缺失值。我们已经计算了所有的计数和聚合统计数据，因此只需要将测试数据与适当的数据合并"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 计算测试数据信息"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"# 读取数据\n",
"test = pd.read_csv('application_test.csv')\n",
"\n",
"# 与bureau_counts数据结合\n",
"test = test.merge(bureau_counts, on = 'SK_ID_CURR', how = 'left')\n",
"\n",
"# 与bbureau_agg数据结合\n",
"test = test.merge(bureau_agg, on = 'SK_ID_CURR', how = 'left')\n",
"\n",
"# 与bureau_balance_by_client数据结合\n",
"test = test.merge(bureau_balance_by_client, on = 'SK_ID_CURR', how = 'left')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of Testing Data: (48744, 332)\n"
]
}
],
"source": [
"print('Shape of Testing Data: ', test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们需要对齐测试和训练数据文件，这意味着需要使它们具有完全相同的列。其实本不需要这样做，但是当进行one-hot编码时，我们需要对齐数据集以确保它们具有相同的列。"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"train_labels = train['TARGET']\n",
"\n",
"# 通过移除'TARGET'列来对齐列\n",
"train, test = train.align(test, join = 'inner', axis = 1)\n",
"\n",
"train['TARGET'] = train_labels"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Data Shape: (307511, 333)\n",
"Testing Data Shape: (48744, 332)\n"
]
}
],
"source": [
"print('Training Data Shape: ', train.shape)\n",
"print('Testing Data Shape: ', test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"现在，数据集已经具有相同的列（除了训练数据中的TARGET列），这意味着我们可以在机器学习模型中使用它们，该模型需要在训练和测试数据中看到相同的列\n",
"\n",
"现在让我们看看测试数据中缺失值的百分比，从而计算出应该丢弃的列"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your selected dataframe has 332 columns.\n",
"There are 275 columns that have missing values.\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Missing Values \n",
" % of Total Values \n",
" \n",
" \n",
" \n",
" \n",
" COMMONAREA_MEDI \n",
" 33495 \n",
" 68.7 \n",
" \n",
" \n",
" COMMONAREA_MODE \n",
" 33495 \n",
" 68.7 \n",
" \n",
" \n",
" COMMONAREA_AVG \n",
" 33495 \n",
" 68.7 \n",
" \n",
" \n",
" NONLIVINGAPARTMENTS_MEDI \n",
" 33347 \n",
" 68.4 \n",
" \n",
" \n",
" NONLIVINGAPARTMENTS_AVG \n",
" 33347 \n",
" 68.4 \n",
" \n",
" \n",
" NONLIVINGAPARTMENTS_MODE \n",
" 33347 \n",
" 68.4 \n",
" \n",
" \n",
" FONDKAPREMONT_MODE \n",
" 32797 \n",
" 67.3 \n",
" \n",
" \n",
" LIVINGAPARTMENTS_MEDI \n",
" 32780 \n",
" 67.2 \n",
" \n",
" \n",
" LIVINGAPARTMENTS_MODE \n",
" 32780 \n",
" 67.2 \n",
" \n",
" \n",
" LIVINGAPARTMENTS_AVG \n",
" 32780 \n",
" 67.2 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Missing Values % of Total Values\n",
"COMMONAREA_MEDI 33495 68.7\n",
"COMMONAREA_MODE 33495 68.7\n",
"COMMONAREA_AVG 33495 68.7\n",
"NONLIVINGAPARTMENTS_MEDI 33347 68.4\n",
"NONLIVINGAPARTMENTS_AVG 33347 68.4\n",
"NONLIVINGAPARTMENTS_MODE 33347 68.4\n",
"FONDKAPREMONT_MODE 32797 67.3\n",
"LIVINGAPARTMENTS_MEDI 32780 67.2\n",
"LIVINGAPARTMENTS_MODE 32780 67.2\n",
"LIVINGAPARTMENTS_AVG 32780 67.2"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_test = missing_values_table(test)\n",
"missing_test.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_test_vars = list(missing_test.index[missing_test['% of Total Values'] > 90])\n",
"len(missing_test_vars)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 0 columns with more than 90% missing in either the training or testing data.\n"
]
}
],
"source": [
"missing_columns = list(set(missing_test_vars + missing_train_vars))\n",
"print('There are %d columns with more than 90%% missing in either the training or testing data.' % len(missing_columns))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"# 舍弃缺失率高的列\n",
"train = train.drop(columns = missing_columns)\n",
"test = test.drop(columns = missing_columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"本轮中没有舍弃任何列，这是由于每一列的缺失率都低于90%，因此我们考虑使用其他的特征选择方法来减少维数\n",
"\n",
"此刻我们保存了完整的训练和测试数据。我鼓励大家尝试采用不同的缺失率阈值来删除缺失列并比较结果"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"train.to_csv('train_bureau_raw.csv', index = False)\n",
"test.to_csv('test_bureau_raw.csv', index = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 相关性\n",
"首先让我们看看变量与目标的相关性。可以从我们创建的任何变量中看到，与训练数据中原有的变量相比，它们具有更大的相关性。"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"# 计算数据集的相关系数\n",
"corrs = train.corr()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" TARGET \n",
" \n",
" \n",
" \n",
" \n",
" TARGET \n",
" 1.000000 \n",
" \n",
" \n",
" bureau_DAYS_CREDIT_mean \n",
" 0.089729 \n",
" \n",
" \n",
" client_bureau_balance_MONTHS_BALANCE_min_mean \n",
" 0.089038 \n",
" \n",
" \n",
" DAYS_BIRTH \n",
" 0.078239 \n",
" \n",
" \n",
" bureau_CREDIT_ACTIVE_Active_count_norm \n",
" 0.077356 \n",
" \n",
" \n",
" client_bureau_balance_MONTHS_BALANCE_mean_mean \n",
" 0.076424 \n",
" \n",
" \n",
" bureau_DAYS_CREDIT_min \n",
" 0.075248 \n",
" \n",
" \n",
" client_bureau_balance_MONTHS_BALANCE_min_min \n",
" 0.073225 \n",
" \n",
" \n",
" client_bureau_balance_MONTHS_BALANCE_sum_mean \n",
" 0.072606 \n",
" \n",
" \n",
" bureau_DAYS_CREDIT_UPDATE_mean \n",
" 0.068927 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" TARGET\n",
"TARGET 1.000000\n",
"bureau_DAYS_CREDIT_mean 0.089729\n",
"client_bureau_balance_MONTHS_BALANCE_min_mean 0.089038\n",
"DAYS_BIRTH 0.078239\n",
"bureau_CREDIT_ACTIVE_Active_count_norm 0.077356\n",
"client_bureau_balance_MONTHS_BALANCE_mean_mean 0.076424\n",
"bureau_DAYS_CREDIT_min 0.075248\n",
"client_bureau_balance_MONTHS_BALANCE_min_min 0.073225\n",
"client_bureau_balance_MONTHS_BALANCE_sum_mean 0.072606\n",
"bureau_DAYS_CREDIT_UPDATE_mean 0.068927"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corrs = corrs.sort_values('TARGET', ascending = False)\n",
"\n",
"# 相关性最强的10个属性\n",
"pd.DataFrame(corrs['TARGET'].head(10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"与TARGET相关度最高的变量（除了TARGET本身）是我们创建的变量。然而变量具有高相关性并不意味着它一定是有用的，需要注意的是，如果我们生成了上百个新变量，其中一些变量的相关性可能仅仅是由于随机噪声而产生的\n",
"\n",
"从怀疑的角度看待相关系数，确实存在有些新创建变量会产生巨大作用。为了评估变量的‘有用性’，我们将查看模型返回的特征重要性。出于好奇（并且因为我们已经编写了函数），所以可以对新创建的变量中的两个进行KDE图展示"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'client_bureau_balance_counts_mean'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3077\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3078\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3079\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 'client_bureau_balance_counts_mean'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mkde_target\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'client_bureau_balance_counts_mean'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m\u001b[0m in \u001b[0;36mkde_target\u001b[1;34m(var_name, df)\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# 计算变量与目标值之间的皮尔森相关系数\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mcorr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TARGET'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvar_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;31m# 计算偿还或未偿还贷款数据的中位数\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 2686\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2687\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2688\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2689\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2690\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 2693\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2694\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2695\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2696\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2697\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 2487\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2488\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2489\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2490\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2491\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m 4113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4114\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4115\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4116\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4117\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mD:\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3078\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3079\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3082\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 'client_bureau_balance_counts_mean'"
]
}
],
"source": [
"kde_target(var_name='client_bureau_balance_counts_mean', df=train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这个变量代表每个客户每月贷款的平均记录数。例如，如果一个客户以前有三笔贷款，月度数据中的记录数为3、4和5，那么平均记录数的值为4。根据分布，每个贷款的平均月度记录较多的客户更有可能用住房信贷偿还贷款。我们不要过多地研究这个值，但是它可能表明，信用历史更早的客户通常更有可能偿还贷款。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"kde_target(var_name='bureau_CREDIT_ACTIVE_active_counts_norm', df=train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"从分布图可以看出，该变量分布于各个地方。该变量表示以前的贷款的贷款中，'CREADIT_ACTIVE'值为active的数量除以客户以前的贷款总数。这里的相关性太弱了，我认为我们不应该得出任何结论"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 共线变量\n",
"我们不仅可以计算变量与目标的相关性，还可以计算每个变量与其他变量的相关性。这将允许我们看看是否存在高度共线变量，这些变量可能应该从数据中删除。\n",
"\n",
"让我们寻找任何与0.8个变量相关的变量。"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"# 设置阈值\n",
"threshold = 0.8\n",
"\n",
"# 保存相关变量的空字典\n",
"above_threshold_vars = {}\n",
"\n",
"# 对于每一列,记录大于阈值的变量\n",
"for col in corrs:\n",
" above_threshold_vars[col] = list(corrs.index[corrs[col] > threshold])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"对于每一对具有高相关性的变量，我们需要去掉其中的一个变量。下面的代码仅通过添加每对共线变量中的其中之一来创建一个变量集合"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of columns to remove: 134\n"
]
}
],
"source": [
"# 记录需要移除的列和已经检查过的列\n",
"cols_to_remove = []\n",
"cols_seen = []\n",
"cols_to_remove_pair = []\n",
"\n",
"# 迭代列及相关列\n",
"for key, value in above_threshold_vars.items():\n",
" # 查找已经检查过的列\n",
" cols_seen.append(key)\n",
" for x in value:\n",
" if x == key:\n",
" next\n",
" else:\n",
" # 移除一对高相关性变量中的一个\n",
" if x not in cols_seen:\n",
" cols_to_remove.append(x)\n",
" cols_to_remove_pair.append(key)\n",
" \n",
"cols_to_remove = list(set(cols_to_remove))\n",
"print('Number of columns to remove: ', len(cols_to_remove))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们可以从训练和测试数据集中删除这些列。在删除这些变量之后，我们必须比较删除前后的性能差别"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Corrs Removed Shape: (307511, 199)\n",
"Testing Corrs Removed Shape: (48744, 198)\n"
]
}
],
"source": [
"train_corrs_removed = train.drop(columns = cols_to_remove)\n",
"test_corrs_removed = test.drop(columns = cols_to_remove)\n",
"\n",
"print('Training Corrs Removed Shape: ', train_corrs_removed.shape)\n",
"print('Testing Corrs Removed Shape: ', test_corrs_removed.shape)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"train_corrs_removed.to_csv('train_bureau_corrs_removed.csv', index = False)\n",
"test_corrs_removed.to_csv('test_bureau_corrs_removed.csv', index = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 建模\n",
"为了实际测试这些新数据集的性能，我们将尝试使用它们来进行机器学习！这里，我们将使用我在另一篇笔记中开发的函数来比较这些特性（原始版本和删除的高度相关的变量）。我已经记录了相关效果，下面列出模型控件及两个测试条件：\n",
"\n",
"对于所有数据集，使用下面所示的模型（用精确的超参数）\n",
"\n",
"控件：基础数据\n",
"测试一：基础数据 + bureau和bureau_balance文件中所有的数据记录的所有数据\n",
"测试二：基础数据 + bureau和bureau_balance文件中所有的数据记录的高相关性数据"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"import lightgbm as lgb\n",
"\n",
"from sklearn.model_selection import KFold\n",
"from sklearn.metrics import roc_auc_score\n",
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"import gc\n",
"\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"def model(features, test_features, encoding = 'ohe', n_folds = 5):\n",
" \n",
" \"\"\"使用基于交叉验证的LightGBM进行训练和测试\n",
" \n",
" 参数\n",
" --------\n",
" features (pd.DataFrame): \n",
" 用于模型训练的训练数据集，其必须包含'TARGET'列\n",
" test_features (pd.DataFrame): \n",
" 用于模型预测的测试数据集 \n",
" encoding (str, default = 'ohe'): \n",
" 指定类别变量的编码方式。'oho'：使用one-hot编码。'le'：使用整型标签编码\n",
" n_folds (int, default = 5): \n",
" 用于交叉检验的折数，默认采用5折交叉检验\n",
" \n",
" 返回值\n",
" --------\n",
" submission (pd.DataFrame): \n",
" 包含`SK_ID_CURR`和`TARGET`属性的预测数据\n",
" feature_importances (pd.DataFrame): \n",
" 该模型的重要特征\n",
" valid_metrics (pd.DataFrame): \n",
" ROC、AUC等误差统计量（包含训练/测试过程） \n",
" \"\"\"\n",
" \n",
" # 抽取id\n",
" train_ids = features['SK_ID_CURR']\n",
" test_ids = test_features['SK_ID_CURR']\n",
" \n",
" # 抽取标签\n",
" labels = features['TARGET']\n",
" \n",
" # 移除id和标签\n",
" features = features.drop(columns = ['SK_ID_CURR', 'TARGET'])\n",
" test_features = test_features.drop(columns = ['SK_ID_CURR'])\n",
" \n",
" \n",
" # One-Hot编码\n",
" if encoding == 'ohe':\n",
" features = pd.get_dummies(features)\n",
" test_features = pd.get_dummies(test_features)\n",
" \n",
" # 通过列对齐数据帧\n",
" features, test_features = features.align(test_features, join = 'inner', axis = 1)\n",
" \n",
" # No categorical indices to record\n",
" cat_indices = 'auto'\n",
" \n",
" # 整型标签编码\n",
" elif encoding == 'le':\n",
" \n",
" # 创建标签编码器(LabelEncoder)\n",
" label_encoder = LabelEncoder()\n",
" \n",
" # 存储分类索引的列表\n",
" cat_indices = []\n",
" \n",
" # 迭代每一列\n",
" for i, col in enumerate(features):\n",
" if features[col].dtype == 'object':\n",
" # 使用标签编码器将分类特征映射到整型\n",
" features[col] = label_encoder.fit_transform(np.array(features[col].astype(str)).reshape((-1,)))\n",
" test_features[col] = label_encoder.transform(np.array(test_features[col].astype(str)).reshape((-1,)))\n",
"\n",
" # 记录分类索引\n",
" cat_indices.append(i)\n",
" \n",
" # 如果标签编码方案无效，则捕获错误\n",
" else:\n",
" raise ValueError(\"Encoding must be either 'ohe' or 'le'\")\n",
" \n",
" print('Training Data Shape: ', features.shape)\n",
" print('Testing Data Shape: ', test_features.shape)\n",
" \n",
" # 抽取特征名称\n",
" feature_names = list(features.columns)\n",
" \n",
" # 转化为np arrays形式\n",
" features = np.array(features)\n",
" test_features = np.array(test_features)\n",
" \n",
" # 创建k折对象\n",
" k_fold = KFold(n_splits = n_folds, shuffle = False, random_state = 50)\n",
" \n",
" # 特征重要性数组（初始化全为0）\n",
" feature_importance_values = np.zeros(len(feature_names))\n",
" \n",
" # 测试预测性能的数组（初始化全为0）\n",
" test_predictions = np.zeros(test_features.shape[0])\n",
" \n",
" # 保存k折交叉验证输出的数组（初始化全为0）\n",
" out_of_fold = np.zeros(features.shape[0])\n",
" \n",
" # 记录验证和训练分数的列表\n",
" valid_scores = []\n",
" train_scores = []\n",
" \n",
" # 在每一折(k-fold)中迭代\n",
" for train_indices, valid_indices in k_fold.split(features):\n",
" \n",
" # 该折的训练数据\n",
" train_features, train_labels = features[train_indices], labels[train_indices]\n",
" # 该折的验证数据\n",
" valid_features, valid_labels = features[valid_indices], labels[valid_indices]\n",
" \n",
" # 建立模型\n",
" model = lgb.LGBMClassifier(n_estimators=10000, objective = 'binary', \n",
" class_weight = 'balanced', learning_rate = 0.05, \n",
" reg_alpha = 0.1, reg_lambda = 0.1, \n",
" subsample = 0.8, n_jobs = -1, random_state = 50)\n",
" \n",
" # 训练模型\n",
" model.fit(train_features, train_labels, eval_metric = 'auc',\n",
" eval_set = [(valid_features, valid_labels), (train_features, train_labels)],\n",
" eval_names = ['valid', 'train'], categorical_feature = cat_indices,\n",
" early_stopping_rounds = 100, verbose = 200)\n",
" \n",
" # 记录最佳迭代\n",
" best_iteration = model.best_iteration_\n",
" \n",
" # 记录特征重要性\n",
" feature_importance_values += model.feature_importances_ / k_fold.n_splits\n",
" \n",
" # 预测\n",
" test_predictions += model.predict_proba(test_features, num_iteration = best_iteration)[:, 1] / k_fold.n_splits\n",
" \n",
" # 记录折中预测\n",
" out_of_fold[valid_indices] = model.predict_proba(valid_features, num_iteration = best_iteration)[:, 1]\n",
" \n",
" # 记录最佳成绩\n",
" valid_score = model.best_score_['valid']['auc']\n",
" train_score = model.best_score_['train']['auc']\n",
" \n",
" valid_scores.append(valid_score)\n",
" train_scores.append(train_score)\n",
" \n",
" # 清除存储\n",
" gc.enable()\n",
" del model, train_features, valid_features\n",
" gc.collect()\n",
" \n",
" # 显示提交的数据文件\n",
" submission = pd.DataFrame({'SK_ID_CURR': test_ids, 'TARGET': test_predictions})\n",
" \n",
" # 显示重要特征\n",
" feature_importances = pd.DataFrame({'feature': feature_names, 'importance': feature_importance_values})\n",
" \n",
" # 整体验证分数\n",
" valid_auc = roc_auc_score(labels, out_of_fold)\n",
" \n",
" # 将整体验证分数加入数组\n",
" valid_scores.append(valid_auc)\n",
" train_scores.append(np.mean(train_scores))\n",
" \n",
" # 相关数据存储\n",
" fold_names = list(range(n_folds))\n",
" fold_names.append('overall')\n",
" \n",
" # 验证分数数据集\n",
" metrics = pd.DataFrame({'fold': fold_names,\n",
" 'train': train_scores,\n",
" 'valid': valid_scores}) \n",
" \n",
" return submission, feature_importances, metrics"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"def plot_feature_importances(df):\n",
" \"\"\"\n",
" 画出模型返回的特征重要性，可以使用于任何的重要性评价标准（默认越高越好）\n",
" \n",
" Args:\n",
" df (dataframe): 特征重要性。必须要有'feature'特征列和'importance'特征重要性列\n",
" \n",
" 返回值:\n",
" 显示出最重要的15个特征\n",
" \n",
" df (dataframe): 按特征重要性进行排序（从高到低），包括一列标准化后的重要性数值\n",
" \"\"\"\n",
" \n",
" # 按特征重要性进行排序\n",
" df = df.sort_values('importance', ascending = False).reset_index()\n",
" \n",
" # 标准化特征重要性\n",
" df['importance_normalized'] = df['importance'] / df['importance'].sum()\n",
"\n",
" # 画出直方图\n",
" plt.figure(figsize = (10, 6))\n",
" ax = plt.subplot()\n",
" \n",
" # 反转，将最重要的特征放在最前面\n",
" ax.barh(list(reversed(list(df.index[:15]))), \n",
" df['importance_normalized'].head(15), \n",
" align = 'center', edgecolor = 'k')\n",
" \n",
" # 设置ytick和label\n",
" ax.set_yticks(list(reversed(list(df.index[:15]))))\n",
" ax.set_yticklabels(df['feature'].head(15))\n",
" \n",
" # 标注相关信息\n",
" plt.xlabel('Normalized Importance'); plt.title('Feature Importances')\n",
" plt.show()\n",
" \n",
" return df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 控制(Control)\n",
"任何实验的第一步都是建立一个控制(Control)。为此，我们将使用上面定义的函数（实现梯度增强机模型(GBM)）和单个主数据源（application.csv）"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"train_control = pd.read_csv('application_train.csv')\n",
"test_control = pd.read_csv('application_test.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"幸运的是，一旦我们花时间编写了一个函数，使用它就非常简单（如果本文有一个中心主题，它就是使用函数来使事情变得更简单和可重用！）上面的函数返回我们可以上传到竞赛的提交数据框架、包含特征重要性的fi数据框架(feature inportance)和具有验证和测试性能的数据框架"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Data Shape: (307511, 241)\n",
"Testing Data Shape: (48744, 241)\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.760007\ttrain's auc: 0.798103\n",
"Early stopping, best iteration is:\n",
"[269]\tvalid's auc: 0.760273\ttrain's auc: 0.809199\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.76114\ttrain's auc: 0.798328\n",
"Early stopping, best iteration is:\n",
"[289]\tvalid's auc: 0.761398\ttrain's auc: 0.812654\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.750232\ttrain's auc: 0.79964\n",
"Early stopping, best iteration is:\n",
"[265]\tvalid's auc: 0.750451\ttrain's auc: 0.809734\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.759831\ttrain's auc: 0.797797\n",
"Early stopping, best iteration is:\n",
"[282]\tvalid's auc: 0.760245\ttrain's auc: 0.811121\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.76071\ttrain's auc: 0.798106\n",
"Early stopping, best iteration is:\n",
"[226]\tvalid's auc: 0.760972\ttrain's auc: 0.802236\n"
]
}
],
"source": [
"submission, fi, metrics = model(train_control, test_control)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" \n",
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"
\n",
""
],
"text/plain": [
" fold train valid\n",
"0 0 0.809199 0.760273\n",
"1 1 0.812654 0.761398\n",
"2 2 0.809734 0.750451\n",
"3 3 0.811121 0.760245\n",
"4 4 0.802236 0.760972\n",
"5 overall 0.808989 0.758635"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"因为训练分数高于验证分数，所以控制器稍微有些过拟合，我们可以在后面的正则化部分中解决这个问题（其实我们已经在使用ReGiaLAMBDA、ReGiAlPH以及早停(early stopping)的过程中执行了一些正则化）\n",
"\n",
"我们可以用另一个函数——plot_feature_importances来可视化特征的重要性。在进行特征选择的时候，特征重要性很可能会有些作用"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fi_sorted = plot_feature_importances(fi)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"submission.to_csv('control.csv', index = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"在比赛中，该控制器的分数为0.745"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 测试一\n",
"让我们进行第一次测试。我们只需要把数据传递给函数，函数就完成了我们的大部分工作。"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Data Shape: (307511, 452)\n",
"Testing Data Shape: (48744, 452)\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.766471\ttrain's auc: 0.810385\n",
"Early stopping, best iteration is:\n",
"[293]\tvalid's auc: 0.767203\ttrain's auc: 0.827287\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.767523\ttrain's auc: 0.810288\n",
"Early stopping, best iteration is:\n",
"[265]\tvalid's auc: 0.768109\ttrain's auc: 0.822264\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.761029\ttrain's auc: 0.811653\n",
"Early stopping, best iteration is:\n",
"[267]\tvalid's auc: 0.761625\ttrain's auc: 0.824147\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.767837\ttrain's auc: 0.809671\n",
"Early stopping, best iteration is:\n",
"[236]\tvalid's auc: 0.76815\ttrain's auc: 0.816322\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.767516\ttrain's auc: 0.809788\n",
"[400]\tvalid's auc: 0.767583\ttrain's auc: 0.842953\n",
"Early stopping, best iteration is:\n",
"[306]\tvalid's auc: 0.767902\ttrain's auc: 0.828518\n"
]
}
],
"source": [
"submission_raw, fi_raw, metrics_raw = model(train, test)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" fold \n",
" train \n",
" valid \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0 \n",
" 0.827287 \n",
" 0.767203 \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 0.822264 \n",
" 0.768109 \n",
" \n",
" \n",
" 2 \n",
" 2 \n",
" 0.824147 \n",
" 0.761625 \n",
" \n",
" \n",
" 3 \n",
" 3 \n",
" 0.816322 \n",
" 0.768150 \n",
" \n",
" \n",
" 4 \n",
" 4 \n",
" 0.828518 \n",
" 0.767902 \n",
" \n",
" \n",
" 5 \n",
" overall \n",
" 0.823708 \n",
" 0.766597 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" fold train valid\n",
"0 0 0.827287 0.767203\n",
"1 1 0.822264 0.768109\n",
"2 2 0.824147 0.761625\n",
"3 3 0.816322 0.768150\n",
"4 4 0.828518 0.767902\n",
"5 overall 0.823708 0.766597"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics_raw"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"从数字上看，处理后的特征训练的效果优于原始特征，但是只有把预测数据提交之后，才可以确定这种更好的验证性能是否转移到了测试数据中"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fi_raw_sorted = plot_feature_importances(fi_raw)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"通过检查特征的重要性发现，我们构造的一些特征是似乎是最重要的。我们可以找出本文所做的最重要的100个特征的百分比。但是，我们不仅仅要与原始的特征进行比较，还需要与hot-encode后的原始特征进行比较。这些已经记录在我们的FI（原始数据）中"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"% of Top 100 Features created from the bureau data = 53.00\n"
]
}
],
"source": [
"top_100 = list(fi_raw_sorted['feature'])[:100]\n",
"new_features = [x for x in top_100 if x not in list(fi['feature'])]\n",
"\n",
"print('%% of Top 100 Features created from the bureau data = %d.00' % len(new_features))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"超过一半的重要特征都是由我们创造的！这让我们相信，我们所做的一切努力都是值得的"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"submission_raw.to_csv('test_one.csv', index = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"在比赛中，测试一的分数为0.759"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 测试二\n",
"这很容易，所以让我们继续吧！同以前一样，但将高度共线的变量删除"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Data Shape: (307511, 318)\n",
"Testing Data Shape: (48744, 318)\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.764122\ttrain's auc: 0.8063\n",
"Early stopping, best iteration is:\n",
"[264]\tvalid's auc: 0.764565\ttrain's auc: 0.817897\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.7652\ttrain's auc: 0.806611\n",
"[400]\tvalid's auc: 0.765625\ttrain's auc: 0.839251\n",
"Early stopping, best iteration is:\n",
"[379]\tvalid's auc: 0.765829\ttrain's auc: 0.836334\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.757092\ttrain's auc: 0.808063\n",
"Early stopping, best iteration is:\n",
"[198]\tvalid's auc: 0.757149\ttrain's auc: 0.807627\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.764131\ttrain's auc: 0.806097\n",
"Early stopping, best iteration is:\n",
"[287]\tvalid's auc: 0.76481\ttrain's auc: 0.821551\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[200]\tvalid's auc: 0.763964\ttrain's auc: 0.806256\n",
"[400]\tvalid's auc: 0.763928\ttrain's auc: 0.838649\n",
"Early stopping, best iteration is:\n",
"[312]\tvalid's auc: 0.764449\ttrain's auc: 0.825298\n"
]
}
],
"source": [
"submission_corrs, fi_corrs, metrics_corr = model(train_corrs_removed, test_corrs_removed)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" fold \n",
" train \n",
" valid \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0 \n",
" 0.817897 \n",
" 0.764565 \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 0.836334 \n",
" 0.765829 \n",
" \n",
" \n",
" 2 \n",
" 2 \n",
" 0.807627 \n",
" 0.757149 \n",
" \n",
" \n",
" 3 \n",
" 3 \n",
" 0.821551 \n",
" 0.764810 \n",
" \n",
" \n",
" 4 \n",
" 4 \n",
" 0.825298 \n",
" 0.764449 \n",
" \n",
" \n",
" 5 \n",
" overall \n",
" 0.821741 \n",
" 0.763352 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" fold train valid\n",
"0 0 0.817897 0.764565\n",
"1 1 0.836334 0.765829\n",
"2 2 0.807627 0.757149\n",
"3 3 0.821551 0.764810\n",
"4 4 0.825298 0.764449\n",
"5 overall 0.821741 0.763352"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics_corr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这些结果优于之前的控制器，但是略低于原始特征"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fi_corrs_sorted = plot_feature_importances(fi_corrs)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"submission_corrs.to_csv('test_two.csv', index = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"在比赛中，测试二的分数为0.753"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 结果\n",
"在完成了所有的工作之后，我们可以说，额外添加的信息确实提高了性能！该模型显然没有优化我们的数据，但相比于原来的数据集，仍然有明显的改善。让我们正式总结一下表现：\n",
"\n",
"Experiment\tTrain AUC\tValidation AUC\tTest AUC\n",
"Control\t0.815\t0.760\t0.745\n",
"Test One\t0.837\t0.767\t0.759\n",
"Test Two\t0.826\t0.765\t0.753\n",
"\n",
"我们所有的辛勤工作转化为了一个小改进——0.014。去除高度共线性的变量会稍微降低性能，所以我们要考虑不同的特征选择方法。此外，我们可以说，我们所构建的很多特征包含于模型所判断的最重要的100个特征中\n",
"\n",
"在这样的比赛中，即使是这种程度的微小提高也足以让我们上升100多位。通过在本文中进行许多小的改进，我们可以逐步实现更好的性能。我鼓励其他人利用这里的结果来改进自己的代码，我将继续记录我帮助他人的步骤"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 下一步\n",
"下一步，我们会将本文中开发的代码应用到其他数据集上。在我们的模型中还有4个其他的数据文件！在下一章中，我们将把这些其他数据文件（其中包含有关以前在家庭信贷贷款的信息）中的信息合并到我们的训练数据中。然后，我们可以建立相同的模型，并运行更多的实验来确定我们的特征工程的效果。在这场比赛中还有很多工作要做，还有更多的成绩要做！我们会在下一章中再见"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: docs/Kaggle/competitions/featured/home-credit-default-risk/README.md
================================================
# **[Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk)**
`你能预测每个申请人偿还贷款的能力吗？`
## 比赛说明
由于信用记录不足或根本不存在，许多人很难获得贷款。而且，不幸的是，这些人口往往被不值得信任的贷款人利用。
家庭信贷集团
[Home Credit](http://www.homecredit.net) 通过提供积极和安全的借贷经验，努力扩大无银行账户人口的金融包容性。为了确保这些服务不足的人口具有积极的贷款经验，Home Credit利用各种替代数据 - 包括电信和交易信息 - 来预测其客户的还款能力。
虽然Home Credit目前正在使用各种统计和机器学习方法来做出这些预测，但他们正在挑战Kagglers帮助他们释放数据的全部潜力。这样做将确保能够偿还的客户不会被拒绝，并且贷款将以本金，到期日和还款日历提供，这将使客户获得成功。
> 注意：[项目规范](/docs/kaggle-quickstart.md)
## 参赛成员
* [#黄花鱼]()
> [项目地址: Home Credit Default Risk](/competitions/featured/home-credit-default-risk/ManualFeatureEngineering_P1.ipynb)
... 剩下后期更新
================================================
FILE: docs/Kaggle/competitions/featured/mercari-price-suggestion-challenge/README.md
================================================
# **Mercari 价格建议挑战赛**
`你能自动建议网上卖家的产品价格？`
## 比赛说明
可能很难知道有多少东西是真正值得的。小细节可能意味着定价的巨大差异。例如，这些毛衫中的一件成本为335美元，另一件成本为9.99美元。你能猜出哪一个是哪个？
考虑到网上销售的产品有多少，产品定价的难度就更大了。服装具有强劲的季节性价格趋势，受品牌影响很大，而电子产品则根据产品规格波动。
日本最大的社区购物应用程序Mercari深知这个问题。他们想为卖家提供定价建议，但是这样做很困难，因为卖家可以在Mercari的市场上投入任何东西或任何东西。
在这场比赛中，Mercari挑战你建立一个算法，自动建议正确的产品价格。您将获得用户输入的产品文本说明，包括产品类别名称，品牌名称和项目条件等详细信息。
请注意，由于这些数据的公共性质，这个竞赛是一个“Kernels Only”竞赛。在挑战的第二阶段，文件只能通过内核获得，并且您将无法修改您的方法来响应新数据。在 [数据选项卡](https://www.kaggle.com/c/mercari-price-suggestion-challenge/data) 和 [内核常见问题页面](https://www.kaggle.com/c/mercari-price-suggestion-challenge#Kernels-FAQ) 阅读更多详细信息。
> 注意：[项目规范](/docs/kaggle-quickstart.md)
## 成员角色
| 角色 | 用户 | QQ | GitHub | 负责内容 | 进度 |
| :--: | :--: | :--: | :--: | :--: | :--: |
| 发起人 | [片刻](http://cwiki.apachecn.org/display/~jiangzhonglian) | 529815144 |https://github.com/jiangzhonglian | 负责整个项目推进 | 5% |
| 负责人
1.目标定义 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责某个业务理解和指标确认 | |
| 参与人
1.目标定义 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责某个业务理解和指标确认 | |
| xx人
2.数据采集 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责 | |
| 参与人
3.数据整理 | 佳乐 | 872520333| | 负责（助手） | |
| 参与人
3.数据整理 | 诺木人 |498744838| https://github.com/1mrliu| 负责（助手） | |
| xx人
4.构建模型 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责 | |
| 参与人
4.构建模型 |/ | 610395649 |https://github.com/lai-bluejay | 负责（助手） | |
| xx人
5.模型评估 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责 | |
| 参与人
5.模型评估 | / | 610395649 |https://github.com/lai-bluejay | 负责（助手） | |
| xx人
6.模型发布 | [昵称](ApacheCN Cwiki地址) | xxx-可以选择匿名 | | 负责 | |
## 1.目标定义
* 任务理解
* 指标确定
## 2.数据采集
* 建模抽样
* 质量把控
## 3.数据整理
* 数据探索
* 数据清洗
* 数据集成
* 数据变换
* 数据规约
## 4.构建模型
* 模式发现
* 构建模型
* 验证模型
## 5.模型评估
### 设定评估指标
> 1.绝对误差于相对误差
> 2.平均绝对误差
> 3.均方误差
> 4.均方根误差
> 5.平均绝对百分误差
> 6.Kappa统计
> 7.识别准确度（正确率）
> 8.识别精确度（ P值 ）
> 9.反馈率（ R值 ）
> 10.非均衡问题（ F值 ）
> 11.ROC曲线/AUC面积
> 12.混淆矩阵
* 多模型对比
* 模型优化
## 6.模型发布
* 模型部署
* 模型重构
================================================
FILE: docs/Kaggle/competitions/featured/mercari-price-suggestion-challenge/mercari_price_notebook.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"在这场比赛中，我们被要求预测一个商品销售的价格，所以这是一个回归问题"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt\n",
"from wordcloud import WordCloud\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"import string\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"train = pd.read_table('train.tsv', engine='c')\n",
"test = pd.read_table('test.tsv', engine='c')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 导入数据观看数据的维度,是否有缺失值,特征之间的关联度等等"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" train_id \n",
" name \n",
" item_condition_id \n",
" category_name \n",
" brand_name \n",
" price \n",
" shipping \n",
" item_description \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0 \n",
" MLB Cincinnati Reds T Shirt Size XL \n",
" 3 \n",
" Men/Tops/T-shirts \n",
" NaN \n",
" 10.0 \n",
" 1 \n",
" No description yet \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" Razer BlackWidow Chroma Keyboard \n",
" 3 \n",
" Electronics/Computers & Tablets/Components & P... \n",
" Razer \n",
" 52.0 \n",
" 0 \n",
" This keyboard is in great condition and works ... \n",
" \n",
" \n",
" 2 \n",
" 2 \n",
" AVA-VIV Blouse \n",
" 1 \n",
" Women/Tops & Blouses/Blouse \n",
" Target \n",
" 10.0 \n",
" 1 \n",
" Adorable top with a hint of lace and a key hol... \n",
" \n",
" \n",
" 3 \n",
" 3 \n",
" Leather Horse Statues \n",
" 1 \n",
" Home/Home Décor/Home Décor Accents \n",
" NaN \n",
" 35.0 \n",
" 1 \n",
" New with tags. Leather horses. Retail for [rm]... \n",
" \n",
" \n",
" 4 \n",
" 4 \n",
" 24K GOLD plated rose \n",
" 1 \n",
" Women/Jewelry/Necklaces \n",
" NaN \n",
" 44.0 \n",
" 0 \n",
" Complete with certificate of authenticity \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" train_id name item_condition_id \\\n",
"0 0 MLB Cincinnati Reds T Shirt Size XL 3 \n",
"1 1 Razer BlackWidow Chroma Keyboard 3 \n",
"2 2 AVA-VIV Blouse 1 \n",
"3 3 Leather Horse Statues 1 \n",
"4 4 24K GOLD plated rose 1 \n",
"\n",
" category_name brand_name price \\\n",
"0 Men/Tops/T-shirts NaN 10.0 \n",
"1 Electronics/Computers & Tablets/Components & P... Razer 52.0 \n",
"2 Women/Tops & Blouses/Blouse Target 10.0 \n",
"3 Home/Home Décor/Home Décor Accents NaN 35.0 \n",
"4 Women/Jewelry/Necklaces NaN 44.0 \n",
"\n",
" shipping item_description \n",
"0 1 No description yet \n",
"1 0 This keyboard is in great condition and works ... \n",
"2 1 Adorable top with a hint of lace and a key hol... \n",
"3 1 New with tags. Leather horses. Retail for [rm]... \n",
"4 0 Complete with certificate of authenticity "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 查看数据的维度\n",
"train_id 是数据id \n",
"name 是商品名 \n",
"item_condition_id 是卖方提供的物品状况 \n",
"category_name 是商品类别 \n",
"brand_name 是品牌 \n",
"price 是价格也就是我们要预测的y \n",
"shipping 是是否包邮 \n",
"item_description 是买家评价 "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"训练集大小:(1482535, 8)\n",
"测试集大小:(693359, 7)\n"
]
}
],
"source": [
"print ('训练集大小:{}\\n测试集大小:{}'.format(train.shape, test.shape))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 目标分布"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20, 15))\n",
"plt.hist(train['price'], bins=50, range=[0,250], label='price')\n",
"plt.title('Train \"price\" distribution', fontsize=15)\n",
"plt.xlabel('Price', fontsize=15)\n",
"plt.ylabel('Samples', fontsize=15)\n",
"plt.xticks(fontsize=15)\n",
"plt.yticks(fontsize=15)\n",
"plt.legend(fontsize=15)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 目标信息"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1.482535e+06\n",
"mean 2.673752e+01\n",
"std 3.858607e+01\n",
"min 0.000000e+00\n",
"25% 1.000000e+01\n",
"50% 1.700000e+01\n",
"75% 2.900000e+01\n",
"max 2.009000e+03\n",
"Name: price, dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train['price'].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们看到价格的均值在26.7左右,但是也有像2009那样的最大值,变量分布左偏待会所以让我们对价格进行对数转换"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20, 15))\n",
"bins=50\n",
"plt.hist(train[train['shipping']==1]['price'], bins, normed=True, range=[0,250],\n",
" alpha=0.6, label='price when shipping==1')\n",
"plt.hist(train[train['shipping']==0]['price'], bins, normed=True, range=[0,250],\n",
" alpha=0.6, label='price when shipping==0')\n",
"plt.title('Train price over shipping type distribution', fontsize=15)\n",
"plt.xlabel('Price', fontsize=15)\n",
"plt.ylabel('Normalized Samples', fontsize=15)\n",
"plt.legend(fontsize=15)\n",
"plt.xticks(fontsize=15)\n",
"plt.yticks(fontsize=15)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们发现发货是否包邮,并没有将价格分开.但这并不带表这个特征没有用处"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 填充缺失值"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这里用log1p来处理目标y是因为防止出现0和负数"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"y_train = np.log1p(train['price'])\n",
"train['category_name'] = train['category_name'].fillna('Other').astype(str)\n",
"train['brand_name'] = train['brand_name'].fillna('missing').astype(str)\n",
"train['shipping'] = train['shipping'].astype(str)\n",
"train['item_condition_id'] = train['item_condition_id'].astype(str)\n",
"train['item_description'] = train['item_description'].fillna('None')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 文本哑变量化"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.pipeline import FeatureUnion\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"default_preprocessor = CountVectorizer().build_preprocessor()\n",
"def build_preprocessor(field):\n",
" field_idx = list(train.columns).index(field)\n",
" return lambda x: default_preprocessor(x[field_idx])\n",
" \n",
"vectorizer = FeatureUnion([\n",
" ('name', CountVectorizer(\n",
" ngram_range=(1, 2),\n",
" max_features=5000,\n",
" preprocessor=build_preprocessor('name'))),\n",
" ('category_name', CountVectorizer(\n",
" token_pattern='.+',\n",
" preprocessor=build_preprocessor('category_name'))),\n",
" ('brand_name', CountVectorizer(\n",
" token_pattern='.+',\n",
" preprocessor=build_preprocessor('brand_name'))),\n",
" ('shipping', CountVectorizer(\n",
" token_pattern='\\d+',\n",
" preprocessor=build_preprocessor('shipping'))),\n",
" ('item_condition_id', CountVectorizer(\n",
" token_pattern='\\d+',\n",
" preprocessor=build_preprocessor('item_condition_id'))),\n",
" ('item_description', TfidfVectorizer(\n",
" ngram_range=(1, 3),\n",
" max_features=5000,\n",
" preprocessor=build_preprocessor('item_description'))),\n",
"])\n",
"X_train = vectorizer.fit_transform(train.values)\n",
"X_train"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 利用ridge算法进行预测"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_rmsle(y_true, y_pred):\n",
" return np.sqrt(mean_squared_log_error(np.expm1(y_true), np.expm1(y_pred)))\n",
"\n",
"cv = KFold(n_splits=10, shuffle=True, random_state=42)\n",
"for train_ids, valid_ids in cv.split(X_train):\n",
" model = Ridge(\n",
" solver='auto',\n",
" fit_intercept=True,\n",
" alpha=0.5,\n",
" max_iter=100,\n",
" normalize=False,\n",
" tol=0.05)\n",
" model.fit(X_train[train_ids], y_train[train_ids])\n",
" y_pred_valid = model.predict(X_train[valid_ids])\n",
" rmsle = get_rmsle(y_pred_valid, y_train[valid_ids])\n",
" print(f'valid rmsle: {rmsle:.5f}')\n",
" break"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 算法精确准确率并不高, 我们可以调高 在文本哑变量化中TfidfVectorizer 的max_feature 参数的值"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 单个ridge模型的准确率不行这时我们就可以用lightgbm了.进行多个模型组合具体可以看代码文件"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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================================================
FILE: docs/Kaggle/competitions/getting-started/digit-recognizer/README.md
================================================
# **数字识别**
## 比赛说明
* MNIST（"修改后的国家标准与技术研究所"）是计算机视觉的事实上的 "hello world" 数据集。自1999年发布以来，手写图像的经典数据集已成为基准分类算法的基础。随着新机器学习技术的出现，MNIST 仍然是研究人员和学习者的可靠资源。
* 在本次比赛中，您的目标是正确 [识别数以万计手写图像的数字](https://www.kaggle.com/c/digit-recognizer) 。我们策划了一套教程式的内核，涵盖从回归到神经网络的一切。我们鼓励您尝试使用不同的算法来学习第一手什么是有效的，以及技术如何比较。
> 注意：[项目规范](/docs/kaggle-quickstart.md)
## 成员角色
| 角色 | 用户 | 内容 | 代码 |
| -- | -- | -- | -- |
| 负责: knn | [诺木人](https://github.com/1mrliu) | [knn项目文档](/competitions/getting-started/digit-recognizer/knn算法描述.md) | [knn项目代码](/src/py3.x/kaggle/getting-started/digit-recognizer/knn-python3.6.py) |
| 负责: svm | [马小穆](https://github.com/maxiaomu) | [svm项目文档](/competitions/getting-started/digit-recognizer/svm算法描述.md) | [svm项目代码](/src/py3.x/kaggle/getting-started/digit-recognizer/svm-python3.6.py) |
| 负责: 随机森林 | [平淡的天](https://github.com/friedhelm739) | [随机森林项目文档](/competitions/getting-started/digit-recognizer/随机森林算法描述.md) | [随机森林项目代码](/src/py3.x/kaggle/getting-started/digit-recognizer/rf-python3.6.py) |
| 负责: 神经网络 | [平淡的天](https://github.com/friedhelm739) | [神经网络项目文档](/competitions/getting-started/digit-recognizer/神经网络算法描述.md) | [神经网络项目代码](/src/py3.x/kaggle/getting-started/digit-recognizer/nn-python3.6.py) |
| 负责: cnn | [==](https://github.com/xiaomingnio) | [cnn项目文档](/competitions/getting-started/digit-recognizer/cnn算法描述.md) | [cnn项目代码](/src/py3.x/kaggle/getting-started/digit-recognizer/keras_cnn-python3.6.py) |
> 数字识别 第一期(2018-04-18)
| 组长 | 组员 | 组员 | 组员 | 组员 | 组员 | 组员 |
| -- | -- | -- | -- | -- | -- | -- |
| [限定心态](https://github.com/island99) | [strengthen](https://github.com/Yestrengthen) | [VS53MV](https://github.com/La-fe) | [不会修电脑](https://github.com/smallsmallwood) | [远心](https://github.com/SwordFaith) | [小耀哥_0011](https://github.com/yirenrumeng) | [丨](https://github.com/nincro)|
> 数字识别 第二期(2018-04-21)
| 组长 | 组员 | 组员 | 组员 | 组员 | 组员 |
| -- | -- | -- | -- | -- | -- |
|[凌少skier](https://github.com/skierlin)|[Blue]() |[Max]()|[考拉]()|[Happyorg]()|[过客]()|
> 数字识别 第三期(2018-05-03)
| 负责人 | 组员 | 组员|
| -- | -- | -- |
|[技术负责人-诺木人](https://github.com/1mrliu)
[辅助负责人-平淡的心]()
[辅助负责人-张凯]()
[活动发起人-片刻](https://github.com/jiangzhonglian)| [ifeng](https://github.com/ifeng2025)
[draw](https://github.com/)
[Faith](https://github.com/77qingliu)
[ggggggggo](https://github.com/)
[嘿！漆漆](https://github.com/77const)
[kickfilper](https://github.com/yongfegnyan)
[Lucien Chen](https://github.com/hubeihubei)
[L~Q~W](https://github.com/)
[琴剑蓝天](https://github.com/xvjie)
[時間ｄāń漠](https://github.com/)
[歲寒✅已认证](https://github.com/)
[給力小青年](https://github.com/XCWDSG)
[星尘](https://github.com/wilderchen)
|[瑛瑛wang](https://github.com/)
[有一个人很酷](https://github.com/)
[静水流深](https://github.com/)
[♡稳稳的幸福](https://github.com/patrickwangqy)
[Verestràsz](https://github.com/soarchorale)
[vslyu](https://github.com/)
[:)]()
[菠菜](https://github.com/wpbshine)
[QQ小冰](https://github.com/Luyu-Liam)
[浅紫色](https://github.com/MarkerGo)
[R](https://github.com/rjdCS)
[ROOT](https://github.com/zch765536121) |
> 数字识别 第四期(2018-05-08)
| 负责人 | 组员 | 组员 | 组员 |
| :--: | :--: | :--: | :--: |
|[技术负责人-诺木人](https://github.com/1mrliu)
[辅助负责人-BrianCai](https://github.com/BrianCai)
[辅助负责人-嘿！漆漆](https://github.com/77const)
| [兰博归来](https://github.com/lanboguilai)
[柳生](https://github.com/liushengxu)
[ZARD Forever](https://github.com/boonguan)
[你别理我我没网](https://github.com/framelove)
[666](https://github.com/xuanbabybaby)
[黄蛟](https://github.com/jhuang111)
[冬冬](https://github.com/swdmike)
[荼蘼](https://github.com/mile)
[烁今](https://github.com/guessay) | [简雨](https://github.com/pengyuanqiuqiu)
[B0lt1st](https://github.com/B0lt1st)
[nickine](https://github.com/nickninth)
[dying in the sun](https://github.com/jialindeng)
[王琪琪]()
[常想一二]()
[以朱代墨]()
[Mang0](0xMJ)
[TonyZERO]()
| [冰花小子]()
[阿铮]()
[zh哲]()
[小菜鸡]()
[电酱prpr]()
[琉璃火]()
[张假飞]()
[HAN Shuai](https://github.com/rudyhan)
[有人@我]()
[天儿](https://github.com/smilesboy)
[Jaybo](https://github.com/strivebo)
|
## 开发流程
* 分类问题：0～9 数字
* 常用算法：knn、决策树、朴素贝叶斯、Logistic回归、SVM、集成方法（随机森林和 AdaBoost）
```
步骤:
一. 数据分析
1. 下载并加载数据
2. 总体预览数据:了解每列数据的含义,数据的格式等
3. 数据初步分析,使用统计学与绘图: 由于特征没有特殊的含义，不需要过多的细致分析
二. 特征工程
1.根据业务,常识,以及第二步的数据分析构造特征工程.
2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)
三. 模型选择
1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.
2.比较各个模型的分数,然后取效果较好的模型作为基础模型.
四. 模型融合
跳过，这个项目的重点是让大家都了解这个kaggle比赛怎么和算法更好的融合在一起。
五. 修改特征和模型参数
此处不做过多分析，主要是优化各个算法的参数。
* KNN => k值
* SVM => 惩罚系数，核函数
* RF => 树个数，树深度，叶子数
* PCA => 特征数 or 信息熵
```
## 一. 数据分析
> 数据下载和加载
数据集下载地址：https://www.kaggle.com/c/digit-recognizer/data
```python
import os
import csv
import datetime
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.neighbors import KNeighborsClassifier
data_dir = '/opt/data/kaggle/getting-started/digit-recognizer/'
# 加载数据
def opencsv():
# 使用 pandas 打开
data = pd.read_csv(os.path.join(data_dir, 'input/train.csv'))
data1 = pd.read_csv(os.path.join(data_dir, 'input/test.csv'))
train_data = data.values[0:, 1:] # 读入全部训练数据, [行，列]
train_label = data.values[0:, 0] # 读取列表的第一列
test_data = data1.values[0:, 0:] # 测试全部测试个数据
return train_data, train_label, test_data
# 加载数据
trainData, trainLabel, testData = opencsv()
```
> 总体预览数据(目标变量+数据特征)
* label: 目标变量（分类标签）
* pixel0~pixel783: 数据特征（分类属性），特征之间没有特别的业务联系（所以没必要进行统计分析了）
```
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```
## 二. 特征工程（特征之间关系不大，此步骤跳过）
1. 当然可以做一些归一化的操作
2. 做降维：因为有些列一直都为了0，信息熵几乎为0，没有使用的必要
```python
# 数据预处理-降维 PCA主成成分分析
def dRPCA(x_train, x_test, COMPONENT_NUM):
print('dimensionality reduction...')
trainData = np.array(x_train)
testData = np.array(x_test)
'''
使用说明：https://www.cnblogs.com/pinard/p/6243025.html
n_components>=1
n_components=NUM 设置占特征数量比
0 < n_components < 1
n_components=0.99 设置阈值总方差占比
'''
pca = PCA(n_components=COMPONENT_NUM, whiten=False)
pca.fit(trainData) # Fit the model with X
pcaTrainData = pca.transform(trainData) # Fit the model with X and 在X上完成降维.
pcaTestData = pca.transform(testData) # Fit the model with X and 在X上完成降维.
# pca 方差大小、方差占比、特征数量
# print("方差大小:\n", pca.explained_variance_, "方差占比:\n", pca.explained_variance_ratio_)
print("特征数量: %s" % pca.n_components_)
print("总方差占比: %s" % sum(pca.explained_variance_ratio_))
return pcaTrainData, pcaTestData
# 降维处理
trainDataPCA, testDataPCA = dRPCA(trainData, testData, 0.8)
```
## 三. 模型选择
1. 根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.
2. 比较各个模型的分数,然后取效果较好的模型作为基础模型.
* 分类问题：0～9 数字
* 常用算法：knn、决策树、朴素贝叶斯、Logistic回归、SVM、集成方法（随机森林和 AdaBoost）
> knn
```python
def trainModel(trainData, trainLabel):
clf = KNeighborsClassifier() # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)
clf.fit(trainData, np.ravel(trainLabel)) # ravel Return a contiguous flattened array.
return clf
# 模型训练
clf = trainModel(trainDataPCA, trainLabel)
# 结果预测
testLabel = clf.predict(testDataPCA)
```
> svm
```python
# 训练模型
def trainModel(trainData, trainLabel):
print('Train SVM...')
clf = SVC(C=4, kernel='rbf')
clf.fit(trainData, trainLabel) # 训练SVM
return clf
# 模型训练
clf = trainModel(trainDataPCA, y_train)
# 结果预测
testLabel = clf.predict(pcaTestData)
```
> RF - Random Forest
```python
# 训练模型
def trainModel(X_train, y_train):
print('Train RF...')
clf = RandomForestClassifier(
n_estimators=10,
max_depth=10,
min_samples_split=2,
min_samples_leaf=1,
random_state=34)
clf.fit(X_train, y_train) # 训练rf
return clf
# 模型训练
clf = trainModel(trainDataPCA, y_train)
# 结果预测
testLabel = clf.predict(pcaTestData)
```
> 结果导出
```python
def saveResult(result, csvName):
with open(csvName, 'wb') as myFile:
myWriter = csv.writer(myFile)
myWriter.writerow(["ImageId", "Label"])
index = 0
for i in result:
tmp = []
index = index+1
tmp.append(index)
# tmp.append(i)
tmp.append(int(i))
myWriter.writerow(tmp)
# 结果的输出
saveResult(testLabel, '/opt/data/kaggle/getting-started/digit-recognizer/output/Result_xxx.csv')
```
## 四. 模型融合
跳过，这个项目的重点是让大家都了解这个kaggle比赛怎么和算法更好的融合在一起。
## 五. 修改特征和模型参数
此处不做过多分析，主要是优化各个算法的参数。
* [KNN](https://github.com/apachecn/AiLearning/blob/master/docs/ml/2.k-近邻算法.md) => k值
* [SVM](https://github.com/apachecn/AiLearning/blob/master/docs/ml/6.支持向量机.md) => 惩罚系数，核函数
* [RF](https://github.com/apachecn/AiLearning/blob/master/docs/ml/7.集成方法-随机森林和AdaBoost.md) => 树个数，树深度，叶子数
* [PCA](https://github.com/apachecn/AiLearning/blob/master/docs/ml/13.利用PCA来简化数据.md) => 特征数 or 信息熵
================================================
FILE: docs/Kaggle/competitions/getting-started/digit-recognizer/cnn算法描述.md
================================================
# 1.导入需要的库
## 1.1.导入一些必要的库，如pandas、numpy、matplotlib、sklearn
## 1.2.导入keras（tensorflow backend），用来搭建神经网络
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import seaborn as sns
#将那些用matplotlib绘制的图显示在页面里而不是弹出一个窗口
%matplotlib inline
np.random.seed(2)
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import itertools
from keras.utils.np_utils import to_categorical # 转换成 one-hot-encoding
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.optimizers import adam, RMSprop
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau
```
Using TensorFlow backend.
# 2.数据准备工作
## 2.1.导入数据
```python
# Load the data
train = pd.read_csv(r'''/home/cd/kaggle-master/datasets/getting-started/digit-recognizer/input/train.csv''')
test = pd.read_csv(r'''/home/cd/kaggle-master/datasets/getting-started/digit-recognizer/input/test.csv''')
X_train = train.values[:,1:]
Y_train = train.values[:,0]
test=test.values
```
## 2.2.标准化
```python
# Normalization
X_train = X_train / 255.0
test = test / 255.0
```
## 2.3.将数组维度变成（28，28，1）
### 之前用pandas导入数据的时候会将数据变成一维数组
```python
X_train = X_train.reshape(-1,28,28,1)
test = test.reshape(-1,28,28,1)
```
## 2.4.将标签编码为one-hot编码
### 如：2 -> [0,0,1,0,0,0,0,0,0,0]
### 7 -> [0,0,0,0,0,0,0,1,0,0]
```python
Y_train = to_categorical(Y_train, num_classes = 10)
```
## 2.5.将训练集随机划分成训练集和验证集
### 设置随机数种子
```python
random_seed = 2
```
```python
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed)
```
# 3.CNN
## 3.1.定义cnn模型
### cnn结构为[[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out
```python
model = Sequential()
model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same',
activation ='relu', input_shape = (28,28,1)))
model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',
activation ='relu'))
model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation = "softmax"))
```
## 3.2.设置优化器
### 优化算法选用自适应学习率算法
[RMSprop](http://blog.csdn.net/bvl10101111/article/details/72616378)
### 选择默认参数
```python
# Define the optimizer
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
```
## 3.3.编译模型
### 优化算法选择RMSprop
### 损失函数选择categorical_crossentropy，亦称作多类的对数损失
### 性能评估方法选择准确率
```python
# Compile the model
model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"])
```
```python
epochs = 30
batch_size = 86
```
### 设置学习率退火器
### *当评价指标不在提升时，减少学习率
### *监测量是val_acc，当3个epoch过去而模型性能不提升时，学习率减少的动作会触发
### *factor：每次减少学习率的因子，学习率将以lr = lr×factor的形式被减少
### *min_lr：学习率的下限
### *回调函数是一组在训练的特定阶段被调用的函数集，你可以使用回调函数来观察训练过程中网络内部的状态和统计信息。
```python
# Set a learning rate annealer
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001)
```
## 3.4.设置图片生成器
### 用以生成一个batch的图像数据，支持实时数据提升。训练时该函数会无限生成数据，直到达到规定的epoch次数为止。
```python
datagen = ImageDataGenerator(
featurewise_center=False, # 使输入数据集去中心化（均值为0）, 按feature执行
samplewise_center=False, # 使输入数据的每个样本均值为0
featurewise_std_normalization=False, # 将输入除以数据集的标准差以完成标准化, 按feature执行
samplewise_std_normalization=False, # 将输入的每个样本除以其自身的标准差
zca_whitening=False, # 对输入数据施加ZCA白化
rotation_range=10, # 数据增强时图片随机转动的角度
zoom_range = 0.1, # 随机缩放的幅度
width_shift_range=0.1, # 图片宽度的某个比例，数据增强时图片水平偏移的幅度
height_shift_range=0.1, # 图片高度的某个比例，数据增强时图片竖直偏移的幅度
horizontal_flip=False, # 进行随机水平翻转
vertical_flip=False) # 进行随机竖直翻转
```
### 计算依赖于数据的变换所需要的统计信息(均值方差等
```python
datagen.fit(X_train)
```
## 3.5.训练模型
### fit_generator：利用Python的生成器，逐个生成数据的batch并进行训练。生成器与模型将并行执行以提高效率
### datagen.flow（）：生成器函数，接收numpy数组和标签为参数,生成经过数据增强或标准化后的batch数据,并在一个无限循环中不断的返回batch数据
### callbacks=[learning_rate_reduction]：回调函数，这个list中的回调函数将会在训练过程中的适当时机被调用
```python
import datetime
starttime = datetime.datetime.now()
history = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),
epochs = epochs, validation_data = (X_val,Y_val),
verbose = 2, steps_per_epoch=X_train.shape[0] // batch_size
, callbacks=[learning_rate_reduction])
endtime = datetime.datetime.now()
print ((endtime - starttime).seconds)
```
Epoch 1/30
- 10s - loss: 0.0352 - acc: 0.9900 - val_loss: 0.0174 - val_acc: 0.9943
Epoch 2/30
- 10s - loss: 0.0322 - acc: 0.9908 - val_loss: 0.0176 - val_acc: 0.9952
Epoch 3/30
- 10s - loss: 0.0353 - acc: 0.9895 - val_loss: 0.0177 - val_acc: 0.9950
Epoch 4/30
- 9s - loss: 0.0320 - acc: 0.9908 - val_loss: 0.0172 - val_acc: 0.9950
Epoch 5/30
- 9s - loss: 0.0350 - acc: 0.9903 - val_loss: 0.0173 - val_acc: 0.9952
Epoch 6/30
Epoch 00006: reducing learning rate to 1e-05.
- 10s - loss: 0.0346 - acc: 0.9897 - val_loss: 0.0170 - val_acc: 0.9948
Epoch 7/30
- 10s - loss: 0.0350 - acc: 0.9897 - val_loss: 0.0171 - val_acc: 0.9948
Epoch 8/30
- 10s - loss: 0.0338 - acc: 0.9907 - val_loss: 0.0170 - val_acc: 0.9950
Epoch 9/30
- 10s - loss: 0.0355 - acc: 0.9896 - val_loss: 0.0172 - val_acc: 0.9948
Epoch 10/30
- 9s - loss: 0.0335 - acc: 0.9907 - val_loss: 0.0174 - val_acc: 0.9948
Epoch 11/30
- 9s - loss: 0.0318 - acc: 0.9903 - val_loss: 0.0171 - val_acc: 0.9948
Epoch 12/30
- 10s - loss: 0.0348 - acc: 0.9902 - val_loss: 0.0173 - val_acc: 0.9948
Epoch 13/30
- 10s - loss: 0.0337 - acc: 0.9902 - val_loss: 0.0170 - val_acc: 0.9950
Epoch 14/30
- 10s - loss: 0.0344 - acc: 0.9902 - val_loss: 0.0172 - val_acc: 0.9948
Epoch 15/30
- 9s - loss: 0.0339 - acc: 0.9900 - val_loss: 0.0171 - val_acc: 0.9950
Epoch 16/30
- 10s - loss: 0.0338 - acc: 0.9904 - val_loss: 0.0168 - val_acc: 0.9948
Epoch 17/30
- 10s - loss: 0.0342 - acc: 0.9902 - val_loss: 0.0166 - val_acc: 0.9950
Epoch 18/30
- 10s - loss: 0.0358 - acc: 0.9903 - val_loss: 0.0169 - val_acc: 0.9950
Epoch 19/30
- 9s - loss: 0.0339 - acc: 0.9903 - val_loss: 0.0166 - val_acc: 0.9950
Epoch 20/30
- 10s - loss: 0.0356 - acc: 0.9903 - val_loss: 0.0166 - val_acc: 0.9950
Epoch 21/30
- 10s - loss: 0.0350 - acc: 0.9900 - val_loss: 0.0165 - val_acc: 0.9952
Epoch 22/30
- 10s - loss: 0.0350 - acc: 0.9899 - val_loss: 0.0169 - val_acc: 0.9950
Epoch 23/30
- 10s - loss: 0.0353 - acc: 0.9898 - val_loss: 0.0171 - val_acc: 0.9948
Epoch 24/30
- 9s - loss: 0.0325 - acc: 0.9904 - val_loss: 0.0167 - val_acc: 0.9948
Epoch 25/30
- 10s - loss: 0.0359 - acc: 0.9892 - val_loss: 0.0168 - val_acc: 0.9948
Epoch 26/30
- 10s - loss: 0.0349 - acc: 0.9901 - val_loss: 0.0163 - val_acc: 0.9948
Epoch 27/30
- 10s - loss: 0.0328 - acc: 0.9908 - val_loss: 0.0166 - val_acc: 0.9948
Epoch 28/30
- 10s - loss: 0.0331 - acc: 0.9910 - val_loss: 0.0166 - val_acc: 0.9952
Epoch 29/30
- 10s - loss: 0.0343 - acc: 0.9903 - val_loss: 0.0173 - val_acc: 0.9943
Epoch 30/30
- 9s - loss: 0.0346 - acc: 0.9902 - val_loss: 0.0166 - val_acc: 0.9948
287
## 可以看到准确率大概在0.995左右，训练时间为287s (GPU加速后)
### 参考
[代码](https://www.kaggle.com/yassineghouzam/introduction-to-cnn-keras-0-997-top-6)
[keras文档](https://keras-cn.readthedocs.io/en/latest/)
### 预测和提交结果
# predict results
results = model.predict(test)
# select the indix with the maximum probability
results = np.argmax(results,axis = 1)
results = pd.Series(results,name="Label")
submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),results],axis = 1)
submission.to_csv("datasets/getting-started/digit-recognizer/ouput/Result_keras_CNN.csv",index=False)
================================================
FILE: docs/Kaggle/competitions/getting-started/digit-recognizer/knn算法描述.md
================================================
# KNN 概述
k-近邻（kNN, k-NearestNeighbor）算法是一种基本分类与回归方法，我们这里只讨论分类问题中的 k-近邻算法。
一句话总结：近朱者赤近墨者黑！
k 近邻算法的输入为实例的特征向量，对应于特征空间的点；输出为实例的类别，可以取多类。k 近邻算法假设给定一个训练数据集，其中的实例类别已定。分类时，对新的实例，根据其 k 个最近邻的训练实例的类别，通过多数表决等方式进行预测。因此，k近邻算法不具有显式的学习过程。
k 近邻算法实际上利用训练数据集对特征向量空间进行划分，并作为其分类的“模型”。 k值的选择、距离度量以及分类决策规则是k近邻算法的三个基本要素。
# KNN场景
电影可以按照题材分类，那么如何区分动作片和爱情片呢？
1. 动作片 打斗次数更多
2. 爱情片 亲吻次数更多
基于电影中的亲吻、打斗出现的次数，使用 k-近邻算法构造程序，就可以自动划分电影的题材类型。现在根据上面我们得到的样本集中所有电影与未知电影的距离，按照距离递增排序，可以找到 k 个距离最近的电影。
假定 k=3，则三个最靠近的电影依次是， He's Not Really into Dudes 、 Beautiful Woman 和 California Man。
knn 算法按照距离最近的三部电影的类型，决定未知电影的类型，而这三部电影全是爱情片，因此我们判定未知电影是爱情片。
# KNN原理
## KNN工作原理
1. 假设有一个带有标签的样本数据集（训练样本集），其中包含每条数据与所属分类的对应关系。
2. 输入没有标签的新数据后，将新数据的每个特征与样本集中数据对应的特征进行比较。
* 计算新数据与样本数据集中每条数据的距离。
* 对求得的所有距离进行排序（从小到大，越小表示越相似）。
* 取前k（k 一般小于等于 20 ）个样本数据对应的分类标签。
3. 求 k 个数据中出现次数最多的分类标签作为新数据的分类。
## KNN通俗理解
给定一个训练数据集，对新的输入实例，在训练数据集中找到与该实例最邻近的 k 个实例，这 k 个实例的多数属于某个类，就把该输入实例分为这个类。
## KNN开发流程
```
收集数据：任何方法
准备数据：距离计算所需要的数值，最好是结构化的数据格式
分析数据：任何方法
训练算法：此步骤不适用于 k-近邻算法
测试算法：计算错误率
使用算法：输入样本数据和结构化的输出结果，然后运行 k-近邻算法判断输入数据分类属于哪个分类，最后对计算出的分类执行后续处理
```
## KNN算法特点
优点：精度高、对异常值不敏感、无数据输入假定
缺点：计算复杂度高、空间复杂度高
适用数据范围：数值型和标称型
# KNN项目案例
> 收集数据: 提供文本文件(目标变量+数据特征)
```
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4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,220,179,6,0,0,0,0,0,0,0,0,9,77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,247,17,0,0,0,0,0,0,0,0,27,202,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,242,155,0,0,0,0,0,0,0,0,27,254,63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,160,207,6,0,0,0,0,0,0,0,27,254,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,127,254,21,0,0,0,0,0,0,0,20,239,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,77,254,21,0,0,0,0,0,0,0,0,195,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,70,254,21,0,0,0,0,0,0,0,0,195,142,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,56,251,21,0,0,0,0,0,0,0,0,195,227,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,222,153,5,0,0,0,0,0,0,0,120,240,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,67,251,40,0,0,0,0,0,0,0,94,255,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,234,184,0,0,0,0,0,0,0,19,245,69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,234,169,0,0,0,0,0,0,0,3,199,182,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,154,205,4,0,0,26,72,128,203,208,254,254,131,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,61,254,129,113,186,245,251,189,75,56,136,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,216,233,233,159,104,52,0,0,0,38,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,254,73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,206,106,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,186,159,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,209,101,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,25,130,155,254,254,254,157,30,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,103,253,253,253,253,253,253,253,253,114,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,208,253,253,253,253,253,253,253,253,253,253,107,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,31,253,253,253,253,253,253,253,253,253,253,253,215,101,3,0,0,0,0,0,0,0,0,0,0,0,0,23,210,253,253,253,248,161,222,222,246,253,253,253,253,253,39,0,0,0,0,0,0,0,0,0,0,0,0,136,253,253,253,229,77,0,0,0,70,218,253,253,253,253,215,91,0,0,0,0,0,0,0,0,0,0,5,214,253,253,253,195,0,0,0,0,0,104,224,253,253,253,253,215,29,0,0,0,0,0,0,0,0,0,116,253,253,253,247,75,0,0,0,0,0,0,26,200,253,253,253,253,216,4,0,0,0,0,0,0,0,0,254,253,253,253,195,0,0,0,0,0,0,0,0,26,200,253,253,253,253,5,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,25,231,253,253,253,36,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,0,223,253,253,253,129,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,0,127,253,253,253,129,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,0,139,253,253,253,90,0,0,0,0,0,0,0,0,254,253,253,253,99,0,0,0,0,0,0,0,0,0,78,248,253,253,253,5,0,0,0,0,0,0,0,0,254,253,253,253,216,34,0,0,0,0,0,0,0,33,152,253,253,253,107,1,0,0,0,0,0,0,0,0,206,253,253,253,253,140,0,0,0,0,0,30,139,234,253,253,253,154,2,0,0,0,0,0,0,0,0,0,16,205,253,253,253,250,208,106,106,106,200,237,253,253,253,253,209,22,0,0,0,0,0,0,0,0,0,0,0,82,253,253,253,253,253,253,253,253,253,253,253,253,253,209,22,0,0,0,0,0,0,0,0,0,0,0,0,1,91,253,253,253,253,253,253,253,253,253,253,213,90,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,18,129,208,253,253,253,253,159,129,90,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
```
> 准备数据: 分离数据特征和目标变量，加载测试数据
```python
# 加载数据
def opencsv():
# 使用 pandas 打开
data = pd.read_csv('datasets/getting-started/digit-recognizer/input/train.csv')
data1 = pd.read_csv('datasets/getting-started/digit-recognizer/input/test.csv')
train_data = data.values[0:, 1:] # 读入全部训练数据
train_label = data.values[0:, 0]
test_data = data1.values[0:, 0:] # 测试全部测试个数据
return train_data, train_label, test_data
trainData, trainLabel, testData = opencsv()
```
> 模型训练: 产生训练模型
```python
# 模型训练
def knnClassify(trainData, trainLabel):
knnClf = KNeighborsClassifier() # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)
knnClf.fit(trainData, ravel(trainLabel))
return knnClf
knnClf = knnClassify(trainData, trainLabel)
```
> 模型评估: 用于评估结果的正确率和召回率
`暂时没写，后面会进行优化`
> 结果预测: 通过模型来预测新来数据的结果
```python
# 结果预测
testLabel = knnClf.predict(testData)
```
> 结果导出
```python
def saveResult(result, csvName):
with open(csvName, 'wb') as myFile:
myWriter = csv.writer(myFile)
myWriter.writerow(["ImageId", "Label"])
index = 0
for i in result:
tmp = []
index = index+1
tmp.append(index)
# tmp.append(i)
tmp.append(int(i))
myWriter.writerow(tmp)
# 结果的输出
saveResult(testLabel, 'datasets/getting-started/digit-recognizer/ouput/Result_sklearn_knn.csv')
```
================================================
FILE: docs/Kaggle/competitions/getting-started/digit-recognizer/svm算法描述.md
================================================
# 基于SVM的数字识别算法研究
## SVM 概述
支持向量机(Support Vector Machines, SVM)：是机器学习当中的一种有监督的学习模型，可以应用于求解分类和回归问题。
## SVM 直观认识
reddit上的[Iddo](http://bytesizebio.net/2014/02/05/support-vector-machines-explained-well/)用了一个很好的例子解释了SVM。
对应于SVM来说，这些球叫做 data，棍子叫做 classifie r,最大距离叫做 optimization， 拍桌子叫做 kernelling, 那张纸叫 hyperplane
## SVM 原理
将上述的直观认识转化为我们最熟悉的数学模型，其实主要内容就是四个部分：
1. SVM的基本原理，从可分到不可分，从线性到非线性
2. 关于带有约束优化的求解方法：优化拉格朗日乘子法和KKT条件
3. 核函数的重要意义
4. 对于优化速度提升的一个重要方法：SMO算法
参考下边的几个博客，内容十分详细，然后回顾下边的两张图：由于公式太多，这两张图列出了一些主要的公式，并且按照SVM的求解思想将整个思路串起来。
引用July大神的一句话：“我相信，SVM理解到了一定程度后，是的确能在脑海里从头至尾推导出相关公式的，最初分类函数，最大化分类间隔，max1/||w||，min1/2||w||^2，凸二次规划，拉格朗日函数，转化为对偶问题，SMO算法，都为寻找一个最优解，一个最优分类平面。”
## SVM应用 数字识别
### 数据集的介绍
在分类研究之前，首先我们需要了解数据的形式和要做的任务：
手写数字数据集MNIST(Modified National Institute of Standards and Technology)：该数据集是大约4W多图片和标签的组合，2W多待测图片，图片为28*28像素的灰度图，每个像素点的灰度为0到255，标签为该图片中的数字，为0-9中的一个整数。
需要下载的文件：
* train.csv 训练数据，每行有785列，第一列为标签(label)，之后784*列，每列c为一个像素的灰度值，该像素值对应的坐标为(c/28,c%28)
* test.csv 需要进行分类的数据，每行有784列，没有第一列标签，其他和训练数据一致
任务是根据训练集训练好自己的模型，然后利用模型对测试集进行分类，并输出成csv的形式存储起来。
### 实现步骤
> 收集数据
从kaggle中下载对应的数据集
test.csv
train.csv
文本文件格式：
```python
# train.csv
label,pixel0,pixel1,pixel2 ... pixel782,pixel783
3 0 0 0 ... 0 0
7 0 0 0 ... 0 0
2 0 0 255 ... 0 0
8 0 1 52 ... 0 0
# test.csv
pixel0,pixel1 ... pixel781,pixel782,pixel783
0 0 ... 68 0 0
0 0 ... 74 55 0
0 0 ... 38 0 0
0 1 ... 0 0 0
```
> 准备数据
这部分主要是加载test.csv和train.csv文件到我们程序当中，通过pandas库文件打开
```python
# 加载数据
def opencsv():
print('Load Data...')
# 使用 pandas 打开
dataTrain = pd.read_csv('datasets/getting-started/digit-recognizer/input/train.csv')
dataTest = pd.read_csv('datasets/getting-started/digit-recognizer/input/test.csv')
trainData = dataTrain.values[:, 1:] # 读入全部训练数据
trainLabel = dataTrain.values[:, 0] # 读入对应的第一列的标签
testData = dataTest.values[:, :] # 测试全部测试个数据
return trainData, trainLabel, testData
```
> 分析数据:
对数据的分析是一个特别重要的过程，不仅会让我们对数据集有一个直观的认识，更重要的是为在后续优化当中提供理论依据。
```python
def analyse_data(dataMat):
meanVals = np.mean(dataMat, axis=0)
meanRemoved = dataMat-meanVals
covMat = np.cov(meanRemoved, rowvar=0)
eigvals, eigVects = np.linalg.eig(np.mat(covMat))
eigValInd = np.argsort(eigvals)
topNfeat = 100
eigValInd = eigValInd[:-(topNfeat+1):-1]
cov_all_score = float(sum(eigvals))
sum_cov_score = 0
for i in range(0, len(eigValInd)):
line_cov_score = float(eigvals[eigValInd[i]])
sum_cov_score += line_cov_score
'''
我们发现其中有超过20%的特征值都是0。
这就意味着这些特征都是其他特征的副本，也就是说，它们可以通过其他特征来表示，而本身并没有提供额外的信息。
最前面15个值的数量级大于10^5，实际上那以后的值都变得非常小。
这就相当于告诉我们只有部分重要特征，重要特征的数目也很快就会下降。
最后，我们可能会注意到有一些小的负值，他们主要源自数值误差应该四舍五入成0.
'''
print('主成分：%s, 方差占比：%s%%, 累积方差占比：%s%%' % (format(i+1, '2.0f'), format(line_cov_score/cov_all_score*100, '4.2f'), format(sum_cov_score/cov_all_score*100, '4.1f')))
```
通过对数据的特征分析我们可以知道数据之间存在有很严重的相关性，那么我们可以在分类的过程中考虑提取出重要的特征。
```python
def dRCsv(x_train, x_test, preData, COMPONENT_NUM):
'''
x_train:训练集中的0.9作为训练集
x_test:训练集中的0.1作为验证集
preData:测试集
COMPONENT_NUM:保留的特征数
'''
print('dimensionality reduction...')
trainData = np.array(x_train)
testData = np.array(x_test)
preData = np.array(preData)
pca = PCA(n_components=COMPONENT_NUM, whiten=True)
pca.fit(trainData) # Fit the model with X
pcaTrainData = pca.transform(trainData) # Fit the model with X and 在X上完成降维.
pcaTestData = pca.transform(testData) # Fit the model with X and 在X上完成降维.
pcaPreData = pca.transform(preData) # Fit the model with X and 在X上完成降维.
return pcaTrainData, pcaTestData, pcaPreData
def trainModel(trainData, trainLabel):
print('Train SVM...')
svmClf = SVC(C=4, kernel='rbf')
svmClf.fit(trainData, trainLabel) # 训练SVM
return svmClf
```
> 训练模型:
根据加载分析数据后结果，我们借用网格搜索的思想，把要保留的特征在一定范围内选取最优，找出最高准确率，并且把最高准确率的模型存储起来
```python
#训练过程
def trainDRSVM():
startTime = time.time()
# 加载数据
trainData, trainLabel, preData = opencsv()
# 模型训练 (数据预处理-降维)
optimalSVMClf, pcaPreData = getOptimalAccuracy(trainData, trainLabel, preData)
storeModel(optimalSVMClf, 'datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.model')
storeModel(pcaPreData, 'datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.pcaPreData')
print("finish!")
stopTime = time.time()
print('TrainModel store time used:%f s' % (stopTime - startTime))
# 训练模型
def trainModel(trainData, trainLabel):
print('Train SVM...')
svmClf = SVC(C=4, kernel='rbf')
svmClf.fit(trainData, trainLabel) # 训练SVM
return svmClf
# 存储模型
def storeModel(model, filename):
import pickle
with open(filename, 'wb') as fw:
pickle.dump(model, fw)
# 找出最高准确率
def getOptimalAccuracy(trainData, trainLabel, preData):
# 分析数据 100个特征左右
# analyse_data(trainData)
x_train, x_test, y_train, y_test = train_test_split(trainData, trainLabel, test_size=0.1)
lineLen, featureLen = np.shape(x_test)
# print(lineLen, type(lineLen), featureLen, type(featureLen))
minErr = 1
minSumErr = 0
optimalNum = 1
optimalLabel = []
optimalSVMClf = None
pcaPreDataResult = None
for i in range(30, 45, 1):
# 评估训练结果
pcaTrainData, pcaTestData, pcaPreData = dRCsv(x_train, x_test, preData, i)
svmClf = trainModel(pcaTrainData, y_train)
svmtestLabel = svmClf.predict(pcaTestData)
errArr = np.mat(np.ones((lineLen, 1)))
sumErrArr = errArr[svmtestLabel != y_test].sum()
sumErr = sumErrArr/lineLen
print('i=%s' % i, lineLen, sumErrArr, sumErr)
if sumErr <= minErr:
minErr = sumErr
minSumErr = sumErrArr
optimalNum = i
optimalSVMClf = svmClf
optimalLabel = svmtestLabel
pcaPreDataResult = pcaPreData
print("i=%s >>>>> \t" % i, lineLen, int(minSumErr), 1-minErr)
'''
展现 准确率与召回率
precision 准确率
recall 召回率
f1-score 准确率和召回率的一个综合得分
support 参与比较的数量
参考链接：http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report
'''
# target_names 以 y的label分类为准
# target_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
target_names = [str(i) for i in list(set(y_test))]
print(target_names)
print(classification_report(y_test, optimalLabel, target_names=target_names))
print("特征数量= %s, 存在最优解：>>> \t" % optimalNum, lineLen, int(minSumErr), 1-minErr)
return optimalSVMClf, pcaPreDataResult
```
> 测试算法：便携一个函数来测试不同的和函数并计算错误率
加载训练好存储的模型，对测试集进行分类并存储结果
```python
def preDRSVM():
startTime = time.time()
# 加载模型和数据
optimalSVMClf = getModel('datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.model')
pcaPreData = getModel('datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.pcaPreData')
# 结果预测
testLabel = optimalSVMClf.predict(pcaPreData)
#print("testLabel = %f" % testscore)
# 结果的输出
saveResult(testLabel, 'datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.csv')
print("finish!")
stopTime = time.time()
print('PreModel load time used:%f s' % (stopTime - startTime))
```
> 使用算法：
根据之前的分析过程，用一个简短的可直接执行的程序总结整体的处理过程，包括加载数据，数据降维，模型训练，数据测试，输出结果
```python
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import csv
import time
import pandas as pd
import numpy as np
from numpy import *
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
# 加载数据
def opencsv():
print('Load Data...')
# 使用 pandas 打开
dataTrain = pd.read_csv(r'datasets/getting-started/digit-recognizer/input/train.csv')
dataTest = pd.read_csv(r'datasets/getting-started/digit-recognizer/input/test.csv')
trainData = dataTrain.values[:, 1:] # 读入全部训练数据
trainLabel = dataTrain.values[:, 0]
preData = dataTest.values[:, :] # 测试全部测试个数据
return trainData, trainLabel,preData
def dRCsv(x_train, x_test, preData, COMPONENT_NUM):
print('dimensionality reduction...')
trainData = np.array(x_train)
testData = np.array(x_test)
preData = np.array(preData)
pca = PCA(n_components=COMPONENT_NUM, whiten=True)
pca.fit(trainData) # Fit the model with X
pcaTrainData = pca.transform(trainData) # Fit the model with X and 在X上完成降维.
pcaTestData = pca.transform(testData) # Fit the model with X and 在X上完成降维.
pcaPreData = pca.transform(preData) # Fit the model with X and 在X上完成降维.
print(sum(pca.explained_variance_ratio_))
return pcaTrainData, pcaTestData, pcaPreData
def svmClassify(trainData, trainLabel):
print('Train SVM...')
svmClf=SVC(C=4, kernel='rbf')
svmClf.fit(trainData, trainLabel) # 训练SVM
return svmClf
def saveResult(result, csvName):
with open(csvName, 'wb') as myFile:
myWriter = csv.writer(myFile)
myWriter.writerow(["ImageId", "Label"])
index = 0
for i in result:
tmp = []
index = index+1
tmp.append(index)
# tmp.append(i)
tmp.append(int(i))
myWriter.writerow(tmp)
def SVM():
#加载数据
start_time = time.time()
trainData, trainLable,preData=opencsv()
print("load data finish")
stop_time_l = time.time()
print('load data time used:%f' % (stop_time_l - start_time))
trainData, testData,trainLable,testLabletrue = train_test_split(trainData, trainLable, test_size=0.1, random_state=41)#交叉验证 测试集10%
#pca降维
trainData,testData,preData =dRCsv(trainData,testData,preData,35)
# print (trainData,trainLable)
# 模型训练
svmClf=svmClassify(trainData, trainLable)
print ('trainsvm finished')
# 结果预测
testLable=svmClf.predict(testData)
preLable=svmClf.predict(preData)
#交叉验证
zeroLable=testLabletrue-testLable
rightCount=0
for i in range(len(zeroLable)):
if zeroLable[i]==0:
rightCount+=1
print ('the right rate is:',float(rightCount)/len(zeroLable))
# 结果的输出
saveResult(preLable, r'datasets/getting-started/digit-recognizer/ouput/Result_sklearn_SVM.csv')
print( "finish!")
stop_time_r = time.time()
print('classify time used:%f' % (stop_time_r - start_time))
if __name__ == '__main__':
SVM()
```
参考文献：
[1] http://bytesizebio.net/2014/02/05/support-vector-machines-explained-well/
[2] http://www.cnblogs.com/en-heng/p/5965438.html
[3] http://blog.csdn.net/on2way/article/details/47729419
[4] http://blog.csdn.net/v_july_v/article/details/7624837
================================================
FILE: docs/Kaggle/competitions/getting-started/digit-recognizer/神经网络算法描述.md
================================================
# 树模型
> 神经网络
本节选用sklearn的神经网络模型进行预测,神经网络是cnn的基础，也是机器学习最重要的组成部分，想要更上一层楼的同学需要把神经网络搞明白，具体可参考周志华的《机器学习》，还有花书 如果不想翻书的话，关于神经网络入门的博客一捏一大把，我就放一个连接吧，主要靠大家自己~
https://blog.csdn.net/a819825294/article/details/53393837
> 代码
```python
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import pandas as pd
import numpy as np
train_data=pd.read_csv(r"C:\Users\312\Desktop\digit-recognizer\train.csv")
test_data=pd.read_csv(r"C:\Users\312\Desktop\digit-recognizer\test.csv")
data=pd.concat([train_data,test_data],axis=0).reset_index(drop=True)
data.drop(['label'],axis=1,inplace=True)
label=train_data.label
pca=PCA(n_components=100, random_state=34)
data_pca=pca.fit_transform(data)
Xtrain,Ytrain,xtest,ytest=train_test_split(data_pca[0:len(train_data)],label,test_size=0.1, random_state=34)
clf=MLPClassifier(hidden_layer_sizes=(100, ), activation='relu', alpha=0.0001,learning_rate='constant', learning_rate_init=0.001,max_iter=200, shuffle=True, random_state=34)
clf.fit(Xtrain,xtest)
y_predict=clf.predict(Ytrain)
zeroLable=ytest-y_predict
rightCount=0
for i in range(len(zeroLable)):
if list(zeroLable)[i]==0:
rightCount+=1
print ('the right rate is:',float(rightCount)/len(zeroLable))
result=clf.predict(data_pca[len(train_data):])
with open("C:\\Users\\312\\Desktop\\digit-recognizer\\result.csv", 'w') as fw:
with open('C:\\Users\\312\\Desktop\\digit-recognizer\\sample_submission.csv') as pred_file:
fw.write('{},{}\n'.format('ImageId', 'Label'))
for i,line in enumerate(pred_file.readlines()[1:]):
splits = line.strip().split(',')
fw.write('{},{}\n'.format(splits[0],result[i]))
```
> 结果:
```
the right rate is: 0.9533333333333334
```
================================================
FILE: docs/Kaggle/competitions/getting-started/digit-recognizer/随机森林算法描述.md
================================================
# 树模型
本节选用随机森林树模型进行演示，也可尝试sklearn和GBDT模型，以及xgboost模型,lightgbm模型 树模型的基础知识请参照周志华的《机器学习》以及李航的《统计学习方法》，另外代码可以参照机器学习实战 树模型最核心的就是信息熵，基尼指数等叶子结点得分，这是树模型分裂的依据，请着重研究~ 想偷懒的同学，社区的连接在此，看看吧
[7.集成方法-随机森林和AdaBoost](https://github.com/apachecn/AiLearning/blob/master/docs/ml/7.%E9%9B%86%E6%88%90%E6%96%B9%E6%B3%95-%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97%E5%92%8CAdaBoost.md)
顺便说一下，嫌慢的同学还是用lightgbm吧，这个超快的~ GridSearchCV是用来调参的，只需要把注释去掉就可以显示出最佳的参数，不过实测慢的要死。。
> 代码：
```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import pandas as pd
import numpy as np
# from sklearn.grid_search import GridSearchCV
# from numpy import arange
# from lightgbm import LGBMClassifier
train_data=pd.read_csv(r"C:\Users\312\Desktop\digit-recognizer\train.csv")
test_data=pd.read_csv(r"C:\Users\312\Desktop\digit-recognizer\test.csv")
data=pd.concat([train_data,test_data],axis=0).reset_index(drop=True)
data.drop(['label'],axis=1,inplace=True)
label=train_data.label
pca=PCA(n_components=100, random_state=34)
data_pca=pca.fit_transform(data)
Xtrain,Ytrain,xtest,ytest=train_test_split(data_pca[0:len(train_data)],label,test_size=0.1, random_state=34)
clf=RandomForestClassifier(n_estimators=110,max_depth=5,min_samples_split=2, min_samples_leaf=1,random_state=34)
# clf=LGBMClassifier(num_leaves=63, max_depth=7, n_estimators=80, n_jobs=20)
# param_test1 = {'n_estimators':arange(10,150,10),'max_depth':arange(1,11,1)}
# gsearch1 = GridSearchCV(estimator = clf, param_grid = param_test1, scoring='accuracy',iid=False,cv=5)
# gsearch1.fit(Xtrain,xtest)
# print(gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_)
clf.fit(Xtrain,xtest)
y_predict=clf.predict(Ytrain)
zeroLable=ytest-y_predict
rightCount=0
for i in range(len(zeroLable)):
if list(zeroLable)[i]==0:
rightCount+=1
print ('the right rate is:',float(rightCount)/len(zeroLable))
result=clf.predict(data_pca[len(train_data):])
with open("C:\\Users\\312\\Desktop\\digit-recognizer\\result.csv", 'w') as fw:
with open('C:\\Users\\312\\Desktop\\digit-recognizer\\sample_submission.csv') as pred_file:
fw.write('{},{}\n'.format('ImageId', 'Label'))
for i,line in enumerate(pred_file.readlines()[1:]):
splits = line.strip().split(',')
fw.write('{},{}\n'.format(splits[0],result[i]))
```
> 结果:
```
the right rate is: 0.819047619047619
```
================================================
FILE: docs/Kaggle/competitions/getting-started/house-price/README.md
================================================
# **房价预测**
## 比赛说明
* [**房价预测**](https://www.kaggle.com/c/house-prices-advanced-regression-techniques)
* 要求购房者描述他们的梦想之家，他们可能不会从地下室天花板的高度或与东西方铁路的接近度开始。但是这个游乐场比赛的数据集证明，对价格谈判的影响远远超过卧室或白色栅栏的数量。
* 有79个解释变量描述（几乎）爱荷华州埃姆斯的住宅房屋的每个方面，这个竞赛挑战你预测每个房屋的最终价格。
## 参赛成员
* 开源组织: [ApacheCN ~ apachecn.org](http://www.apachecn.org/)
## 比赛分析
* 回归问题：价格的问题
* 常用算法： `回归`、`树回归`、`GBDT`、`xgboost`、`lightGBM`
```
步骤:
一. 数据分析
1. 下载并加载数据
2. 总体预览:了解每列数据的含义,数据的格式等
3. 数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备
二. 特征工程
1.根据业务,常识,以及第二步的数据分析构造特征工程.
2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)
三. 模型选择
1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.
2.比较各个模型的分数,然后取效果较好的模型作为基础模型.
四. 模型融合
1. 可以参考泰坦尼克号的简单模型融合方式，通过对模型的对比打分方式选择合适的模型
2. 在房价预测里我们使用模型融合的方法来输出结果，最终的效果很好。
五. 修改特征和模型参数
1.可以通过添加或者修改特征,提高模型的上限.
2.通过修改模型的参数,是模型逼近上限
```
## 一. 数据分析
### 数据下载和加载
* 数据集下载地址：
```python
# 导入相关数据包
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from scipy import stats
from scipy.stats import norm
```
```python
root_path = '/opt/data/kaggle/getting-started/house-prices'
train = pd.read_csv('%s/%s' % (root_path, 'train.csv'))
test = pd.read_csv('%s/%s' % (root_path, 'test.csv'))
```
### 特征说明
```python
train.columns
```
Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',
'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',
'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',
'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',
'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',
'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',
'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',
'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',
'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',
'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',
'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',
'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',
'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',
'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',
'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',
'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',
'SaleCondition', 'SalePrice'],
dtype='object')
```python
train.info()
```
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 81 columns):
Id 1460 non-null int64
MSSubClass 1460 non-null int64
MSZoning 1460 non-null object
LotFrontage 1201 non-null float64
LotArea 1460 non-null int64
Street 1460 non-null object
Alley 91 non-null object
LotShape 1460 non-null object
LandContour 1460 non-null object
Utilities 1460 non-null object
LotConfig 1460 non-null object
LandSlope 1460 non-null object
Neighborhood 1460 non-null object
Condition1 1460 non-null object
Condition2 1460 non-null object
BldgType 1460 non-null object
HouseStyle 1460 non-null object
OverallQual 1460 non-null int64
OverallCond 1460 non-null int64
YearBuilt 1460 non-null int64
YearRemodAdd 1460 non-null int64
RoofStyle 1460 non-null object
RoofMatl 1460 non-null object
Exterior1st 1460 non-null object
Exterior2nd 1460 non-null object
MasVnrType 1452 non-null object
MasVnrArea 1452 non-null float64
ExterQual 1460 non-null object
ExterCond 1460 non-null object
Foundation 1460 non-null object
BsmtQual 1423 non-null object
BsmtCond 1423 non-null object
BsmtExposure 1422 non-null object
BsmtFinType1 1423 non-null object
BsmtFinSF1 1460 non-null int64
BsmtFinType2 1422 non-null object
BsmtFinSF2 1460 non-null int64
BsmtUnfSF 1460 non-null int64
TotalBsmtSF 1460 non-null int64
Heating 1460 non-null object
HeatingQC 1460 non-null object
CentralAir 1460 non-null object
Electrical 1459 non-null object
1stFlrSF 1460 non-null int64
2ndFlrSF 1460 non-null int64
LowQualFinSF 1460 non-null int64
GrLivArea 1460 non-null int64
BsmtFullBath 1460 non-null int64
BsmtHalfBath 1460 non-null int64
FullBath 1460 non-null int64
HalfBath 1460 non-null int64
BedroomAbvGr 1460 non-null int64
KitchenAbvGr 1460 non-null int64
KitchenQual 1460 non-null object
TotRmsAbvGrd 1460 non-null int64
Functional 1460 non-null object
Fireplaces 1460 non-null int64
FireplaceQu 770 non-null object
GarageType 1379 non-null object
GarageYrBlt 1379 non-null float64
GarageFinish 1379 non-null object
GarageCars 1460 non-null int64
GarageArea 1460 non-null int64
GarageQual 1379 non-null object
GarageCond 1379 non-null object
PavedDrive 1460 non-null object
WoodDeckSF 1460 non-null int64
OpenPorchSF 1460 non-null int64
EnclosedPorch 1460 non-null int64
3SsnPorch 1460 non-null int64
ScreenPorch 1460 non-null int64
PoolArea 1460 non-null int64
PoolQC 7 non-null object
Fence 281 non-null object
MiscFeature 54 non-null object
MiscVal 1460 non-null int64
MoSold 1460 non-null int64
YrSold 1460 non-null int64
SaleType 1460 non-null object
SaleCondition 1460 non-null object
SalePrice 1460 non-null int64
dtypes: float64(3), int64(35), object(43)
memory usage: 924.0+ KB
### 特征详情
```python
train.head(5)
```
Id
MSSubClass
MSZoning
LotFrontage
LotArea
Street
Alley
LotShape
LandContour
Utilities
...
PoolArea
PoolQC
Fence
MiscFeature
MiscVal
MoSold
YrSold
SaleType
SaleCondition
SalePrice
0
1
60
RL
65.0
8450
Pave
NaN
Reg
Lvl
AllPub
...
0
NaN
NaN
NaN
0
2
2008
WD
Normal
208500
1
2
20
RL
80.0
9600
Pave
NaN
Reg
Lvl
AllPub
...
0
NaN
NaN
NaN
0
5
2007
WD
Normal
181500
2
3
60
RL
68.0
11250
Pave
NaN
IR1
Lvl
AllPub
...
0
NaN
NaN
NaN
0
9
2008
WD
Normal
223500
3
4
70
RL
60.0
9550
Pave
NaN
IR1
Lvl
AllPub
...
0
NaN
NaN
NaN
0
2
2006
WD
Abnorml
140000
4
5
60
RL
84.0
14260
Pave
NaN
IR1
Lvl
AllPub
...
0
NaN
NaN
NaN
0
12
2008
WD
Normal
250000
5 rows × 81 columns
### 特征分析（统计学与绘图）
每一行是一条房子出售的记录，原始特征有80列，具体的意思可以根据data_description来查询，我们要预测的是房子的售价，即“SalePrice”。训练集有1459条记录，测试集有1460条记录，数据量还是很小的。
```python
# 相关性协方差表,corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关.
train_corr = train.drop('Id',axis=1).corr()
train_corr
```
MSSubClass
LotFrontage
LotArea
OverallQual
OverallCond
YearBuilt
YearRemodAdd
MasVnrArea
BsmtFinSF1
BsmtFinSF2
...
WoodDeckSF
OpenPorchSF
EnclosedPorch
3SsnPorch
ScreenPorch
PoolArea
MiscVal
MoSold
YrSold
SalePrice
MSSubClass
1.000000
-0.386347
-0.139781
0.032628
-0.059316
0.027850
0.040581
0.022936
-0.069836
-0.065649
...
-0.012579
-0.006100
-0.012037
-0.043825
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LotFrontage
-0.386347
1.000000
0.426095
0.251646
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0.123349
0.088866
0.193458
0.233633
0.049900
...
0.088521
0.151972
0.010700
0.070029
0.041383
0.206167
0.003368
0.011200
0.007450
0.351799
LotArea
-0.139781
0.426095
1.000000
0.105806
-0.005636
0.014228
0.013788
0.104160
0.214103
0.111170
...
0.171698
0.084774
-0.018340
0.020423
0.043160
0.077672
0.038068
0.001205
-0.014261
0.263843
OverallQual
0.032628
0.251646
0.105806
1.000000
-0.091932
0.572323
0.550684
0.411876
0.239666
-0.059119
...
0.238923
0.308819
-0.113937
0.030371
0.064886
0.065166
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0.070815
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0.790982
OverallCond
-0.059316
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1.000000
-0.375983
0.073741
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0.040229
...
-0.003334
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0.070356
0.025504
0.054811
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0.068777
-0.003511
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YearBuilt
0.027850
0.123349
0.014228
0.572323
-0.375983
1.000000
0.592855
0.315707
0.249503
-0.049107
...
0.224880
0.188686
-0.387268
0.031355
-0.050364
0.004950
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0.012398
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0.522897
YearRemodAdd
0.040581
0.088866
0.013788
0.550684
0.073741
0.592855
1.000000
0.179618
0.128451
-0.067759
...
0.205726
0.226298
-0.193919
0.045286
-0.038740
0.005829
-0.010286
0.021490
0.035743
0.507101
MasVnrArea
0.022936
0.193458
0.104160
0.411876
-0.128101
0.315707
0.179618
1.000000
0.264736
-0.072319
...
0.159718
0.125703
-0.110204
0.018796
0.061466
0.011723
-0.029815
-0.005965
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0.477493
BsmtFinSF1
-0.069836
0.233633
0.214103
0.239666
-0.046231
0.249503
0.128451
0.264736
1.000000
-0.050117
...
0.204306
0.111761
-0.102303
0.026451
0.062021
0.140491
0.003571
-0.015727
0.014359
0.386420
BsmtFinSF2
-0.065649
0.049900
0.111170
-0.059119
0.040229
-0.049107
-0.067759
-0.072319
-0.050117
1.000000
...
0.067898
0.003093
0.036543
-0.029993
0.088871
0.041709
0.004940
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0.031706
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BsmtUnfSF
-0.140759
0.132644
-0.002618
0.308159
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0.149040
0.181133
0.114442
-0.495251
-0.209294
...
-0.005316
0.129005
-0.002538
0.020764
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0.034888
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0.214479
TotalBsmtSF
-0.238518
0.392075
0.260833
0.537808
-0.171098
0.391452
0.291066
0.363936
0.522396
0.104810
...
0.232019
0.247264
-0.095478
0.037384
0.084489
0.126053
-0.018479
0.013196
-0.014969
0.613581
1stFlrSF
-0.251758
0.457181
0.299475
0.476224
-0.144203
0.281986
0.240379
0.344501
0.445863
0.097117
...
0.235459
0.211671
-0.065292
0.056104
0.088758
0.131525
-0.021096
0.031372
-0.013604
0.605852
2ndFlrSF
0.307886
0.080177
0.050986
0.295493
0.028942
0.010308
0.140024
0.174561
-0.137079
-0.099260
...
0.092165
0.208026
0.061989
-0.024358
0.040606
0.081487
0.016197
0.035164
-0.028700
0.319334
LowQualFinSF
0.046474
0.038469
0.004779
-0.030429
0.025494
-0.183784
-0.062419
-0.069071
-0.064503
0.014807
...
-0.025444
0.018251
0.061081
-0.004296
0.026799
0.062157
-0.003793
-0.022174
-0.028921
-0.025606
GrLivArea
0.074853
0.402797
0.263116
0.593007
-0.079686
0.199010
0.287389
0.390857
0.208171
-0.009640
...
0.247433
0.330224
0.009113
0.020643
0.101510
0.170205
-0.002416
0.050240
-0.036526
0.708624
BsmtFullBath
0.003491
0.100949
0.158155
0.111098
-0.054942
0.187599
0.119470
0.085310
0.649212
0.158678
...
0.175315
0.067341
-0.049911
-0.000106
0.023148
0.067616
-0.023047
-0.025361
0.067049
0.227122
BsmtHalfBath
-0.002333
-0.007234
0.048046
-0.040150
0.117821
-0.038162
-0.012337
0.026673
0.067418
0.070948
...
0.040161
-0.025324
-0.008555
0.035114
0.032121
0.020025
-0.007367
0.032873
-0.046524
-0.016844
FullBath
0.131608
0.198769
0.126031
0.550600
-0.194149
0.468271
0.439046
0.276833
0.058543
-0.076444
...
0.187703
0.259977
-0.115093
0.035353
-0.008106
0.049604
-0.014290
0.055872
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0.560664
HalfBath
0.177354
0.053532
0.014259
0.273458
-0.060769
0.242656
0.183331
0.201444
0.004262
-0.032148
...
0.108080
0.199740
-0.095317
-0.004972
0.072426
0.022381
0.001290
-0.009050
-0.010269
0.284108
BedroomAbvGr
-0.023438
0.263170
0.119690
0.101676
0.012980
-0.070651
-0.040581
0.102821
-0.107355
-0.015728
...
0.046854
0.093810
0.041570
-0.024478
0.044300
0.070703
0.007767
0.046544
-0.036014
0.168213
KitchenAbvGr
0.281721
-0.006069
-0.017784
-0.183882
-0.087001
-0.174800
-0.149598
-0.037610
-0.081007
-0.040751
...
-0.090130
-0.070091
0.037312
-0.024600
-0.051613
-0.014525
0.062341
0.026589
0.031687
-0.135907
TotRmsAbvGrd
0.040380
0.352096
0.190015
0.427452
-0.057583
0.095589
0.191740
0.280682
0.044316
-0.035227
...
0.165984
0.234192
0.004151
-0.006683
0.059383
0.083757
0.024763
0.036907
-0.034516
0.533723
Fireplaces
-0.045569
0.266639
0.271364
0.396765
-0.023820
0.147716
0.112581
0.249070
0.260011
0.046921
...
0.200019
0.169405
-0.024822
0.011257
0.184530
0.095074
0.001409
0.046357
-0.024096
0.466929
GarageYrBlt
0.085072
0.070250
-0.024947
0.547766
-0.324297
0.825667
0.642277
0.252691
0.153484
-0.088011
...
0.224577
0.228425
-0.297003
0.023544
-0.075418
-0.014501
-0.032417
0.005337
-0.001014
0.486362
GarageCars
-0.040110
0.285691
0.154871
0.600671
-0.185758
0.537850
0.420622
0.364204
0.224054
-0.038264
...
0.226342
0.213569
-0.151434
0.035765
0.050494
0.020934
-0.043080
0.040522
-0.039117
0.640409
GarageArea
-0.098672
0.344997
0.180403
0.562022
-0.151521
0.478954
0.371600
0.373066
0.296970
-0.018227
...
0.224666
0.241435
-0.121777
0.035087
0.051412
0.061047
-0.027400
0.027974
-0.027378
0.623431
WoodDeckSF
-0.012579
0.088521
0.171698
0.238923
-0.003334
0.224880
0.205726
0.159718
0.204306
0.067898
...
1.000000
0.058661
-0.125989
-0.032771
-0.074181
0.073378
-0.009551
0.021011
0.022270
0.324413
OpenPorchSF
-0.006100
0.151972
0.084774
0.308819
-0.032589
0.188686
0.226298
0.125703
0.111761
0.003093
...
0.058661
1.000000
-0.093079
-0.005842
0.074304
0.060762
-0.018584
0.071255
-0.057619
0.315856
EnclosedPorch
-0.012037
0.010700
-0.018340
-0.113937
0.070356
-0.387268
-0.193919
-0.110204
-0.102303
0.036543
...
-0.125989
-0.093079
1.000000
-0.037305
-0.082864
0.054203
0.018361
-0.028887
-0.009916
-0.128578
3SsnPorch
-0.043825
0.070029
0.020423
0.030371
0.025504
0.031355
0.045286
0.018796
0.026451
-0.029993
...
-0.032771
-0.005842
-0.037305
1.000000
-0.031436
-0.007992
0.000354
0.029474
0.018645
0.044584
ScreenPorch
-0.026030
0.041383
0.043160
0.064886
0.054811
-0.050364
-0.038740
0.061466
0.062021
0.088871
...
-0.074181
0.074304
-0.082864
-0.031436
1.000000
0.051307
0.031946
0.023217
0.010694
0.111447
PoolArea
0.008283
0.206167
0.077672
0.065166
-0.001985
0.004950
0.005829
0.011723
0.140491
0.041709
...
0.073378
0.060762
0.054203
-0.007992
0.051307
1.000000
0.029669
-0.033737
-0.059689
0.092404
MiscVal
-0.007683
0.003368
0.038068
-0.031406
0.068777
-0.034383
-0.010286
-0.029815
0.003571
0.004940
...
-0.009551
-0.018584
0.018361
0.000354
0.031946
0.029669
1.000000
-0.006495
0.004906
-0.021190
MoSold
-0.013585
0.011200
0.001205
0.070815
-0.003511
0.012398
0.021490
-0.005965
-0.015727
-0.015211
...
0.021011
0.071255
-0.028887
0.029474
0.023217
-0.033737
-0.006495
1.000000
-0.145721
0.046432
YrSold
-0.021407
0.007450
-0.014261
-0.027347
0.043950
-0.013618
0.035743
-0.008201
0.014359
0.031706
...
0.022270
-0.057619
-0.009916
0.018645
0.010694
-0.059689
0.004906
-0.145721
1.000000
-0.028923
SalePrice
-0.084284
0.351799
0.263843
0.790982
-0.077856
0.522897
0.507101
0.477493
0.386420
-0.011378
...
0.324413
0.315856
-0.128578
0.044584
0.111447
0.092404
-0.021190
0.046432
-0.028923
1.000000
37 rows × 37 columns
> 所有特征相关度分析
```python
# 画出相关性热力图
a = plt.subplots(figsize=(20, 12))#调整画布大小
a = sns.heatmap(train_corr, vmax=.8, square=True)#画热力图 annot=True 显示系数
```
> SalePrice 相关度特征排序
```python
# 寻找K个最相关的特征信息
k = 10 # number of variables for heatmap
cols = train_corr.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(train[cols].values.T)
sns.set(font_scale=1.5)
hm = plt.subplots(figsize=(20, 12))#调整画布大小
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
'''
1. GarageCars 和 GarageAre 相关性很高、就像双胞胎一样，所以我们只需要其中的一个变量，例如：GarageCars。
2. TotalBsmtSF 和 1stFloor 与上述情况相同，我们选择 TotalBsmtS
3. GarageAre 和 TotRmsAbvGrd 与上述情况相同，我们选择 GarageAre
'''
```
'\n1. GarageCars 和 GarageAre 相关性很高、就像双胞胎一样，所以我们只需要其中的一个变量，例如：GarageCars。\n2. TotalBsmtSF 和 1stFloor 与上述情况相同，我们选择 TotalBsmtS\n3. GarageAre 和 TotRmsAbvGrd 与上述情况相同，我们选择 GarageAre\n'
> SalePrice 和相关变量之间的散点图
```python
sns.set()
cols = ['SalePrice', 'OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']
sns.pairplot(train[cols], size = 2.5)
plt.show();
```
```python
train[['SalePrice', 'OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']].info()
```
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 7 columns):
SalePrice 1460 non-null int64
OverallQual 1460 non-null int64
GrLivArea 1460 non-null int64
GarageCars 1460 non-null int64
TotalBsmtSF 1460 non-null int64
FullBath 1460 non-null int64
YearBuilt 1460 non-null int64
dtypes: int64(7)
memory usage: 79.9 KB
## 二. 特征工程
```
test['SalePrice'] = None
train_test = pd.concat((train, test)).reset_index(drop=True)
```
### 1. 缺失值分析
2. 根据业务,常识,以及第二步的数据分析构造特征工程.
2. 将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)
```python
total= train_test.isnull().sum().sort_values(ascending=False)
percent = (train_test.isnull().sum()/train_test.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total','Lost Percent'])
print(missing_data[missing_data.isnull().values==False].sort_values('Total', axis=0, ascending=False).head(20))
'''
1. 对于缺失率过高的特征，例如 超过15% 我们应该删掉相关变量且假设该变量并不存在
2. GarageX 变量群的缺失数据量和概率都相同，可以选择一个就行，例如：GarageCars
3. 对于缺失数据在5%左右（缺失率低），可以直接删除/回归预测
'''
```
'\n1. 对于缺失率过高的特征，例如 超过15% 我们应该删掉相关变量且假设该变量并不存在\n2. GarageX 变量群的缺失数据量和概率都相同，可以选择一个就行，例如：GarageCars\n3. 对于缺失数据在5%左右（缺失率低），可以直接删除/回归预测\n'
```python
train_test = train_test.drop((missing_data[missing_data['Total'] > 1]).index.drop('SalePrice') , axis=1)
# train_test = train_test.drop(train.loc[train['Electrical'].isnull()].index)
tmp = train_test[train_test['SalePrice'].isnull().values==False]
print(tmp.isnull().sum().max()) # justchecking that there's no missing data missing
```
1
### 2. 异常值处理
#### 单因素分析
这里的关键在于如何建立阈值，定义一个观察值为异常值。我们对数据进行正态化，意味着把数据值转换成均值为 0，方差为 1 的数据
```python
fig = plt.figure(figsize=(12, 6))
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
ax1.hist(train.SalePrice)
ax2.hist(np.log1p(train.SalePrice))
'''
从直方图中可以看出：
* 偏离正态分布
* 数据正偏
* 有峰值
'''
# 数据偏度和峰度度量：
print("Skewness: %f" % train['SalePrice'].skew())
print("Kurtosis: %f" % train['SalePrice'].kurt())
'''
低范围的值都比较相似并且在 0 附近分布。
高范围的值离 0 很远，并且七点几的值远在正常范围之外。
'''
```
'\n低范围的值都比较相似并且在 0 附近分布。\n高范围的值离 0 很远，并且七点几的值远在正常范围之外。\n'
#### 双变量分析
> 1.GrLivArea 和 SalePrice 双变量分析
```python
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice', ylim=(0,800000));
'''
从图中可以看出：
1. 有两个离群的 GrLivArea 值很高的数据，我们可以推测出现这种情况的原因。
或许他们代表了农业地区，也就解释了低价。 这两个点很明显不能代表典型样例，所以我们将它们定义为异常值并删除。
2. 图中顶部的两个点是七点几的观测值，他们虽然看起来像特殊情况，但是他们依然符合整体趋势，所以我们将其保留下来。
'''
```
'\n从图中可以看出：\n\n1. 有两个离群的 GrLivArea 值很高的数据，我们可以推测出现这种情况的原因。\n 或许他们代表了农业地区，也就解释了低价。 这两个点很明显不能代表典型样例，所以我们将它们定义为异常值并删除。\n2. 图中顶部的两个点是七点几的观测值，他们虽然看起来像特殊情况，但是他们依然符合整体趋势，所以我们将其保留下来。\n'
```python
# 删除点
print(train.sort_values(by='GrLivArea', ascending = False)[:2])
tmp = train_test[train_test['SalePrice'].isnull().values==False]
train_test = train_test.drop(tmp[tmp['Id'] == 1299].index)
train_test = train_test.drop(tmp[tmp['Id'] == 524].index)
```
> 2.TotalBsmtSF 和 SalePrice 双变量分析
```python
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'],train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice',ylim=(0,800000))
```
### 核心部分
“房价” 到底是谁？
这个问题的答案，需要我们验证根据数据基础进行多元分析的假设。
我们已经进行了数据清洗，并且发现了 SalePrice 的很多信息，现在我们要更进一步理解 SalePrice 如何遵循统计假设，可以让我们应用多元技术。
应该测量 4 个假设量：
* 正态性
* 同方差性
* 线性
* 相关错误缺失
#### 正态性：
应主要关注以下两点：直方图 – 峰度和偏度。
正态概率图 – 数据分布应紧密跟随代表正态分布的对角线。
1. SalePrice 绘制直方图和正态概率图：
```python
sns.distplot(train['SalePrice'], fit=norm)
fig = plt.figure()
res = stats.probplot(train['SalePrice'], plot=plt)
'''
可以看出，房价分布不是正态的，显示了峰值，正偏度，但是并不跟随对角线。
可以用对数变换来解决这个问题
'''
```
'\n可以看出，房价分布不是正态的，显示了峰值，正偏度，但是并不跟随对角线。\n可以用对数变换来解决这个问题\n'
```python
# 进行对数变换：
# 进行对数变换：
train_test['SalePrice'] = [i if i is None else np.log1p(i) for i in train_test['SalePrice']]
```
```python
# 绘制变换后的直方图和正态概率图：
tmp = train_test[train_test['SalePrice'].isnull().values==False]
sns.distplot(tmp[tmp['SalePrice'] !=0]['SalePrice'], fit=norm);
fig = plt.figure()
res = stats.probplot(tmp['SalePrice'], plot=plt)
```
#### 2. GrLivArea
绘制直方图和正态概率曲线图：
```python
sns.distplot(train['GrLivArea'], fit=norm);
fig = plt.figure()
res = stats.probplot(train['GrLivArea'], plot=plt)
```
```python
# 进行对数变换：
train_test['GrLivArea'] = [i if i is None else np.log1p(i) for i in train_test['GrLivArea']]
# 绘制变换后的直方图和正态概率图：
tmp = train_test[train_test['SalePrice'].isnull().values==False]
sns.distplot(tmp['GrLivArea'], fit=norm)
fig = plt.figure()
res = stats.probplot(tmp['GrLivArea'], plot=plt)
```
#### 3.TotalBsmtSF
绘制直方图和正态概率曲线图：
```python
sns.distplot(train['TotalBsmtSF'],fit=norm);
fig = plt.figure()
res = stats.probplot(train['TotalBsmtSF'],plot=plt)
'''
从图中可以看出：
* 显示出了偏度
* 大量为 0(Y值) 的观察值（没有地下室的房屋）
* 含 0(Y值) 的数据无法进行对数变换
'''
```
'\n从图中可以看出：\n* 显示出了偏度\n* 大量为 0(Y值) 的观察值（没有地下室的房屋）\n* 含 0(Y值) 的数据无法进行对数变换\n'
```python
# 去掉为0的分布情况
tmp = train_test[train_test['SalePrice'].isnull().values==False]
tmp = np.array(tmp.loc[tmp['TotalBsmtSF']>0, ['TotalBsmtSF']])[:, 0]
sns.distplot(tmp, fit=norm)
fig = plt.figure()
res = stats.probplot(tmp, plot=plt)
```
```python
# 我们建立了一个变量，可以得到有没有地下室的影响值（二值变量），我们选择忽略零值，只对非零值进行对数变换。
# 这样我们既可以变换数据，也不会损失有没有地下室的影响。
print(train.loc[train['TotalBsmtSF']==0, ['TotalBsmtSF']].count())
train.loc[train['TotalBsmtSF']==0,'TotalBsmtSF'] = 1
print(train.loc[train['TotalBsmtSF']==1, ['TotalBsmtSF']].count())
```
TotalBsmtSF 37
dtype: int64
TotalBsmtSF 37
dtype: int64
```python
# 进行对数变换：
tmp = train_test[train_test['SalePrice'].isnull().values==False]
print(tmp['TotalBsmtSF'].head(10))
train_test['TotalBsmtSF']= np.log1p(train_test['TotalBsmtSF'])
tmp = train_test[train_test['SalePrice'].isnull().values==False]
print(tmp['TotalBsmtSF'].head(10))
```
0 856.0
1 1262.0
2 920.0
3 756.0
4 1145.0
5 796.0
6 1686.0
7 1107.0
8 952.0
9 991.0
Name: TotalBsmtSF, dtype: float64
0 6.753438
1 7.141245
2 6.825460
3 6.629363
4 7.044033
5 6.680855
6 7.430707
7 7.010312
8 6.859615
9 6.899723
Name: TotalBsmtSF, dtype: float64
```python
# 绘制变换后的直方图和正态概率图：
tmp = train_test[train_test['SalePrice'].isnull().values==False]
tmp = np.array(tmp.loc[tmp['TotalBsmtSF']>0, ['TotalBsmtSF']])[:, 0]
sns.distplot(tmp, fit=norm)
fig = plt.figure()
res = stats.probplot(tmp, plot=plt)
```
#### 同方差性：
最好的测量两个变量的同方差性的方法就是图像。
1. SalePrice 和 GrLivArea 同方差性
绘制散点图：
```python
tmp = train_test[train_test['SalePrice'].isnull().values==False]
plt.scatter(tmp['GrLivArea'], tmp['SalePrice'])
```
2. SalePrice with TotalBsmtSF 同方差性
绘制散点图：
```python
tmp = train_test[train_test['SalePrice'].isnull().values==False]
plt.scatter(tmp[tmp['TotalBsmtSF']>0]['TotalBsmtSF'], tmp[tmp['TotalBsmtSF']>0]['SalePrice'])
# 可以看出 SalePrice 在整个 TotalBsmtSF 变量范围内显示出了同等级别的变化。
```
## 三. 模型选择
### 1.数据标准化
```python
tmp = train_test[train_test['SalePrice'].isnull().values==False]
tmp_1 = train_test[train_test['SalePrice'].isnull().values==True]
x_train = tmp[['OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']]
y_train = tmp[["SalePrice"]].values.ravel()
x_test = tmp_1[['OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']]
# 简单测试，用中位数来替代
# print(x_test.GarageCars.mean(), x_test.GarageCars.median(), x_test.TotalBsmtSF.mean(), x_test.TotalBsmtSF.median())
x_test["GarageCars"].fillna(x_test.GarageCars.median(), inplace=True)
x_test["TotalBsmtSF"].fillna(x_test.TotalBsmtSF.median(), inplace=True)
```
### 2.开始建模
1. 可选单个模型模型有 线性回归（Ridge、Lasso）、树回归、GBDT、XGBoost、LightGBM 等.
2. 也可以将多个模型组合起来,进行模型融合,比如voting,stacking等方法
3. 好的特征决定模型上限,好的模型和参数可以无线逼近上限.
4. 我测试了多种模型,模型结果最高的随机森林,最高有0.8.
#### bagging:
单个分类器的效果真的是很有限。
我们会倾向于把N多的分类器合在一起，做一个“综合分类器”以达到最好的效果。
我们从刚刚的试验中得知，Ridge(alpha=15)给了我们最好的结果。
```python
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import BaggingRegressor, RandomForestRegressor
ridge = Ridge(alpha=0.1)
# bagging 把很多小的分类器放在一起，每个train随机的一部分数据，然后把它们的最终结果综合起来（多数投票）
# bagging 算是一种算法框架
params = [1, 10, 20, 40, 60]
test_scores = []
for param in params:
clf = BaggingRegressor(base_estimator=ridge, n_estimators=param)
# cv=5表示cross_val_score采用的是k-fold cross validation的方法，重复5次交叉验证
# scoring='precision'、scoring='recall'、scoring='f1', scoring='neg_mean_squared_error' 方差值
test_score = np.sqrt(-cross_val_score(clf, x_train, y_train, cv=10, scoring='neg_mean_squared_error'))
test_scores.append(np.mean(test_score))
print(test_score.mean())
plt.plot(params, test_scores)
plt.title('n_estimators vs CV Error')
plt.show()
```
```python
# 模型选择
## LASSO Regression :
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=1))
* Elastic Net Regression
ENet = make_pipeline(
RobustScaler(), ElasticNet(
alpha=0.0005, l1_ratio=.9, random_state=3))
Kernel Ridge Regression
KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)
## Gradient Boosting Regression
GBoost = GradientBoostingRegressor(
n_estimators=3000,
learning_rate=0.05,
max_depth=4,
max_features='sqrt',
min_samples_leaf=15,
min_samples_split=10,
loss='huber',
random_state=5)
## XGboost
model_xgb = xgb.XGBRegressor(
colsample_bytree=0.4603,
gamma=0.0468,
learning_rate=0.05,
max_depth=3,
min_child_weight=1.7817,
n_estimators=2200,
reg_alpha=0.4640,
reg_lambda=0.8571,
subsample=0.5213,
silent=1,
random_state=7,
nthread=-1)
## lightGBM
model_lgb = lgb.LGBMRegressor(
objective='regression',
num_leaves=5,
learning_rate=0.05,
n_estimators=720,
max_bin=55,
bagging_fraction=0.8,
bagging_freq=5,
feature_fraction=0.2319,
feature_fraction_seed=9,
bagging_seed=9,
min_data_in_leaf=6,
min_sum_hessian_in_leaf=11)
## 对这些基本模型进行打分
score = rmsle_cv(lasso)
print("\nLasso score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(ENet)
print("ElasticNet score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(KRR)
print(
"Kernel Ridge score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(GBoost)
print("Gradient Boosting score: {:.4f} ({:.4f})\n".format(score.mean(),
score.std()))
score = rmsle_cv(model_xgb)
print("Xgboost score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(model_lgb)
print("LGBM score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
```
```python
from sklearn.linear_model import Ridge
from sklearn.model_selection import learning_curve
ridge = Ridge(alpha=0.1)
train_sizes, train_loss, test_loss = learning_curve(ridge, x_train, y_train, cv=10,
scoring='neg_mean_squared_error',
train_sizes = [0.1, 0.3, 0.5, 0.7, 0.9 , 0.95, 1])
# 训练误差均值
train_loss_mean = -np.mean(train_loss, axis = 1)
# 测试误差均值
test_loss_mean = -np.mean(test_loss, axis = 1)
# 绘制误差曲线
plt.plot(train_sizes/len(x_train), train_loss_mean, 'o-', color = 'r', label = 'Training')
plt.plot(train_sizes/len(x_train), test_loss_mean, 'o-', color = 'g', label = 'Cross-Validation')
plt.xlabel('Training data size')
plt.ylabel('Loss')
plt.legend(loc = 'best')
plt.show()
```
```python
mode_br = BaggingRegressor(base_estimator=ridge, n_estimators=10)
mode_br.fit(x_train, y_train)
y_test = np.expm1(mode_br.predict(x_test))
```
## 四 建立模型
> 模型融合 voting
```python
# 模型融合
class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):
def __init__(self, models):
self.models = models
# we define clones of the original models to fit the data in
def fit(self, X, y):
self.models_ = [clone(x) for x in self.models]
# Train cloned base models
for model in self.models_:
model.fit(X, y)
return self
# Now we do the predictions for cloned models and average them
def predict(self, X):
predictions = np.column_stack(
[model.predict(X) for model in self.models_])
return np.mean(predictions, axis=1)
# 评价这四个模型的好坏
averaged_models = AveragingModels(models=(ENet, GBoost, KRR, lasso))
score = rmsle_cv(averaged_models)
print(" Averaged base models score: {:.4f} ({:.4f})\n".format(score.mean(),
score.std()))
# 最终对模型的训练和预测
# StackedRegressor
stacked_averaged_models.fit(train.values, y_train)
stacked_train_pred = stacked_averaged_models.predict(train.values)
stacked_pred = np.expm1(stacked_averaged_models.predict(test.values))
print(rmsle(y_train, stacked_train_pred))
# XGBoost
model_xgb.fit(train, y_train)
xgb_train_pred = model_xgb.predict(train)
xgb_pred = np.expm1(model_xgb.predict(test))
print(rmsle(y_train, xgb_train_pred))
# lightGBM
model_lgb.fit(train, y_train)
lgb_train_pred = model_lgb.predict(train)
lgb_pred = np.expm1(model_lgb.predict(test.values))
print(rmsle(y_train, lgb_train_pred))
'''RMSE on the entire Train data when averaging'''
print('RMSLE score on train data:')
print(rmsle(y_train, stacked_train_pred * 0.70 + xgb_train_pred * 0.15 +
lgb_train_pred * 0.15))
# 模型融合的预测效果
ensemble = stacked_pred * 0.70 + xgb_pred * 0.15 + lgb_pred * 0.15
# 保存结果
result = pd.DataFrame()
result['Id'] = test_ID
result['SalePrice'] = ensemble
# index=False 是用来除去行编号
result.to_csv('/Users/liudong/Desktop/house_price/result.csv', index=False)
```
================================================
FILE: docs/Kaggle/competitions/getting-started/house-price/house-price.md
================================================
## **房价预测**
[房价预测](https://www.kaggle.com/c/house-prices-advanced-regression-techniques):Predict sales prices and practice feature engineering
### **内容说明**
- Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
- With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
### **开发流程**
>收集数据:[数据集](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)
>准备数据:
```python
#这里对数据做一些转换,原因要么是某些类别个数太少而且分布相近,要么是特征内的值之间有较为明显的优先级
mapper = {'LandSlope': {'Gtl':'Gtl', 'Mod':'unGtl', 'Sev':'unGtl'},
'LotShape': {'Reg':'Reg', 'IR1':'IR1', 'IR2':'other', 'IR3':'other'},
'RoofMatl': {'ClyTile':'other', 'CompShg':'CompShg', 'Membran':'other', 'Metal':'other',
'Roll':'other', 'Tar&Grv':'Tar&Grv', 'WdShake':'WdShake', 'WdShngl':'WdShngl'},
'Heating':{'GasA':'GasA', 'GasW':'GasW', 'Grav':'Grav', 'Floor':'other',
'OthW':'other', 'Wall':'Wall'},
'HeatingQC':{'Po':1, 'Fa':2, 'TA':3, 'Gd':4, 'Ex':5},
'KitchenQual': {'Fa':1, 'TA':2, 'Gd':3, 'Ex':4}
}
#对结果影响很小,或者与其他特征相关性较高的特征将被丢弃
to_drop = ['Id','Street','Utilities','Condition2','PoolArea','PoolQC','Fence',
'YrSold','MoSold','BsmtHalfBath','BsmtFinSF2','GarageQual','MiscVal'
,'EnclosedPorch','3SsnPorch','GarageArea','TotRmsAbvGrd','GarageYrBlt'
,'BsmtFinType2','BsmtUnfSF','GarageCond'
,'GarageFinish','FireplaceQu','BsmtCond','BsmtQual','Alley']
#特渣工程之瞎搞特征,别问我思路是什么,纯属乱拍脑袋搞出来,而且对结果貌似也仅有一点点影响
'''
data['house_remod']: 重新装修的年份与房建年份的差值
data['livingRate']: LotArea查了下是地块面积,这个特征是居住面积/地块面积*总体评价
data['lot_area']: LotFrontage是与房子相连的街道大小,现在想了下把GrLivArea换成LotArea会不会好点?
data['room_area']: 房间数/居住面积
data['fu_room']: 带有浴室的房间占总房间数的比例
data['gr_room']: 卧室与房间数的占比
'''
def create_feature(data):
#是否拥有地下室
hBsmt_index = data.index[data['TotalBsmtSF']>0]
data['HaveBsmt'] = 0;
data.loc[hBsmt_index,'HaveBsmt'] = 1
data['house_remod'] = data['YearRemodAdd']-data['YearBuilt'];
data['livingRate'] = (data['GrLivArea']/data['LotArea'])*data['OverallCond'];
data['lot_area'] = data['LotFrontage']/data['GrLivArea'];
data['room_area'] = data['TotRmsAbvGrd']/data['GrLivArea'];
data['fu_room'] = data['FullBath']/data['TotRmsAbvGrd'];
data['gr_room'] = data['BedroomAbvGr']/data['TotRmsAbvGrd'];
def processing(data):
#构造新特征
create_feature(data);
#丢弃特征
data.drop(to_drop,axis=1,inplace=True)
#填充None值,因为在特征说明中,None也是某些特征的一个值,所以对于这部分特征的缺失值以None填充
fill_none = ['MasVnrType','BsmtExposure','GarageType','MiscFeature']
for col in fill_none:
data[col].fillna('None',inplace=True);
#对其他缺失值进行填充,离散型特征填充众数,数值型特征填充中位数
na_col = data.dtypes[data.isnull().any()];
for col in na_col.index:
if na_col[col] != 'object':
med = data[col].median();
data[col].fillna(med,inplace=True);
else:
mode = data[col].mode()[0];
data[col].fillna(mode,inplace=True);
#对正态偏移的特征进行正态转换,numeric_col就是数值型特征,zero_col是含有零值的数值型特征
#因为如果对含零特征进行转换的话会有各种各种的小问题,所以干脆单独只对非零数值进行转换
numeric_col = data.skew().index;
zero_col = data.columns[data.isin([0]).any()]
for col in numeric_col:
#对于那些condition特征,例如取值是0,1,2,3...那些我不作变换,因为意义不大
if len(pd.value_counts(data[col])) <= 10 : continue;
#如果是含有零值的特征,则只对非零值变换,至于用哪种形式变换,boxcox会自动根据数据来调整
if col in zero_col:
trans_data = data[data>0][col];
before = abs(trans_data.skew());
cox,_ = boxcox(trans_data)
log_after = abs(Series(cox).skew());
if log_after < before:
data.loc[trans_data.index,col] = cox;
#如果是非零值的特征,则全部作转换
else:
before = abs(data[col].skew());
cox,_ = boxcox(data[col])
log_after = abs(Series(cox).skew());
if log_after < before:
data.loc[:,col] = cox;
#mapper值的映射转换
for col,mapp in mapper.items():
data.loc[:,col] = data[col].map(mapp);
df_train = pd.read_csv(os.path.join(data_dir, "train.csv"));
df_test = pd.read_csv(os.path.join(data_dir, "test.csv"));
test_ID = df_test['Id'];
#去除离群点
GrLivArea_outlier = set(df_train.index[(df_train['SalePrice']<200000)&(df_train['GrLivArea']>4000)]);
LotFrontage_outlier = set(df_train.index[df_train['LotFrontage']>300]);
df_train.drop(LotFrontage_outlier|GrLivArea_outlier,inplace=True)
#因为删除了几行数据,所以index的序列不再连续,需要重新reindex
df_train.reset_index(drop=True,inplace=True)
prices = np.log1p(df_train.loc[:,'SalePrice'])
df_train.drop(['SalePrice'],axis=1,inplace=True)
#这里对训练集和测试集进行合并,然后再进行特征工程
all_data = pd.concat([df_train,df_test])
all_data.reset_index(drop=True,inplace=True)
#进行特征工程
processing(all_data);
#dummy转换
dummy = pd.get_dummies(all_data,drop_first=True);
```
>模型训练：产生训练模型:
```
#试了Ridge,Lasso,ElasticNet以及GBM,发现ridge的表现比其他的都好,参数alpha=6是调参结果
ridge = Ridge(6);
ridge.fit(dummy.iloc[:prices.shape[0],:],prices);
```
>模型评估:RMSE
```
暂时没写
```
>结果预测：
```python
result = np.expm1(ridge.predict(dummy.iloc[prices.shape[0]:,:]))
```
>结果导出：
```python
pre = DataFrame(result,columns=['SalePrice'])
prediction = pd.concat([test_ID,pre],axis=1)
prediction.to_csv(os.path.join(data_dir, "submission.csv"),index=False)
```
================================================
FILE: docs/Kaggle/competitions/getting-started/house-price/房价测试文档.ipynb
================================================
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" NaN \n",
" NaN \n",
" NaN \n",
" 0 \n",
" 2 \n",
" 2008 \n",
" WD \n",
" Normal \n",
" 208500 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 20 \n",
" RL \n",
" 80.0 \n",
" 9600 \n",
" Pave \n",
" NaN \n",
" Reg \n",
" Lvl \n",
" AllPub \n",
" ... \n",
" 0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0 \n",
" 5 \n",
" 2007 \n",
" WD \n",
" Normal \n",
" 181500 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 60 \n",
" RL \n",
" 68.0 \n",
" 11250 \n",
" Pave \n",
" NaN \n",
" IR1 \n",
" Lvl \n",
" AllPub \n",
" ... \n",
" 0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0 \n",
" 9 \n",
" 2008 \n",
" WD \n",
" Normal \n",
" 223500 \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 70 \n",
" RL \n",
" 60.0 \n",
" 9550 \n",
" Pave \n",
" NaN \n",
" IR1 \n",
" Lvl \n",
" AllPub \n",
" ... \n",
" 0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0 \n",
" 2 \n",
" 2006 \n",
" WD \n",
" Abnorml \n",
" 140000 \n",
" \n",
" \n",
" 4 \n",
" 5 \n",
" 60 \n",
" RL \n",
" 84.0 \n",
" 14260 \n",
" Pave \n",
" NaN \n",
" IR1 \n",
" Lvl \n",
" AllPub \n",
" ... \n",
" 0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0 \n",
" 12 \n",
" 2008 \n",
" WD \n",
" Normal \n",
" 250000 \n",
" \n",
" \n",
"
\n",
"5 rows × 81 columns
\n",
""
],
"text/plain": [
" Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n",
"0 1 60 RL 65.0 8450 Pave NaN Reg \n",
"1 2 20 RL 80.0 9600 Pave NaN Reg \n",
"2 3 60 RL 68.0 11250 Pave NaN IR1 \n",
"3 4 70 RL 60.0 9550 Pave NaN IR1 \n",
"4 5 60 RL 84.0 14260 Pave NaN IR1 \n",
"\n",
" LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal \\\n",
"0 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"1 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"2 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"3 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"4 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"\n",
" MoSold YrSold SaleType SaleCondition SalePrice \n",
"0 2 2008 WD Normal 208500 \n",
"1 5 2007 WD Normal 181500 \n",
"2 9 2008 WD Normal 223500 \n",
"3 2 2006 WD Abnorml 140000 \n",
"4 12 2008 WD Normal 250000 \n",
"\n",
"[5 rows x 81 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = pd.read_csv('../input/train.csv')\n",
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Id \n",
" MSSubClass \n",
" MSZoning \n",
" LotFrontage \n",
" LotArea \n",
" Street \n",
" Alley \n",
" LotShape \n",
" LandContour \n",
" Utilities \n",
" ... \n",
" ScreenPorch \n",
" PoolArea \n",
" PoolQC \n",
" Fence \n",
" MiscFeature \n",
" MiscVal \n",
" MoSold \n",
" YrSold \n",
" SaleType \n",
" SaleCondition \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1461 \n",
" 20 \n",
" RH \n",
" 80.0 \n",
" 11622 \n",
" Pave \n",
" NaN \n",
" Reg \n",
" Lvl \n",
" AllPub \n",
" ... \n",
" 120 \n",
" 0 \n",
" NaN \n",
" MnPrv \n",
" NaN \n",
" 0 \n",
" 6 \n",
" 2010 \n",
" WD \n",
" Normal \n",
" \n",
" \n",
" 1 \n",
" 1462 \n",
" 20 \n",
" RL \n",
" 81.0 \n",
" 14267 \n",
" Pave \n",
" NaN \n",
" IR1 \n",
" Lvl \n",
" AllPub \n",
" ... \n",
" 0 \n",
" 0 \n",
" NaN \n",
" NaN \n",
" Gar2 \n",
" 12500 \n",
" 6 \n",
" 2010 \n",
" WD \n",
" Normal \n",
" \n",
" \n",
" 2 \n",
" 1463 \n",
" 60 \n",
" RL \n",
" 74.0 \n",
" 13830 \n",
" Pave \n",
" NaN \n",
" IR1 \n",
" Lvl \n",
" AllPub \n",
" ... \n",
" 0 \n",
" 0 \n",
" NaN \n",
" MnPrv \n",
" NaN \n",
" 0 \n",
" 3 \n",
" 2010 \n",
" WD \n",
" Normal \n",
" \n",
" \n",
" 3 \n",
" 1464 \n",
" 60 \n",
" RL \n",
" 78.0 \n",
" 9978 \n",
" Pave \n",
" NaN \n",
" IR1 \n",
" Lvl \n",
" AllPub \n",
" ... \n",
" 0 \n",
" 0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0 \n",
" 6 \n",
" 2010 \n",
" WD \n",
" Normal \n",
" \n",
" \n",
" 4 \n",
" 1465 \n",
" 120 \n",
" RL \n",
" 43.0 \n",
" 5005 \n",
" Pave \n",
" NaN \n",
" IR1 \n",
" HLS \n",
" AllPub \n",
" ... \n",
" 144 \n",
" 0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 0 \n",
" 1 \n",
" 2010 \n",
" WD \n",
" Normal \n",
" \n",
" \n",
"
\n",
"5 rows × 80 columns
\n",
""
],
"text/plain": [
" Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n",
"0 1461 20 RH 80.0 11622 Pave NaN Reg \n",
"1 1462 20 RL 81.0 14267 Pave NaN IR1 \n",
"2 1463 60 RL 74.0 13830 Pave NaN IR1 \n",
"3 1464 60 RL 78.0 9978 Pave NaN IR1 \n",
"4 1465 120 RL 43.0 5005 Pave NaN IR1 \n",
"\n",
" LandContour Utilities ... ScreenPorch PoolArea PoolQC Fence \\\n",
"0 Lvl AllPub ... 120 0 NaN MnPrv \n",
"1 Lvl AllPub ... 0 0 NaN NaN \n",
"2 Lvl AllPub ... 0 0 NaN MnPrv \n",
"3 Lvl AllPub ... 0 0 NaN NaN \n",
"4 HLS AllPub ... 144 0 NaN NaN \n",
"\n",
" MiscFeature MiscVal MoSold YrSold SaleType SaleCondition \n",
"0 NaN 0 6 2010 WD Normal \n",
"1 Gar2 12500 6 2010 WD Normal \n",
"2 NaN 0 3 2010 WD Normal \n",
"3 NaN 0 6 2010 WD Normal \n",
"4 NaN 0 1 2010 WD Normal \n",
"\n",
"[5 rows x 80 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test = pd.read_csv('../input/test.csv')\n",
"test.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"数值型和类别型分开处理"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('int64')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.dtypes['Id']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Id', 'MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\n",
"['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']\n"
]
}
],
"source": [
"num_columns = []\n",
"cate_columns = []\n",
"for column in test.columns:\n",
" if test.dtypes[column] != np.dtype('object'):\n",
" num_columns.append(column)\n",
" else:\n",
" cate_columns.append(column)\n",
"print(num_columns)\n",
"print(cate_columns)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"label = train.pop('SalePrice')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"填充缺失"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 数值型用中值填充\n",
"for column in num_columns:\n",
" train[column] = train[column].fillna(train[column].median())\n",
" test[column] = test[column].fillna(test[column].median())\n",
"\n",
"# # 类别型用最多的填充 # 线上0.13488\n",
"# for column in cate_columns:\n",
"# train[column] = train[column].fillna(train[column].mode())\n",
"# test[column] = test[column].fillna(test[column].mode())\n",
" \n",
"# 类别型填充'NaN' # 线上0.13436\n",
"for column in cate_columns:\n",
" train[column] = train[column].fillna('NaN')\n",
" test[column] = test[column].fillna('NaN')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"类别型哑变量处理"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = pd.concat([train,test],axis=0) #训练集要和测试集放一起\n",
"for column in cate_columns:\n",
" \n",
" t = pd.get_dummies(data[column],prefix=column)\n",
" train = pd.concat([train,t[:len(train)]],axis=1)\n",
" train.drop(column,axis=1,inplace=True)\n",
" \n",
" test = pd.concat([test,t[len(train):]],axis=1)\n",
" test.drop(column,axis=1,inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"回归走起"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.linear_model import Lasso,LinearRegression,Ridge,ElasticNet,TheilSenRegressor,HuberRegressor,RANSACRegressor\n",
"from sklearn.svm import SVR\n",
"from sklearn.tree import DecisionTreeRegressor,ExtraTreeRegressor\n",
"from sklearn.ensemble import AdaBoostRegressor,ExtraTreesRegressor,GradientBoostingRegressor,RandomForestRegressor\n",
"from xgboost import XGBRegressor\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import mean_squared_error\n",
"import itertools"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(train, label, test_size=0.33, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"regs = [\n",
" ['Lasso',Lasso()],\n",
" ['LinearRegression',LinearRegression()],\n",
" ['Ridge',Ridge()],\n",
" ['ElasticNet',ElasticNet()],\n",
" ['TheilSenRegressor',TheilSenRegressor()],\n",
" ['RANSACRegressor',RANSACRegressor()],\n",
" ['HuberRegressor',HuberRegressor()],\n",
" ['SVR',SVR(kernel='linear')],\n",
" ['DecisionTreeRegressor',DecisionTreeRegressor()],\n",
" ['ExtraTreeRegressor',ExtraTreeRegressor()],\n",
" ['AdaBoostRegressor',AdaBoostRegressor(n_estimators=150)],\n",
" ['ExtraTreesRegressor',ExtraTreesRegressor(n_estimators=150)],\n",
" ['GradientBoostingRegressor',GradientBoostingRegressor(n_estimators=150)],\n",
" ['RandomForestRegressor',RandomForestRegressor(n_estimators=150)],\n",
" ['XGBRegressor',XGBRegressor(n_estimators=150)],\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lasso\n",
"LinearRegression"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
" ConvergenceWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Ridge\n",
"ElasticNet\n",
"TheilSenRegressor\n",
"y_pred have 1 negative values, we fill it with np.median(y_pred)\n",
"RANSACRegressor\n",
"y_pred have 1 negative values, we fill it with np.median(y_pred)\n",
"HuberRegressor\n",
"SVR\n",
"DecisionTreeRegressor\n",
"ExtraTreeRegressor\n",
"AdaBoostRegressor\n",
"ExtraTreesRegressor\n",
"GradientBoostingRegressor\n",
"RandomForestRegressor\n",
"XGBRegressor\n"
]
}
],
"source": [
"preds = []\n",
"for reg_name,reg in regs:\n",
" print(reg_name)\n",
" reg.fit(X_train,y_train)\n",
" y_pred = reg.predict(X_test)\n",
" if np.sum(y_pred<0) > 0:\n",
" print('y_pred have',np.sum(y_pred<0),'negative values, we fill it with np.median(y_pred)')\n",
" y_pred[y_pred<0] = np.median(y_pred)\n",
" score = np.sqrt(mean_squared_error(np.log(y_test),np.log(y_pred)))\n",
" preds.append([reg_name,y_pred])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model Amount : 1\n",
"GradientBoostingRegressor 0.133400775498\n",
"XGBRegressor 0.135452373343\n",
"RandomForestRegressor 0.146986410514\n",
"ExtraTreesRegressor 0.163995003619\n",
"Lasso 0.164197374564\n",
"Ridge 0.165564495344\n",
"ElasticNet 0.173365651563\n",
"LinearRegression 0.176583028477\n",
"SVR 0.19083071496\n",
"TheilSenRegressor 0.191042908374\n",
"RANSACRegressor 0.192942079892\n",
"AdaBoostRegressor 0.204862821167\n",
"DecisionTreeRegressor 0.211129131696\n",
"HuberRegressor 0.230319112049\n",
"ExtraTreeRegressor 0.232806183519\n",
"\n",
"Model Amount : 2\n",
"Lasso+XGBRegressor 0.127194837142\n",
"Lasso+GradientBoostingRegressor 0.127648646599\n",
"LinearRegression+XGBRegressor 0.129854978109\n",
"LinearRegression+GradientBoostingRegressor 0.130322414269\n",
"Lasso+RandomForestRegressor 0.131777367574\n",
"Ridge+GradientBoostingRegressor 0.132674339351\n",
"Ridge+XGBRegressor 0.132717636512\n",
"GradientBoostingRegressor+XGBRegressor 0.133092668715\n",
"TheilSenRegressor+GradientBoostingRegressor 0.134189654069\n",
"LinearRegression+RandomForestRegressor 0.134459423562\n",
"TheilSenRegressor+XGBRegressor 0.13452849859\n",
"Ridge+RandomForestRegressor 0.135965557993\n",
"GradientBoostingRegressor+RandomForestRegressor 0.136722351754\n",
"RandomForestRegressor+XGBRegressor 0.137379778459\n",
"RANSACRegressor+XGBRegressor 0.137778252354\n",
"RANSACRegressor+GradientBoostingRegressor 0.13887130292\n",
"TheilSenRegressor+RandomForestRegressor 0.138939845235\n",
"Lasso+ExtraTreesRegressor 0.139028201472\n",
"ElasticNet+GradientBoostingRegressor 0.140793992378\n",
"ElasticNet+XGBRegressor 0.141269235288\n",
"LinearRegression+ExtraTreesRegressor 0.141278575302\n",
"ExtraTreesRegressor+GradientBoostingRegressor 0.142456173756\n",
"RANSACRegressor+RandomForestRegressor 0.142529450531\n",
"ExtraTreesRegressor+XGBRegressor 0.142661094906\n",
"Lasso+ElasticNet 0.143739306229\n",
"SVR+GradientBoostingRegressor 0.144334394399\n",
"SVR+XGBRegressor 0.144429354811\n",
"Lasso+SVR 0.144839874141\n",
"Ridge+ExtraTreesRegressor 0.145533921197\n",
"TheilSenRegressor+ExtraTreesRegressor 0.145762688821\n",
"ElasticNet+RandomForestRegressor 0.145930647279\n",
"LinearRegression+ElasticNet 0.14714366104\n",
"Ridge+SVR 0.148675979762\n",
"RANSACRegressor+ExtraTreesRegressor 0.149035532702\n",
"LinearRegression+SVR 0.149463974892\n",
"ExtraTreesRegressor+RandomForestRegressor 0.150897032771\n",
"ElasticNet+RANSACRegressor 0.151238088126\n",
"Lasso+DecisionTreeRegressor 0.151435568567\n",
"ElasticNet+ExtraTreesRegressor 0.151484233334\n",
"SVR+RandomForestRegressor 0.151802505259\n",
"Lasso+AdaBoostRegressor 0.152727177296\n",
"Ridge+DecisionTreeRegressor 0.153349579532\n",
"LinearRegression+AdaBoostRegressor 0.153588796404\n",
"SVR+ExtraTreesRegressor 0.153773908357\n",
"TheilSenRegressor+SVR 0.154020790332\n",
"Ridge+ElasticNet 0.154222928805\n",
"HuberRegressor+GradientBoostingRegressor 0.155171883334\n",
"HuberRegressor+XGBRegressor 0.155421011111\n",
"Lasso+Ridge 0.155731170786\n",
"TheilSenRegressor+RANSACRegressor 0.155745389175\n",
"Ridge+TheilSenRegressor 0.156467195378\n",
"ElasticNet+TheilSenRegressor 0.15663707817\n",
"DecisionTreeRegressor+GradientBoostingRegressor 0.156878250168\n",
"Lasso+HuberRegressor 0.156936725358\n",
"DecisionTreeRegressor+XGBRegressor 0.156984998134\n",
"Ridge+HuberRegressor 0.157458812217\n",
"LinearRegression+DecisionTreeRegressor 0.157658699295\n",
"Ridge+AdaBoostRegressor 0.158269919879\n",
"TheilSenRegressor+DecisionTreeRegressor 0.159291493516\n",
"AdaBoostRegressor+GradientBoostingRegressor 0.159476695957\n",
"Ridge+RANSACRegressor 0.159712355716\n",
"TheilSenRegressor+AdaBoostRegressor 0.159890970839\n",
"AdaBoostRegressor+XGBRegressor 0.160207457994\n",
"LinearRegression+TheilSenRegressor 0.160341205153\n",
"RANSACRegressor+SVR 0.160352896285\n",
"LinearRegression+HuberRegressor 0.160413162684\n",
"Lasso+TheilSenRegressor 0.160669786588\n",
"RANSACRegressor+AdaBoostRegressor 0.161124399361\n",
"LinearRegression+Ridge 0.16140737581\n",
"Lasso+RANSACRegressor 0.161466311938\n",
"RANSACRegressor+DecisionTreeRegressor 0.161718787176\n",
"TheilSenRegressor+HuberRegressor 0.162084000758\n",
"ElasticNet+DecisionTreeRegressor 0.162954948425\n",
"ExtraTreeRegressor+GradientBoostingRegressor 0.16391025733\n",
"HuberRegressor+RandomForestRegressor 0.164903671718\n",
"ExtraTreeRegressor+XGBRegressor 0.165114883621\n",
"DecisionTreeRegressor+RandomForestRegressor 0.165273826908\n",
"HuberRegressor+ExtraTreesRegressor 0.165623710284\n",
"Lasso+ExtraTreeRegressor 0.166008878878\n",
"ElasticNet+AdaBoostRegressor 0.166014141401\n",
"LinearRegression+RANSACRegressor 0.166404470152\n",
"Lasso+LinearRegression 0.166758170725\n",
"SVR+DecisionTreeRegressor 0.167077526763\n",
"SVR+AdaBoostRegressor 0.167284448134\n",
"TheilSenRegressor+ExtraTreeRegressor 0.167491407559\n",
"LinearRegression+ExtraTreeRegressor 0.167894255825\n",
"DecisionTreeRegressor+ExtraTreesRegressor 0.167947156153\n",
"Ridge+ExtraTreeRegressor 0.168542361882\n",
"AdaBoostRegressor+RandomForestRegressor 0.168634913239\n",
"RANSACRegressor+ExtraTreeRegressor 0.170671156869\n",
"ExtraTreeRegressor+RandomForestRegressor 0.170815887983\n",
"ElasticNet+SVR 0.171791460365\n",
"ElasticNet+ExtraTreeRegressor 0.172058735931\n",
"AdaBoostRegressor+ExtraTreesRegressor 0.174525027385\n",
"SVR+ExtraTreeRegressor 0.17503544538\n",
"ExtraTreeRegressor+ExtraTreesRegressor 0.176785179871\n",
"ElasticNet+HuberRegressor 0.176930865209\n",
"RANSACRegressor+HuberRegressor 0.177538361764\n",
"HuberRegressor+DecisionTreeRegressor 0.178652706775\n",
"HuberRegressor+AdaBoostRegressor 0.179775917092\n",
"DecisionTreeRegressor+AdaBoostRegressor 0.183158779112\n",
"HuberRegressor+ExtraTreeRegressor 0.184983904469\n",
"DecisionTreeRegressor+ExtraTreeRegressor 0.187241512071\n",
"ExtraTreeRegressor+AdaBoostRegressor 0.188826296636\n",
"HuberRegressor+SVR 0.201144832431\n",
"\n",
"Model Amount : 3\n",
"Lasso+GradientBoostingRegressor+XGBRegressor 0.124979582373\n",
"LinearRegression+GradientBoostingRegressor+XGBRegressor 0.126070603112\n",
"Lasso+GradientBoostingRegressor+RandomForestRegressor 0.127349903462\n",
"Lasso+RandomForestRegressor+XGBRegressor 0.12736301581\n",
"LinearRegression+RandomForestRegressor+XGBRegressor 0.128456964708\n",
"LinearRegression+GradientBoostingRegressor+RandomForestRegressor 0.128460717855\n",
"Ridge+GradientBoostingRegressor+XGBRegressor 0.128869822618\n",
"TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.129256345946\n",
"Ridge+GradientBoostingRegressor+RandomForestRegressor 0.130631927178\n",
"Ridge+RandomForestRegressor+XGBRegressor 0.130822051512\n",
"Lasso+ExtraTreesRegressor+XGBRegressor 0.13101688532\n",
"Lasso+SVR+XGBRegressor 0.131062962077\n",
"Lasso+ExtraTreesRegressor+GradientBoostingRegressor 0.131174046484\n",
"Lasso+ElasticNet+XGBRegressor 0.131352935978\n",
"Lasso+SVR+GradientBoostingRegressor 0.131388980045\n",
"RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.1315453077\n",
"Lasso+ElasticNet+GradientBoostingRegressor 0.131575408013\n",
"TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131601465568\n",
"TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.131921433551\n",
"LinearRegression+ExtraTreesRegressor+XGBRegressor 0.131985026291\n",
"LinearRegression+ExtraTreesRegressor+GradientBoostingRegressor 0.13215564798\n",
"LinearRegression+ElasticNet+XGBRegressor 0.132774799511\n",
"LinearRegression+SVR+XGBRegressor 0.133008105872\n",
"LinearRegression+ElasticNet+GradientBoostingRegressor 0.133033328397\n",
"Lasso+Ridge+XGBRegressor 0.133036107338\n",
"LinearRegression+SVR+GradientBoostingRegressor 0.133345358091\n",
"Lasso+Ridge+GradientBoostingRegressor 0.1334359391\n",
"RANSACRegressor+RandomForestRegressor+XGBRegressor 0.133766941366\n",
"RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13405755034\n",
"Lasso+ElasticNet+RandomForestRegressor 0.134156709443\n",
"Ridge+SVR+XGBRegressor 0.134177123947\n",
"Ridge+SVR+GradientBoostingRegressor 0.134338876769\n",
"Lasso+LinearRegression+XGBRegressor 0.134593407945\n",
"Lasso+RANSACRegressor+XGBRegressor 0.134604199059\n",
"GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134718726418\n",
"Lasso+Ridge+RandomForestRegressor 0.134913478165\n",
"Lasso+TheilSenRegressor+XGBRegressor 0.134981602937\n",
"LinearRegression+TheilSenRegressor+XGBRegressor 0.134991516604\n",
"TheilSenRegressor+RANSACRegressor+XGBRegressor 0.135097508357\n",
"Lasso+SVR+RandomForestRegressor 0.135260126413\n",
"TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135260155044\n",
"Lasso+TheilSenRegressor+GradientBoostingRegressor 0.135276755258\n",
"LinearRegression+TheilSenRegressor+GradientBoostingRegressor 0.135302532504\n",
"Lasso+RANSACRegressor+GradientBoostingRegressor 0.135331812635\n",
"Lasso+LinearRegression+GradientBoostingRegressor 0.135339576936\n",
"TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.135351792211\n",
"TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.135374084802\n",
"Ridge+TheilSenRegressor+XGBRegressor 0.135464910981\n",
"LinearRegression+Ridge+XGBRegressor 0.135495837447\n",
"ElasticNet+GradientBoostingRegressor+XGBRegressor 0.13550120494\n",
"Ridge+TheilSenRegressor+GradientBoostingRegressor 0.135514286653\n",
"LinearRegression+ElasticNet+RandomForestRegressor 0.135537848931\n",
"Ridge+ExtraTreesRegressor+GradientBoostingRegressor 0.135610836355\n",
"Ridge+ExtraTreesRegressor+XGBRegressor 0.135654860976\n",
"Lasso+ExtraTreesRegressor+RandomForestRegressor 0.135689633617\n",
"LinearRegression+Ridge+GradientBoostingRegressor 0.13590756415\n",
"SVR+GradientBoostingRegressor+XGBRegressor 0.136090460273\n",
"TheilSenRegressor+SVR+GradientBoostingRegressor 0.136312109027\n",
"TheilSenRegressor+SVR+XGBRegressor 0.136356767408\n",
"ElasticNet+RANSACRegressor+XGBRegressor 0.136363058136\n",
"Lasso+DecisionTreeRegressor+XGBRegressor 0.136548469109\n",
"LinearRegression+RANSACRegressor+XGBRegressor 0.136566486885\n",
"Ridge+RANSACRegressor+XGBRegressor 0.136657772155\n",
"LinearRegression+ExtraTreesRegressor+RandomForestRegressor 0.136679687489\n",
"ElasticNet+RANSACRegressor+GradientBoostingRegressor 0.136739306783\n",
"Lasso+DecisionTreeRegressor+GradientBoostingRegressor 0.136932341872\n",
"Lasso+LinearRegression+RandomForestRegressor 0.136991613268\n",
"ElasticNet+TheilSenRegressor+GradientBoostingRegressor 0.137012393762\n",
"LinearRegression+SVR+RandomForestRegressor 0.137118906089\n",
"Ridge+ElasticNet+XGBRegressor 0.137132141619\n",
"ElasticNet+TheilSenRegressor+XGBRegressor 0.137161630119\n",
"Ridge+RANSACRegressor+GradientBoostingRegressor 0.137188944023\n",
"Ridge+ElasticNet+GradientBoostingRegressor 0.137199716108\n",
"RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.137222119301\n",
"LinearRegression+TheilSenRegressor+RandomForestRegressor 0.137235232839\n",
"LinearRegression+RANSACRegressor+GradientBoostingRegressor 0.137273661666\n",
"Lasso+TheilSenRegressor+RandomForestRegressor 0.137311561995\n",
"LinearRegression+Ridge+RandomForestRegressor 0.137337422307\n",
"Lasso+SVR+ExtraTreesRegressor 0.137367717867\n",
"ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137399536713\n",
"Lasso+RANSACRegressor+RandomForestRegressor 0.137454032606\n",
"Ridge+TheilSenRegressor+RandomForestRegressor 0.137455173145\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137599210446\n",
"Lasso+HuberRegressor+XGBRegressor 0.137614363948\n",
"Lasso+HuberRegressor+GradientBoostingRegressor 0.137757389997\n",
"Ridge+SVR+RandomForestRegressor 0.137870766356\n",
"ElasticNet+GradientBoostingRegressor+RandomForestRegressor 0.137884075568\n",
"Lasso+ElasticNet+ExtraTreesRegressor 0.137892093447\n",
"TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.13801425231\n",
"ElasticNet+RandomForestRegressor+XGBRegressor 0.138190814378\n",
"Ridge+RANSACRegressor+RandomForestRegressor 0.138638378641\n",
"RANSACRegressor+SVR+XGBRegressor 0.13877987889\n",
"LinearRegression+DecisionTreeRegressor+XGBRegressor 0.138794168796\n",
"Ridge+DecisionTreeRegressor+XGBRegressor 0.138951643956\n",
"LinearRegression+SVR+ExtraTreesRegressor 0.139117820317\n",
"LinearRegression+HuberRegressor+XGBRegressor 0.139151426183\n",
"Ridge+DecisionTreeRegressor+GradientBoostingRegressor 0.139159982897\n",
"LinearRegression+DecisionTreeRegressor+GradientBoostingRegressor 0.139172198546\n",
"LinearRegression+ElasticNet+ExtraTreesRegressor 0.139209828335\n",
"LinearRegression+HuberRegressor+GradientBoostingRegressor 0.139302747191\n",
"RANSACRegressor+SVR+GradientBoostingRegressor 0.139313665543\n",
"Ridge+HuberRegressor+XGBRegressor 0.139328902131\n",
"Ridge+HuberRegressor+GradientBoostingRegressor 0.139333738888\n",
"LinearRegression+RANSACRegressor+RandomForestRegressor 0.139371799073\n",
"Ridge+ElasticNet+RandomForestRegressor 0.139432060261\n",
"ElasticNet+RANSACRegressor+RandomForestRegressor 0.139550770611\n",
"Lasso+AdaBoostRegressor+GradientBoostingRegressor 0.139692172295\n",
"Ridge+ExtraTreesRegressor+RandomForestRegressor 0.139694759406\n",
"SVR+GradientBoostingRegressor+RandomForestRegressor 0.139828688942\n",
"TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139903341803\n",
"Lasso+AdaBoostRegressor+XGBRegressor 0.139922584567\n",
"ElasticNet+TheilSenRegressor+RandomForestRegressor 0.139932543852\n",
"SVR+RandomForestRegressor+XGBRegressor 0.139933910337\n",
"LinearRegression+AdaBoostRegressor+GradientBoostingRegressor 0.140014653965\n",
"Lasso+Ridge+ExtraTreesRegressor 0.14020117687\n",
"LinearRegression+AdaBoostRegressor+XGBRegressor 0.140223496345\n",
"TheilSenRegressor+SVR+RandomForestRegressor 0.14032274654\n",
"ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor 0.140785141187\n",
"SVR+ExtraTreesRegressor+XGBRegressor 0.140787361118\n",
"ElasticNet+ExtraTreesRegressor+XGBRegressor 0.140791829286\n",
"Lasso+LinearRegression+ExtraTreesRegressor 0.140862111768\n",
"SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.141037897556\n",
"Ridge+SVR+ExtraTreesRegressor 0.141286440305\n",
"Lasso+RANSACRegressor+ExtraTreesRegressor 0.141291470086\n",
"Lasso+Ridge+SVR 0.141307126411\n",
"TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.141338416861\n",
"LinearRegression+TheilSenRegressor+ExtraTreesRegressor 0.141359919585\n",
"TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.1413858281\n",
"Lasso+TheilSenRegressor+ExtraTreesRegressor 0.141494228673\n",
"TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.141562512009\n",
"ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141624498894\n",
"Lasso+DecisionTreeRegressor+RandomForestRegressor 0.141640462871\n",
"TheilSenRegressor+HuberRegressor+XGBRegressor 0.14173771946\n",
"ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141766669282\n",
"HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.141767544379\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.142000401318\n",
"RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142036965791\n",
"Lasso+SVR+DecisionTreeRegressor 0.142132030059\n",
"TheilSenRegressor+SVR+ExtraTreesRegressor 0.142213379179\n",
"Lasso+ElasticNet+DecisionTreeRegressor 0.142237444521\n",
"LinearRegression+Ridge+ExtraTreesRegressor 0.142477784251\n",
"Ridge+TheilSenRegressor+ExtraTreesRegressor 0.142717561417\n",
"Lasso+Ridge+DecisionTreeRegressor 0.142799380827\n",
"RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.142810101517\n",
"Lasso+HuberRegressor+RandomForestRegressor 0.142917126267\n",
"LinearRegression+RANSACRegressor+ExtraTreesRegressor 0.143017114091\n",
"ElasticNet+RANSACRegressor+ExtraTreesRegressor 0.143061847138\n",
"RANSACRegressor+SVR+RandomForestRegressor 0.143353968256\n",
"ElasticNet+TheilSenRegressor+ExtraTreesRegressor 0.143371364473\n",
"RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.143379869236\n",
"Ridge+DecisionTreeRegressor+RandomForestRegressor 0.14345209346\n",
"Lasso+ElasticNet+RANSACRegressor 0.143595317171\n",
"Ridge+RANSACRegressor+ExtraTreesRegressor 0.143621158803\n",
"Ridge+AdaBoostRegressor+GradientBoostingRegressor 0.143625157197\n",
"Ridge+SVR+DecisionTreeRegressor 0.143733390011\n",
"LinearRegression+DecisionTreeRegressor+RandomForestRegressor 0.143867060581\n",
"TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143949681602\n",
"Lasso+ExtraTreeRegressor+GradientBoostingRegressor 0.14395313715\n",
"Ridge+AdaBoostRegressor+XGBRegressor 0.143995153765\n",
"Lasso+DecisionTreeRegressor+ExtraTreesRegressor 0.144059937807\n",
"Lasso+ExtraTreeRegressor+XGBRegressor 0.144182167454\n",
"DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.144183946577\n",
"Ridge+HuberRegressor+RandomForestRegressor 0.144202397796\n",
"Lasso+SVR+AdaBoostRegressor 0.144306948427\n",
"Ridge+ElasticNet+ExtraTreesRegressor 0.144310718738\n",
"TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.144369462964\n",
"LinearRegression+HuberRegressor+RandomForestRegressor 0.144436323498\n",
"Lasso+HuberRegressor+ExtraTreesRegressor 0.144456025242\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor 0.144624333302\n",
"LinearRegression+ExtraTreeRegressor+GradientBoostingRegressor 0.14464351014\n",
"Ridge+TheilSenRegressor+SVR 0.144655559942\n",
"Lasso+Ridge+ElasticNet 0.144722255789\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor 0.144822555058\n",
"LinearRegression+ExtraTreeRegressor+XGBRegressor 0.144836106499\n",
"Lasso+LinearRegression+SVR 0.144840576063\n",
"Lasso+ElasticNet+AdaBoostRegressor 0.144866406707\n",
"Lasso+RANSACRegressor+SVR 0.144911897293\n",
"Lasso+LinearRegression+AdaBoostRegressor 0.144918461028\n",
"LinearRegression+Ridge+SVR 0.144935531601\n",
"Lasso+AdaBoostRegressor+RandomForestRegressor 0.145110657337\n",
"Lasso+Ridge+AdaBoostRegressor 0.145167750188\n",
"LinearRegression+SVR+AdaBoostRegressor 0.145203301499\n",
"Lasso+TheilSenRegressor+SVR 0.145305057898\n",
"RANSACRegressor+SVR+ExtraTreesRegressor 0.14532102163\n",
"TheilSenRegressor+RANSACRegressor+SVR 0.145368005927\n",
"LinearRegression+ElasticNet+AdaBoostRegressor 0.14541347911\n",
"LinearRegression+SVR+DecisionTreeRegressor 0.145457060231\n",
"RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.145516354757\n",
"LinearRegression+AdaBoostRegressor+RandomForestRegressor 0.145527931097\n",
"ElasticNet+ExtraTreesRegressor+RandomForestRegressor 0.145533880614\n",
"RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145591960731\n",
"LinearRegression+TheilSenRegressor+SVR 0.145605351688\n",
"Lasso+LinearRegression+ElasticNet 0.145634813181\n",
"LinearRegression+ElasticNet+RANSACRegressor 0.145653502149\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor 0.145790991729\n",
"LinearRegression+HuberRegressor+ExtraTreesRegressor 0.145850012584\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor 0.145949270022\n",
"RANSACRegressor+HuberRegressor+XGBRegressor 0.1459554773\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor 0.146052529366\n",
"Ridge+RANSACRegressor+SVR 0.146059417098\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor 0.146124913773\n",
"Ridge+ElasticNet+DecisionTreeRegressor 0.146187382015\n",
"TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.146210419439\n",
"ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor 0.14626611705\n",
"LinearRegression+ElasticNet+TheilSenRegressor 0.146294682276\n",
"RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.146295303488\n",
"ElasticNet+DecisionTreeRegressor+XGBRegressor 0.146296092416\n",
"LinearRegression+Ridge+DecisionTreeRegressor 0.146421576145\n",
"Lasso+RANSACRegressor+AdaBoostRegressor 0.146421947638\n",
"Lasso+ElasticNet+SVR 0.146437806846\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor 0.1465329126\n",
"TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.146569571423\n",
"LinearRegression+Ridge+AdaBoostRegressor 0.146581546965\n",
"Ridge+ElasticNet+RANSACRegressor 0.146583801526\n",
"HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146669969914\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor 0.146671899237\n",
"TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.146715146744\n",
"Ridge+ExtraTreeRegressor+GradientBoostingRegressor 0.146837935952\n",
"SVR+ExtraTreesRegressor+RandomForestRegressor 0.146861516438\n",
"HuberRegressor+RandomForestRegressor+XGBRegressor 0.146861724023\n",
"Lasso+LinearRegression+DecisionTreeRegressor 0.14689362802\n",
"Ridge+HuberRegressor+ExtraTreesRegressor 0.146922266184\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor 0.146941124542\n",
"Lasso+Ridge+HuberRegressor 0.146958859542\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.147004331115\n",
"Ridge+DecisionTreeRegressor+ExtraTreesRegressor 0.147070128476\n",
"SVR+DecisionTreeRegressor+XGBRegressor 0.147134043988\n",
"TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.147136251939\n",
"HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.147181430791\n",
"Ridge+ExtraTreeRegressor+XGBRegressor 0.147197749555\n",
"Lasso+ElasticNet+TheilSenRegressor 0.147262334809\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor 0.147287914916\n",
"AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.147314530695\n",
"SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.1473168892\n",
"HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.147336931042\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor 0.147382307737\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor 0.14748827827\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor 0.147727146896\n",
"LinearRegression+RANSACRegressor+SVR 0.147756090358\n",
"Ridge+SVR+AdaBoostRegressor 0.1478970737\n",
"Lasso+ExtraTreeRegressor+RandomForestRegressor 0.147955242858\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.147976702819\n",
"ElasticNet+SVR+GradientBoostingRegressor 0.147991084452\n",
"ElasticNet+SVR+XGBRegressor 0.148003307005\n",
"TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.148027350775\n",
"RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.14804519662\n",
"RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.148051905588\n",
"LinearRegression+Ridge+ElasticNet 0.148085992468\n",
"RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.148144540191\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor 0.148310800037\n",
"Ridge+RANSACRegressor+AdaBoostRegressor 0.148500851131\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.148531521519\n",
"DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.148593574952\n",
"Ridge+AdaBoostRegressor+RandomForestRegressor 0.148627428796\n",
"DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.148646878693\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor 0.148659681628\n",
"Ridge+ElasticNet+TheilSenRegressor 0.148711446092\n",
"LinearRegression+ElasticNet+SVR 0.148748336988\n",
"LinearRegression+ExtraTreeRegressor+RandomForestRegressor 0.148749879006\n",
"Ridge+TheilSenRegressor+HuberRegressor 0.148834719536\n",
"ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.148914910758\n",
"Lasso+AdaBoostRegressor+ExtraTreesRegressor 0.149160914128\n",
"TheilSenRegressor+SVR+AdaBoostRegressor 0.149268245813\n",
"Lasso+HuberRegressor+DecisionTreeRegressor 0.149378141895\n",
"TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.149424693862\n",
"LinearRegression+AdaBoostRegressor+ExtraTreesRegressor 0.149466466811\n",
"ElasticNet+AdaBoostRegressor+GradientBoostingRegressor 0.149477599128\n",
"SVR+AdaBoostRegressor+GradientBoostingRegressor 0.149495656356\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor 0.149559171764\n",
"SVR+AdaBoostRegressor+XGBRegressor 0.149667625326\n",
"LinearRegression+Ridge+HuberRegressor 0.149809204627\n",
"ElasticNet+AdaBoostRegressor+XGBRegressor 0.149839668299\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor 0.149847042323\n",
"Ridge+HuberRegressor+DecisionTreeRegressor 0.149866087842\n",
"Lasso+SVR+ExtraTreeRegressor 0.149921665073\n",
"DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.14996239711\n",
"RANSACRegressor+SVR+DecisionTreeRegressor 0.150155739123\n",
"DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.150192211585\n",
"Lasso+ElasticNet+ExtraTreeRegressor 0.150224592614\n",
"Lasso+ElasticNet+HuberRegressor 0.150312945196\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.150353142372\n",
"Ridge+ExtraTreeRegressor+RandomForestRegressor 0.150447633596\n",
"Lasso+TheilSenRegressor+HuberRegressor 0.150470512709\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor 0.15063852419\n",
"TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.150656350772\n",
"Ridge+ElasticNet+AdaBoostRegressor 0.150705003963\n",
"Ridge+TheilSenRegressor+RANSACRegressor 0.150770078512\n",
"RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.150812207405\n",
"LinearRegression+TheilSenRegressor+HuberRegressor 0.15094108315\n",
"ElasticNet+DecisionTreeRegressor+RandomForestRegressor 0.151161272773\n",
"Lasso+LinearRegression+HuberRegressor 0.151197848306\n",
"LinearRegression+SVR+ExtraTreeRegressor 0.151244466712\n",
"ElasticNet+HuberRegressor+GradientBoostingRegressor 0.151299252469\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor 0.151343563998\n",
"ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor 0.151389762499\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor 0.151405651269\n",
"ElasticNet+HuberRegressor+XGBRegressor 0.151416874308\n",
"RANSACRegressor+SVR+AdaBoostRegressor 0.151605355218\n",
"RANSACRegressor+HuberRegressor+RandomForestRegressor 0.151785158435\n",
"Lasso+ExtraTreeRegressor+ExtraTreesRegressor 0.151955451537\n",
"ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.151963204901\n",
"AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.151986888109\n",
"ElasticNet+SVR+RandomForestRegressor 0.152000672518\n",
"SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.152003110554\n",
"ElasticNet+ExtraTreeRegressor+XGBRegressor 0.152018617517\n",
"RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.152039379825\n",
"Ridge+ElasticNet+SVR 0.152089949027\n",
"Lasso+HuberRegressor+AdaBoostRegressor 0.15209146801\n",
"LinearRegression+ElasticNet+HuberRegressor 0.152109042379\n",
"Ridge+SVR+ExtraTreeRegressor 0.152153068592\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor 0.152250160671\n",
"AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.152390857281\n",
"SVR+ExtraTreeRegressor+XGBRegressor 0.152455175298\n",
"Ridge+RANSACRegressor+HuberRegressor 0.15250724214\n",
"Lasso+RANSACRegressor+HuberRegressor 0.152515485883\n",
"ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.15254844135\n",
"LinearRegression+ExtraTreeRegressor+ExtraTreesRegressor 0.152624056181\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor 0.152669782032\n",
"Lasso+TheilSenRegressor+RANSACRegressor 0.152699489581\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor 0.152816762778\n",
"Lasso+DecisionTreeRegressor+AdaBoostRegressor 0.152831304929\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor 0.153000176254\n",
"ElasticNet+RANSACRegressor+SVR 0.153009903531\n",
"ElasticNet+SVR+ExtraTreesRegressor 0.153081590468\n",
"ElasticNet+TheilSenRegressor+SVR 0.153157905546\n",
"SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.153193328188\n",
"SVR+DecisionTreeRegressor+RandomForestRegressor 0.153231607076\n",
"RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.153234720704\n",
"TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.153331736814\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor 0.153500212583\n",
"Ridge+AdaBoostRegressor+ExtraTreesRegressor 0.153513005804\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.153604612869\n",
"HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.15367083856\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor 0.153722322756\n",
"HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.153761388506\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor 0.153799852553\n",
"Ridge+ElasticNet+HuberRegressor 0.153832194374\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor 0.15399896941\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor 0.154003471061\n",
"HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.154116957527\n",
"Lasso+Ridge+TheilSenRegressor 0.154144253127\n",
"Lasso+Ridge+ExtraTreeRegressor 0.15439262095\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor 0.154397472465\n",
"Ridge+HuberRegressor+AdaBoostRegressor 0.154399646656\n",
"Lasso+Ridge+RANSACRegressor 0.154401238159\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor 0.154413842211\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor 0.154452486808\n",
"RANSACRegressor+SVR+ExtraTreeRegressor 0.154579209643\n",
"RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.154661625243\n",
"TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.1546997521\n",
"Ridge+ElasticNet+ExtraTreeRegressor 0.154916171629\n",
"LinearRegression+RANSACRegressor+HuberRegressor 0.15493260781\n",
"ElasticNet+TheilSenRegressor+HuberRegressor 0.155002727522\n",
"ElasticNet+AdaBoostRegressor+RandomForestRegressor 0.155108760947\n",
"ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.155231725882\n",
"LinearRegression+Ridge+TheilSenRegressor 0.155250790822\n",
"Ridge+DecisionTreeRegressor+AdaBoostRegressor 0.15538855796\n",
"AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.155415086742\n",
"ElasticNet+ExtraTreeRegressor+RandomForestRegressor 0.155495490519\n",
"ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.155596530141\n",
"Ridge+ExtraTreeRegressor+ExtraTreesRegressor 0.155612975915\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor 0.155627276413\n",
"AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.155728537763\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor 0.155771878305\n",
"TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.155838180554\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor 0.155960717125\n",
"RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.15596885041\n",
"SVR+AdaBoostRegressor+RandomForestRegressor 0.156041441707\n",
"LinearRegression+Ridge+ExtraTreeRegressor 0.156264288591\n",
"DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.15635688565\n",
"Lasso+HuberRegressor+ExtraTreeRegressor 0.156540710202\n",
"HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.156559054757\n",
"ElasticNet+HuberRegressor+RandomForestRegressor 0.156560887065\n",
"HuberRegressor+AdaBoostRegressor+XGBRegressor 0.156800674903\n",
"Lasso+LinearRegression+ExtraTreeRegressor 0.156863253984\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor 0.156886293056\n",
"SVR+ExtraTreeRegressor+RandomForestRegressor 0.157080965187\n",
"ElasticNet+HuberRegressor+ExtraTreesRegressor 0.157131354848\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor 0.15731989834\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor 0.157333912283\n",
"TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.157379075456\n",
"Ridge+HuberRegressor+ExtraTreeRegressor 0.157475401427\n",
"LinearRegression+Ridge+RANSACRegressor 0.157499344626\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor 0.157594479787\n",
"HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.15771948838\n",
"ElasticNet+SVR+DecisionTreeRegressor 0.15776700485\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.158160680573\n",
"HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.158186097512\n",
"SVR+AdaBoostRegressor+ExtraTreesRegressor 0.158192314009\n",
"ElasticNet+RANSACRegressor+HuberRegressor 0.158338762076\n",
"Lasso+ExtraTreeRegressor+AdaBoostRegressor 0.158473419326\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.15850296886\n",
"ElasticNet+AdaBoostRegressor+ExtraTreesRegressor 0.158555424311\n",
"ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor 0.158689146377\n",
"Lasso+LinearRegression+Ridge 0.158788310878\n",
"LinearRegression+ExtraTreeRegressor+AdaBoostRegressor 0.158815956508\n",
"SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.158850649996\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.158903316212\n",
"Lasso+HuberRegressor+SVR 0.159080801283\n",
"Lasso+LinearRegression+TheilSenRegressor 0.15913930934\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.159340761365\n",
"ElasticNet+SVR+AdaBoostRegressor 0.159544471417\n",
"DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.15965048946\n",
"DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.159749921089\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.159919617425\n",
"RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.16010841686\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.16019769713\n",
"Ridge+HuberRegressor+SVR 0.160317136176\n",
"DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.160463087672\n",
"Lasso+LinearRegression+RANSACRegressor 0.160483625918\n",
"ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.160610332362\n",
"HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.160632570736\n",
"DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.160877048887\n",
"HuberRegressor+SVR+XGBRegressor 0.161181534003\n",
"HuberRegressor+SVR+GradientBoostingRegressor 0.161239771354\n",
"LinearRegression+HuberRegressor+SVR 0.161268521263\n",
"TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.161547709854\n",
"ElasticNet+DecisionTreeRegressor+AdaBoostRegressor 0.161615069609\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.161625478929\n",
"AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.16173775264\n",
"SVR+DecisionTreeRegressor+AdaBoostRegressor 0.161750463079\n",
"Ridge+ExtraTreeRegressor+AdaBoostRegressor 0.161799766358\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor 0.162079120529\n",
"RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.162308262458\n",
"TheilSenRegressor+HuberRegressor+SVR 0.162519927836\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor 0.1626482104\n",
"ElasticNet+SVR+ExtraTreeRegressor 0.1631284164\n",
"ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.163312437177\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.163417582309\n",
"HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.163741916903\n",
"HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.163773887439\n",
"ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.163860876222\n",
"ElasticNet+HuberRegressor+AdaBoostRegressor 0.164599047724\n",
"HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.16503735735\n",
"HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.165347747977\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor 0.165989412482\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor 0.165995361759\n",
"DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.166065257087\n",
"HuberRegressor+SVR+ExtraTreesRegressor 0.166223595423\n",
"DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.16625913363\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor 0.16666748115\n",
"HuberRegressor+SVR+RandomForestRegressor 0.167248615541\n",
"DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.167921298128\n",
"DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.168696739833\n",
"ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.169176698515\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.169328715821\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.169879535648\n",
"HuberRegressor+SVR+DecisionTreeRegressor 0.17172430101\n",
"HuberRegressor+SVR+AdaBoostRegressor 0.172132631452\n",
"RANSACRegressor+HuberRegressor+SVR 0.172162631801\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.172729998218\n",
"ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.173025384464\n",
"HuberRegressor+SVR+ExtraTreeRegressor 0.175330532009\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.176100154278\n",
"ElasticNet+HuberRegressor+SVR 0.178558575565\n",
"\n",
"Model Amount : 4\n",
"Lasso+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.126842572869\n",
"LinearRegression+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127430882312\n",
"Lasso+LinearRegression+GradientBoostingRegressor+XGBRegressor 0.127534306838\n",
"Lasso+Ridge+GradientBoostingRegressor+XGBRegressor 0.127715306802\n",
"Lasso+SVR+GradientBoostingRegressor+XGBRegressor 0.127863422257\n",
"Lasso+ElasticNet+GradientBoostingRegressor+XGBRegressor 0.128519203008\n",
"Lasso+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.128580466535\n",
"LinearRegression+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.128623700788\n",
"Lasso+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.128733522112\n",
"Lasso+LinearRegression+RandomForestRegressor+XGBRegressor 0.128896785934\n",
"Lasso+Ridge+RandomForestRegressor+XGBRegressor 0.128907077943\n",
"LinearRegression+SVR+GradientBoostingRegressor+XGBRegressor 0.128914364323\n",
"Lasso+Ridge+GradientBoostingRegressor+RandomForestRegressor 0.128988758634\n",
"LinearRegression+Ridge+GradientBoostingRegressor+XGBRegressor 0.129058076766\n",
"Lasso+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129092510098\n",
"Lasso+LinearRegression+GradientBoostingRegressor+RandomForestRegressor 0.129132461864\n",
"LinearRegression+ElasticNet+GradientBoostingRegressor+XGBRegressor 0.12929462195\n",
"Ridge+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129531479167\n",
"LinearRegression+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129625956438\n",
"TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.129722296025\n",
"LinearRegression+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.129754689213\n",
"Ridge+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.129759303038\n",
"TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12998529459\n",
"Lasso+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.130166353911\n",
"LinearRegression+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.130169733677\n",
"Lasso+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.130177217064\n",
"LinearRegression+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13019076876\n",
"LinearRegression+Ridge+RandomForestRegressor+XGBRegressor 0.130229545438\n",
"Lasso+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.130266810795\n",
"Lasso+ElasticNet+RandomForestRegressor+XGBRegressor 0.130271838409\n",
"Lasso+ElasticNet+GradientBoostingRegressor+RandomForestRegressor 0.130276942946\n",
"LinearRegression+Ridge+GradientBoostingRegressor+RandomForestRegressor 0.130323006694\n",
"Ridge+SVR+GradientBoostingRegressor+XGBRegressor 0.130330417399\n",
"Lasso+SVR+RandomForestRegressor+XGBRegressor 0.130355054836\n",
"Lasso+SVR+GradientBoostingRegressor+RandomForestRegressor 0.130446319925\n",
"Lasso+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.130565382646\n",
"Ridge+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130833799846\n",
"LinearRegression+ElasticNet+RandomForestRegressor+XGBRegressor 0.131017488975\n",
"LinearRegression+ElasticNet+GradientBoostingRegressor+RandomForestRegressor 0.131042337956\n",
"Ridge+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13111762219\n",
"Lasso+Ridge+SVR+XGBRegressor 0.131207781942\n",
"Ridge+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.131213573793\n",
"LinearRegression+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.131278390691\n",
"LinearRegression+SVR+RandomForestRegressor+XGBRegressor 0.131367645943\n",
"TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131380100427\n",
"Lasso+LinearRegression+ExtraTreesRegressor+XGBRegressor 0.131416924195\n",
"LinearRegression+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131469787125\n",
"RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131475299394\n",
"Lasso+Ridge+SVR+GradientBoostingRegressor 0.131505413944\n",
"TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.131549062234\n",
"LinearRegression+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131575745142\n",
"Lasso+SVR+ExtraTreesRegressor+XGBRegressor 0.131583567417\n",
"TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131626064317\n",
"Lasso+LinearRegression+ExtraTreesRegressor+GradientBoostingRegressor 0.131732770836\n",
"Lasso+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.131840102423\n",
"Lasso+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.131920902716\n",
"Lasso+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131949296703\n",
"Lasso+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131978148772\n",
"Lasso+LinearRegression+SVR+XGBRegressor 0.132004195002\n",
"Ridge+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.132006136938\n",
"TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132119541518\n",
"ElasticNet+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.132167395997\n",
"Ridge+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132198429338\n",
"Lasso+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132215382118\n",
"Lasso+Ridge+ExtraTreesRegressor+XGBRegressor 0.132237131032\n",
"ElasticNet+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.132319163715\n",
"Lasso+Ridge+ExtraTreesRegressor+GradientBoostingRegressor 0.132392989087\n",
"Ridge+ElasticNet+GradientBoostingRegressor+XGBRegressor 0.132446418142\n",
"Lasso+LinearRegression+SVR+GradientBoostingRegressor 0.132458675738\n",
"Lasso+ElasticNet+ExtraTreesRegressor+XGBRegressor 0.132484699453\n",
"LinearRegression+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132486638672\n",
"Ridge+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132490979828\n",
"LinearRegression+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132525245944\n",
"LinearRegression+SVR+ExtraTreesRegressor+XGBRegressor 0.132549605813\n",
"Ridge+SVR+RandomForestRegressor+XGBRegressor 0.132604954593\n",
"Ridge+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132612756012\n",
"Lasso+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor 0.132630476184\n",
"LinearRegression+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.132734197967\n",
"Lasso+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.132763732351\n",
"Lasso+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.132769202082\n",
"LinearRegression+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132774391851\n",
"LinearRegression+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132815014586\n",
"LinearRegression+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132866163842\n",
"Lasso+LinearRegression+ElasticNet+XGBRegressor 0.132867552575\n",
"Lasso+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132886375944\n",
"Lasso+ElasticNet+RANSACRegressor+XGBRegressor 0.132967144019\n",
"RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133055567739\n",
"Lasso+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133111810514\n",
"LinearRegression+Ridge+SVR+XGBRegressor 0.133151908942\n",
"Lasso+RANSACRegressor+SVR+XGBRegressor 0.133185385378\n",
"LinearRegression+ElasticNet+ExtraTreesRegressor+XGBRegressor 0.133201007036\n",
"Lasso+TheilSenRegressor+SVR+XGBRegressor 0.133281767219\n",
"Lasso+LinearRegression+ElasticNet+GradientBoostingRegressor 0.133299340474\n",
"Lasso+ElasticNet+RANSACRegressor+GradientBoostingRegressor 0.133335878004\n",
"LinearRegression+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133354471894\n",
"LinearRegression+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor 0.133364988902\n",
"Lasso+Ridge+DecisionTreeRegressor+XGBRegressor 0.133375164592\n",
"LinearRegression+Ridge+SVR+GradientBoostingRegressor 0.133459376129\n",
"Lasso+TheilSenRegressor+SVR+GradientBoostingRegressor 0.133489641034\n",
"LinearRegression+Ridge+ExtraTreesRegressor+XGBRegressor 0.133496382826\n",
"Lasso+Ridge+SVR+RandomForestRegressor 0.133572449587\n",
"LinearRegression+TheilSenRegressor+SVR+XGBRegressor 0.133578971887\n",
"Ridge+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133625413269\n",
"Lasso+RANSACRegressor+SVR+GradientBoostingRegressor 0.133626495161\n",
"RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133631710503\n",
"LinearRegression+Ridge+ExtraTreesRegressor+GradientBoostingRegressor 0.133662758442\n",
"Lasso+Ridge+ElasticNet+XGBRegressor 0.133673007237\n",
"LinearRegression+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.133697680785\n",
"Lasso+Ridge+DecisionTreeRegressor+GradientBoostingRegressor 0.133729042215\n",
"LinearRegression+TheilSenRegressor+SVR+GradientBoostingRegressor 0.133796624003\n",
"TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.133871141771\n",
"Ridge+ElasticNet+GradientBoostingRegressor+RandomForestRegressor 0.133902983575\n",
"ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133910464866\n",
"Ridge+TheilSenRegressor+SVR+XGBRegressor 0.133915318338\n",
"Lasso+Ridge+ElasticNet+GradientBoostingRegressor 0.133930729294\n",
"TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.133967224933\n",
"Ridge+ElasticNet+RandomForestRegressor+XGBRegressor 0.133975315175\n",
"Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor 0.134014001425\n",
"LinearRegression+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134037945383\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.1340661869\n",
"LinearRegression+ElasticNet+RANSACRegressor+XGBRegressor 0.134080753717\n",
"ElasticNet+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.134092082604\n",
"ElasticNet+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.134163357974\n",
"Lasso+SVR+DecisionTreeRegressor+XGBRegressor 0.134210470208\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134215479686\n",
"ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134290784032\n",
"Ridge+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134311716547\n",
"Lasso+LinearRegression+ExtraTreesRegressor+RandomForestRegressor 0.134371137824\n",
"LinearRegression+ElasticNet+TheilSenRegressor+XGBRegressor 0.134392845755\n",
"LinearRegression+ElasticNet+RANSACRegressor+GradientBoostingRegressor 0.134451785116\n",
"Lasso+LinearRegression+DecisionTreeRegressor+XGBRegressor 0.134466446316\n",
"TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.13447420718\n",
"Ridge+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.134486216114\n",
"Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13448980694\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+XGBRegressor 0.134537977743\n",
"Lasso+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.13453908911\n",
"LinearRegression+ElasticNet+TheilSenRegressor+GradientBoostingRegressor 0.134584519571\n",
"Lasso+LinearRegression+SVR+RandomForestRegressor 0.134602180387\n",
"Lasso+ElasticNet+TheilSenRegressor+XGBRegressor 0.134619940118\n",
"Ridge+SVR+ExtraTreesRegressor+XGBRegressor 0.13462614211\n",
"Lasso+LinearRegression+ElasticNet+RandomForestRegressor 0.13465825274\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.134658916039\n",
"TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.134669310927\n",
"LinearRegression+RANSACRegressor+SVR+XGBRegressor 0.134697309486\n",
"Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.134777230569\n",
"Lasso+ElasticNet+TheilSenRegressor+GradientBoostingRegressor 0.134792296313\n",
"Lasso+ElasticNet+DecisionTreeRegressor+XGBRegressor 0.134804404167\n",
"Ridge+RANSACRegressor+SVR+XGBRegressor 0.13481555527\n",
"Lasso+Ridge+ExtraTreesRegressor+RandomForestRegressor 0.134868378791\n",
"Lasso+Ridge+HuberRegressor+XGBRegressor 0.134908179198\n",
"TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.134949027872\n",
"TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134965414086\n",
"Lasso+LinearRegression+DecisionTreeRegressor+GradientBoostingRegressor 0.134969333736\n",
"Ridge+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135017436539\n",
"Lasso+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor 0.135044290429\n",
"TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.135054936633\n",
"TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135072762016\n",
"Lasso+Ridge+HuberRegressor+GradientBoostingRegressor 0.135083425385\n",
"Ridge+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135091709665\n",
"LinearRegression+RANSACRegressor+SVR+GradientBoostingRegressor 0.135131131528\n",
"Lasso+ElasticNet+RANSACRegressor+RandomForestRegressor 0.135146972283\n",
"TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.13515083401\n",
"Ridge+RANSACRegressor+SVR+GradientBoostingRegressor 0.135161206436\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.135162829933\n",
"Lasso+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135167572516\n",
"Ridge+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.135195404555\n",
"Lasso+Ridge+ElasticNet+RandomForestRegressor 0.135213997108\n",
"Lasso+HuberRegressor+RandomForestRegressor+XGBRegressor 0.135216460546\n",
"Lasso+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135219331734\n",
"Lasso+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135228037048\n",
"ElasticNet+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13526549011\n",
"LinearRegression+Ridge+DecisionTreeRegressor+XGBRegressor 0.13528215184\n",
"LinearRegression+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135318236202\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.135369238089\n",
"Lasso+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135374404988\n",
"LinearRegression+Ridge+ElasticNet+XGBRegressor 0.135405673323\n",
"Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135428019664\n",
"LinearRegression+Ridge+SVR+RandomForestRegressor 0.135453724449\n",
"TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135488520225\n",
"Lasso+LinearRegression+AdaBoostRegressor+XGBRegressor 0.135499419831\n",
"Lasso+LinearRegression+AdaBoostRegressor+GradientBoostingRegressor 0.135534271398\n",
"Lasso+SVR+ExtraTreesRegressor+RandomForestRegressor 0.135544579308\n",
"RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.135611688911\n",
"LinearRegression+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135622890435\n",
"LinearRegression+Ridge+DecisionTreeRegressor+GradientBoostingRegressor 0.135635228035\n",
"Lasso+ElasticNet+ExtraTreesRegressor+RandomForestRegressor 0.135636554923\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135641948639\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135665620716\n",
"Lasso+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135671229567\n",
"LinearRegression+Ridge+ElasticNet+GradientBoostingRegressor 0.135686721865\n",
"Ridge+ElasticNet+RANSACRegressor+XGBRegressor 0.135783918754\n",
"SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135790181812\n",
"Lasso+TheilSenRegressor+SVR+RandomForestRegressor 0.135832059104\n",
"RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.135840344575\n",
"Ridge+SVR+DecisionTreeRegressor+XGBRegressor 0.135877161949\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.13589116369\n",
"LinearRegression+SVR+DecisionTreeRegressor+XGBRegressor 0.135892463782\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.135893027958\n",
"Lasso+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135947512878\n",
"Lasso+TheilSenRegressor+RANSACRegressor+XGBRegressor 0.136005441712\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+XGBRegressor 0.136052875781\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.13605406297\n",
"LinearRegression+HuberRegressor+RandomForestRegressor+XGBRegressor 0.136057218822\n",
"LinearRegression+TheilSenRegressor+SVR+RandomForestRegressor 0.136057253868\n",
"Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor 0.136059982296\n",
"LinearRegression+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136068288298\n",
"Lasso+Ridge+SVR+ExtraTreesRegressor 0.136096334553\n",
"Lasso+ElasticNet+SVR+XGBRegressor 0.136107894772\n",
"LinearRegression+Ridge+ExtraTreesRegressor+RandomForestRegressor 0.136114893253\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RandomForestRegressor 0.136117270677\n",
"Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136119710004\n",
"ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136126607179\n",
"Lasso+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.136134568418\n",
"ElasticNet+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.136140503322\n",
"Lasso+LinearRegression+HuberRegressor+XGBRegressor 0.136148657497\n",
"Lasso+RANSACRegressor+SVR+RandomForestRegressor 0.136151468537\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.136169022184\n",
"Ridge+TheilSenRegressor+RANSACRegressor+XGBRegressor 0.136188069153\n",
"LinearRegression+ElasticNet+RANSACRegressor+RandomForestRegressor 0.136218492241\n",
"LinearRegression+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136220256692\n",
"Ridge+TheilSenRegressor+SVR+RandomForestRegressor 0.136265735991\n",
"Lasso+LinearRegression+SVR+ExtraTreesRegressor 0.136284515617\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor 0.136303091483\n",
"Lasso+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13630522642\n",
"ElasticNet+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.136306682436\n",
"Lasso+ElasticNet+SVR+GradientBoostingRegressor 0.136314645599\n",
"Lasso+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.136322094122\n",
"LinearRegression+ElasticNet+ExtraTreesRegressor+RandomForestRegressor 0.136343288644\n",
"LinearRegression+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.136354144765\n",
"Lasso+Ridge+DecisionTreeRegressor+RandomForestRegressor 0.136379261808\n",
"Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.136402578247\n",
"SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136410522769\n",
"Lasso+ElasticNet+TheilSenRegressor+RandomForestRegressor 0.136421246787\n",
"RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136453997746\n",
"Lasso+LinearRegression+HuberRegressor+GradientBoostingRegressor 0.136458458395\n",
"LinearRegression+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136485863168\n",
"LinearRegression+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136504888946\n",
"LinearRegression+Ridge+HuberRegressor+XGBRegressor 0.13650495679\n",
"ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136529830455\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+XGBRegressor 0.136537017855\n",
"Lasso+Ridge+RANSACRegressor+XGBRegressor 0.136540671126\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.136546201866\n",
"Ridge+ElasticNet+TheilSenRegressor+XGBRegressor 0.136552545578\n",
"Lasso+Ridge+AdaBoostRegressor+GradientBoostingRegressor 0.136579739977\n",
"Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor 0.13661058241\n",
"RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136616243556\n",
"Ridge+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13665374361\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.136664963927\n",
"Lasso+Ridge+AdaBoostRegressor+XGBRegressor 0.136674522663\n",
"LinearRegression+Ridge+HuberRegressor+GradientBoostingRegressor 0.136687517667\n",
"Lasso+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136699914659\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor 0.136719044002\n",
"Ridge+HuberRegressor+RandomForestRegressor+XGBRegressor 0.136724532338\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.136733971871\n",
"RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136746965276\n",
"RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.136814861546\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.136852154424\n",
"LinearRegression+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136864675114\n",
"ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136889184691\n",
"Ridge+ElasticNet+ExtraTreesRegressor+XGBRegressor 0.13690142747\n",
"Lasso+Ridge+TheilSenRegressor+XGBRegressor 0.136903831256\n",
"LinearRegression+Ridge+ElasticNet+RandomForestRegressor 0.136913083874\n",
"LinearRegression+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.136920673382\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.136938001497\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.136940633843\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.136947344104\n",
"Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor 0.136950323372\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136969919536\n",
"Lasso+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136975792653\n",
"Lasso+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13697703422\n",
"Lasso+Ridge+RANSACRegressor+GradientBoostingRegressor 0.136999255523\n",
"Lasso+SVR+AdaBoostRegressor+XGBRegressor 0.136999430048\n",
"Lasso+TheilSenRegressor+HuberRegressor+XGBRegressor 0.137032696593\n",
"Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.137039121114\n",
"Ridge+TheilSenRegressor+HuberRegressor+XGBRegressor 0.13704114823\n",
"Lasso+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137056559929\n",
"Ridge+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.137077654779\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.137088980815\n",
"LinearRegression+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137097856175\n",
"Lasso+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.137125356354\n",
"Ridge+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137131291529\n",
"Ridge+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137134961213\n",
"RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.137162780797\n",
"Lasso+Ridge+TheilSenRegressor+GradientBoostingRegressor 0.137179248357\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137230765921\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreesRegressor 0.137251478997\n",
"RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.137302985924\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+XGBRegressor 0.137333729141\n",
"LinearRegression+ElasticNet+SVR+XGBRegressor 0.137356528385\n",
"LinearRegression+Ridge+AdaBoostRegressor+GradientBoostingRegressor 0.137370297011\n",
"Ridge+RANSACRegressor+SVR+RandomForestRegressor 0.137378461617\n",
"TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.137378902149\n",
"LinearRegression+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137383003221\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137385741493\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.137397537694\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.137429323169\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.137433432983\n",
"LinearRegression+Ridge+AdaBoostRegressor+XGBRegressor 0.137449021026\n",
"LinearRegression+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.13747921879\n",
"LinearRegression+SVR+AdaBoostRegressor+XGBRegressor 0.13748820289\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.137516572657\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137531873261\n",
"Ridge+ElasticNet+RANSACRegressor+RandomForestRegressor 0.137534940453\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreesRegressor 0.137574571861\n",
"LinearRegression+ElasticNet+SVR+GradientBoostingRegressor 0.137583683947\n",
"LinearRegression+Ridge+TheilSenRegressor+XGBRegressor 0.137585452533\n",
"LinearRegression+RANSACRegressor+SVR+RandomForestRegressor 0.137594273529\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreesRegressor 0.137669739315\n",
"Lasso+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor 0.137730501516\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137736592407\n",
"Lasso+LinearRegression+DecisionTreeRegressor+RandomForestRegressor 0.137767318734\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor 0.137775430478\n",
"Lasso+RANSACRegressor+SVR+ExtraTreesRegressor 0.137811759245\n",
"Ridge+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.137817703913\n",
"Ridge+ElasticNet+DecisionTreeRegressor+XGBRegressor 0.137823878585\n",
"Lasso+RANSACRegressor+HuberRegressor+XGBRegressor 0.137823893766\n",
"Lasso+LinearRegression+Ridge+XGBRegressor 0.137840950755\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.137844950265\n",
"Lasso+ElasticNet+AdaBoostRegressor+XGBRegressor 0.137847075711\n",
"LinearRegression+Ridge+TheilSenRegressor+GradientBoostingRegressor 0.137870871525\n",
"LinearRegression+Ridge+SVR+ExtraTreesRegressor 0.137926986557\n",
"Ridge+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137945106284\n",
"Lasso+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.137976986868\n",
"Lasso+LinearRegression+RANSACRegressor+XGBRegressor 0.137980254275\n",
"Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor 0.137981271607\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137984093207\n",
"Lasso+Ridge+HuberRegressor+RandomForestRegressor 0.137999905724\n",
"ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138004664371\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138016126223\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor 0.13803401339\n",
"Lasso+Ridge+RANSACRegressor+RandomForestRegressor 0.138042263288\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.138076301269\n",
"TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138102404819\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+XGBRegressor 0.138129058868\n",
"Lasso+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.138133959379\n",
"Ridge+ElasticNet+TheilSenRegressor+RandomForestRegressor 0.138154753458\n",
"Lasso+LinearRegression+TheilSenRegressor+XGBRegressor 0.138169638737\n",
"Lasso+Ridge+TheilSenRegressor+RandomForestRegressor 0.138188619473\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.13819969098\n",
"Lasso+ElasticNet+DecisionTreeRegressor+RandomForestRegressor 0.13821854383\n",
"LinearRegression+Ridge+DecisionTreeRegressor+RandomForestRegressor 0.138231971328\n",
"LinearRegression+Ridge+RANSACRegressor+XGBRegressor 0.138236275249\n",
"Ridge+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138250563013\n",
"Ridge+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138251492872\n",
"TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.138265849491\n",
"TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138279619451\n",
"Lasso+LinearRegression+Ridge+GradientBoostingRegressor 0.13835853976\n",
"Ridge+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.138378314868\n",
"Lasso+SVR+DecisionTreeRegressor+RandomForestRegressor 0.138406324634\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.138453243258\n",
"Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138455502557\n",
"Lasso+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138472034454\n",
"TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138554369433\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.138554616574\n",
"Lasso+Ridge+ElasticNet+ExtraTreesRegressor 0.138555669634\n",
"Lasso+LinearRegression+RANSACRegressor+GradientBoostingRegressor 0.13857310217\n",
"TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.1385811003\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.13858948245\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.138594094591\n",
"Lasso+LinearRegression+TheilSenRegressor+GradientBoostingRegressor 0.138637901762\n",
"Lasso+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13864588189\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.138646198149\n",
"LinearRegression+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138675676957\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor 0.138680020371\n",
"LinearRegression+Ridge+RANSACRegressor+GradientBoostingRegressor 0.13868657387\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor 0.138722887774\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.138727031792\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor 0.138743739711\n",
"Ridge+RANSACRegressor+HuberRegressor+XGBRegressor 0.138785631802\n",
"LinearRegression+Ridge+TheilSenRegressor+RandomForestRegressor 0.138810876575\n",
"LinearRegression+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138836978774\n",
"Lasso+ElasticNet+SVR+RandomForestRegressor 0.138846792459\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.138861569055\n",
"Lasso+ElasticNet+HuberRegressor+XGBRegressor 0.138887264585\n",
"TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138893449651\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.138895730534\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.138898359025\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.138930150751\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor 0.138937024167\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.138960209623\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138963777202\n",
"Lasso+LinearRegression+AdaBoostRegressor+RandomForestRegressor 0.138984446487\n",
"Lasso+ElasticNet+HuberRegressor+GradientBoostingRegressor 0.138990062844\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139006870233\n",
"Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.139011238487\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.139035180792\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreesRegressor 0.139035557428\n",
"TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.139036338304\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.139038907781\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreesRegressor 0.139048941998\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor 0.139049212976\n",
"TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139063595726\n",
"Lasso+LinearRegression+Ridge+RandomForestRegressor 0.13906949252\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.139074626527\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.13907931015\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor 0.139123236122\n",
"LinearRegression+RANSACRegressor+HuberRegressor+XGBRegressor 0.139133190845\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor 0.139177669737\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.13918297822\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.139188790295\n",
"ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13923662506\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.139247386764\n",
"Ridge+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139288230159\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor 0.139312175467\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor 0.139341035336\n",
"Lasso+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13939189584\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+RandomForestRegressor 0.13942812328\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.139434248503\n",
"LinearRegression+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.139437964932\n",
"Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139457893442\n",
"Lasso+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.139461995519\n",
"RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139464018962\n",
"Lasso+LinearRegression+HuberRegressor+RandomForestRegressor 0.139469807391\n",
"Lasso+Ridge+SVR+AdaBoostRegressor 0.1394758199\n",
"TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139517288718\n",
"Lasso+SVR+ExtraTreeRegressor+XGBRegressor 0.139541511258\n",
"LinearRegression+Ridge+HuberRegressor+RandomForestRegressor 0.139558422753\n",
"Ridge+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.139563457502\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor 0.139573996842\n",
"Ridge+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139578773407\n",
"Lasso+LinearRegression+TheilSenRegressor+RandomForestRegressor 0.139581869094\n",
"Lasso+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13959000572\n",
"Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139604984401\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor 0.139613037975\n",
"ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139639087752\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.139663722256\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor 0.13968336402\n",
"LinearRegression+Ridge+RANSACRegressor+RandomForestRegressor 0.13970010103\n",
"Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.139707369065\n",
"Ridge+ElasticNet+ExtraTreesRegressor+RandomForestRegressor 0.139712809823\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor 0.139716703287\n",
"RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.139746956281\n",
"Ridge+RANSACRegressor+SVR+ExtraTreesRegressor 0.139764059428\n",
"Ridge+SVR+DecisionTreeRegressor+RandomForestRegressor 0.13976968754\n",
"LinearRegression+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139785370195\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor 0.139793811359\n",
"ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139805098173\n",
"Ridge+SVR+AdaBoostRegressor+XGBRegressor 0.139806581088\n",
"Ridge+ElasticNet+SVR+XGBRegressor 0.139807876859\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor 0.13980803299\n",
"Lasso+Ridge+AdaBoostRegressor+RandomForestRegressor 0.139810922141\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.139843825269\n",
"ElasticNet+SVR+GradientBoostingRegressor+XGBRegressor 0.139847997895\n",
"ElasticNet+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139859258835\n",
"LinearRegression+ElasticNet+HuberRegressor+XGBRegressor 0.139895318437\n",
"Lasso+LinearRegression+RANSACRegressor+RandomForestRegressor 0.139916579913\n",
"Ridge+ElasticNet+SVR+GradientBoostingRegressor 0.139948379815\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139960499114\n",
"LinearRegression+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139964905885\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.139970022509\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor 0.139994634626\n",
"LinearRegression+ElasticNet+HuberRegressor+GradientBoostingRegressor 0.140013592935\n",
"RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140018082455\n",
"SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140020409397\n",
"LinearRegression+SVR+DecisionTreeRegressor+RandomForestRegressor 0.140024305055\n",
"SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140032054033\n",
"ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140038834927\n",
"LinearRegression+ElasticNet+SVR+RandomForestRegressor 0.140041032691\n",
"Ridge+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.140054488146\n",
"Lasso+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor 0.14008140954\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140092087035\n",
"HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140092920332\n",
"SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140120143016\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.140125548676\n",
"Lasso+ElasticNet+SVR+ExtraTreesRegressor 0.140164776895\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.140165972965\n",
"LinearRegression+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.140175876681\n",
"Lasso+Ridge+HuberRegressor+ExtraTreesRegressor 0.140183736552\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor 0.140225062083\n",
"LinearRegression+SVR+ExtraTreeRegressor+XGBRegressor 0.140234433194\n",
"Lasso+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.140234870587\n",
"Lasso+ElasticNet+ExtraTreeRegressor+XGBRegressor 0.140236289345\n",
"ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor 0.14033261527\n",
"RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140335855973\n",
"ElasticNet+TheilSenRegressor+SVR+XGBRegressor 0.14033897865\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.140353843808\n",
"HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140421792172\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.140432118132\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.140450654334\n",
"ElasticNet+RANSACRegressor+SVR+XGBRegressor 0.140487481511\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.140500181035\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.140503981874\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.140510212483\n",
"Lasso+Ridge+ExtraTreeRegressor+GradientBoostingRegressor 0.140524545679\n",
"Lasso+LinearRegression+ExtraTreeRegressor+XGBRegressor 0.140535761793\n",
"Lasso+Ridge+ExtraTreeRegressor+XGBRegressor 0.14054531738\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.140570556321\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.140570616013\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.14061285408\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor 0.140616946232\n",
"LinearRegression+Ridge+AdaBoostRegressor+RandomForestRegressor 0.140617757129\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor 0.140626454099\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor 0.14065189953\n",
"Lasso+LinearRegression+ExtraTreeRegressor+GradientBoostingRegressor 0.140658597168\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.140660017956\n",
"Lasso+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140663810508\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140670462643\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.140716463256\n",
"RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.140720664716\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor 0.140723928031\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor 0.140724273253\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140753853924\n",
"ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor 0.140760283258\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+XGBRegressor 0.140778492744\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.140782479084\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor 0.140801624878\n",
"RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140851538193\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor 0.140864046471\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreesRegressor 0.140866609848\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.14088345959\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.140901365748\n",
"Ridge+ElasticNet+DecisionTreeRegressor+RandomForestRegressor 0.140921752276\n",
"RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.140934871133\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor 0.14093822806\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.140948196625\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.140980239292\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.140993193404\n",
"Lasso+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141060223355\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor 0.141091251262\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.141131514617\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor 0.14114505767\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor 0.141147217604\n",
"Lasso+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141151993822\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.14115640422\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141198585512\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.141213237322\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.14122082556\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor 0.141223875133\n",
"Ridge+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141227895485\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.141232295667\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor 0.141240123987\n",
"Lasso+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141270657043\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.141280585258\n",
"Lasso+ElasticNet+RANSACRegressor+AdaBoostRegressor 0.141281381298\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor 0.141303632525\n",
"LinearRegression+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141304454048\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreesRegressor 0.141322726514\n",
"Lasso+SVR+AdaBoostRegressor+RandomForestRegressor 0.141323205362\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor 0.141327168458\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor 0.141341197924\n",
"LinearRegression+ElasticNet+SVR+ExtraTreesRegressor 0.141347075467\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141376384159\n",
"LinearRegression+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141411264722\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor 0.141418226985\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.14143319852\n",
"LinearRegression+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141449936474\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.141457649964\n",
"Lasso+ElasticNet+AdaBoostRegressor+RandomForestRegressor 0.1414692711\n",
"Ridge+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141481419421\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.14148319921\n",
"Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.141483766292\n",
"Lasso+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.141507585746\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.141517038356\n",
"TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141528875089\n",
"LinearRegression+Ridge+ExtraTreeRegressor+XGBRegressor 0.141537377373\n",
"LinearRegression+Ridge+ExtraTreeRegressor+GradientBoostingRegressor 0.141538902897\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141558645259\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.141574297111\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.141577340579\n",
"Ridge+ElasticNet+HuberRegressor+XGBRegressor 0.14158083239\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141600379282\n",
"Ridge+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141613438084\n",
"Ridge+SVR+ExtraTreeRegressor+XGBRegressor 0.141618815607\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.141622713913\n",
"Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor 0.141622865332\n",
"RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.141632761682\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.141639456733\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.14166567483\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor 0.141684145541\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreesRegressor 0.141715300335\n",
"Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor 0.141734185755\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor 0.141744079018\n",
"TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141756366158\n",
"LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor 0.141772785694\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+RandomForestRegressor 0.141784883928\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.141793637578\n",
"TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141815171476\n",
"Lasso+LinearRegression+AdaBoostRegressor+ExtraTreesRegressor 0.141816315553\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor 0.141836580181\n",
"LinearRegression+SVR+AdaBoostRegressor+RandomForestRegressor 0.141842294707\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.141852832355\n",
"Ridge+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.141883942981\n",
"RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141890402427\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.141892740732\n",
"Ridge+ElasticNet+AdaBoostRegressor+XGBRegressor 0.141916085613\n",
"RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.141948802692\n",
"Lasso+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14198515372\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor 0.141988888811\n",
"DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142017244608\n",
"Lasso+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142031185776\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor 0.142060277517\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.142071230722\n",
"RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.142074934658\n",
"Lasso+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142088735491\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.142090041837\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142097557657\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.142103459835\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor 0.142117364473\n",
"Lasso+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.142122437914\n",
"TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.142123918391\n",
"Ridge+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.142133076838\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.142153695383\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.14216719633\n",
"ElasticNet+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.142198142083\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor 0.142199073364\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.142201411541\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.142212328686\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.142214477979\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.142227084173\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.142239851361\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR 0.142240700984\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.142275260311\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.142276528817\n",
"Ridge+ElasticNet+SVR+RandomForestRegressor 0.142293659067\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.142304112116\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor 0.142305540448\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.142312292494\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.142318754715\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor 0.142320655465\n",
"LinearRegression+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14232256871\n",
"Lasso+ElasticNet+HuberRegressor+RandomForestRegressor 0.142327450804\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+XGBRegressor 0.142329600004\n",
"ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor 0.142339063472\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor 0.142347213574\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreesRegressor 0.142363836816\n",
"ElasticNet+SVR+RandomForestRegressor+XGBRegressor 0.142395413143\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreesRegressor 0.142403414307\n",
"Lasso+Ridge+RANSACRegressor+SVR 0.142403674799\n",
"LinearRegression+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142409654648\n",
"Lasso+LinearRegression+Ridge+ExtraTreesRegressor 0.142410478486\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142428687797\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.142431637585\n",
"SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142497154655\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.142508922623\n",
"Lasso+Ridge+ElasticNet+SVR 0.14251168433\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.142544884849\n",
"Lasso+ElasticNet+TheilSenRegressor+AdaBoostRegressor 0.142578324469\n",
"Lasso+LinearRegression+ElasticNet+SVR 0.14259629325\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.142600057303\n",
"Ridge+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142607308416\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.142669698084\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR 0.142686339982\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.142710230635\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.142712877246\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.142714266206\n",
"Lasso+ElasticNet+RANSACRegressor+SVR 0.142732337423\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor 0.142756152112\n",
"LinearRegression+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.142778930138\n",
"Ridge+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.142782932744\n",
"RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14280854011\n",
"Lasso+Ridge+ExtraTreeRegressor+RandomForestRegressor 0.142819525101\n",
"Lasso+ElasticNet+ExtraTreeRegressor+RandomForestRegressor 0.142821673346\n",
"RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.142825142696\n",
"RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142839597165\n",
"RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142845198768\n",
"Lasso+SVR+ExtraTreeRegressor+RandomForestRegressor 0.142848399707\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.142852502509\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor 0.142877107342\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor 0.14289004455\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.142902590163\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.142907637514\n",
"DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.142911922166\n",
"ElasticNet+SVR+ExtraTreesRegressor+XGBRegressor 0.142934376675\n",
"Lasso+Ridge+TheilSenRegressor+SVR 0.14295460077\n",
"ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor 0.142980021432\n",
"ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142999967488\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.143077756833\n",
"ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143090127474\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor 0.143101297354\n",
"ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.143108866238\n",
"ElasticNet+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.143126331472\n",
"Lasso+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14314383691\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.143165507576\n",
"Lasso+LinearRegression+ExtraTreeRegressor+RandomForestRegressor 0.143166688446\n",
"Lasso+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143183808703\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.143203139153\n",
"Lasso+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.143205573217\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor 0.143222794399\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor 0.143228527209\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.143272432648\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.14327780144\n",
"Ridge+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143286040671\n",
"Lasso+Ridge+AdaBoostRegressor+ExtraTreesRegressor 0.143288475659\n",
"LinearRegression+ElasticNet+HuberRegressor+RandomForestRegressor 0.143308582498\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor 0.143318446902\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.14333468926\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.143339994227\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreesRegressor 0.14335975978\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143376707053\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.143407625871\n",
"TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14340900876\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.143413340963\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+RandomForestRegressor 0.143415159767\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor 0.143422025991\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor 0.143425372433\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.143469822298\n",
"Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor 0.143490862911\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR 0.143497523427\n",
"ElasticNet+RANSACRegressor+SVR+RandomForestRegressor 0.143541445934\n",
"LinearRegression+SVR+ExtraTreeRegressor+RandomForestRegressor 0.143588325179\n",
"Lasso+HuberRegressor+SVR+XGBRegressor 0.143596558081\n",
"SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.14360837424\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor 0.143609894827\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor 0.143635269169\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.143655503895\n",
"LinearRegression+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.143675213883\n",
"SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.143688183086\n",
"Ridge+ElasticNet+ExtraTreeRegressor+XGBRegressor 0.143696172156\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.143699041709\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143714893967\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.143723304433\n",
"ElasticNet+RANSACRegressor+HuberRegressor+XGBRegressor 0.143729584722\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.143734729155\n",
"SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.1437357512\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.143741483261\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor 0.143759392677\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143763816763\n",
"Lasso+HuberRegressor+SVR+GradientBoostingRegressor 0.143767349024\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor 0.143800453136\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.143804732354\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143806746295\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143813305999\n",
"Ridge+SVR+AdaBoostRegressor+RandomForestRegressor 0.143822911728\n",
"SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143825640635\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor 0.14383329494\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor 0.143856013282\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.143861939613\n",
"LinearRegression+Ridge+ExtraTreeRegressor+RandomForestRegressor 0.143863466718\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor 0.143890920102\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor 0.143897367516\n",
"ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.143901449358\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.143901739678\n",
"Lasso+LinearRegression+Ridge+SVR 0.143901988906\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor 0.143907691491\n",
"ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.143917303433\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.143919848235\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor 0.143922562698\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor 0.143923012148\n",
"SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143928915433\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor 0.143929098672\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.143950180685\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR 0.143977162007\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor 0.144011554314\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.144029594998\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor 0.144029747616\n",
"Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.144041633138\n",
"LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor 0.14404233974\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.144076442637\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor 0.144085327532\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.144098153046\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor 0.144100535195\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144117350578\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.144128734789\n",
"TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144133704225\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor 0.144139267681\n",
"Lasso+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor 0.144140019976\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.144172277022\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor 0.144191374998\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.144200715262\n",
"Ridge+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.144202199528\n",
"HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.144208690885\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144213844594\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144227165641\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.144292566088\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR 0.14429679475\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.144303133656\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor 0.144311842318\n",
"TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144322676318\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.14433011557\n",
"TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14434935484\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor 0.144352882995\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR 0.144359752091\n",
"Ridge+ElasticNet+SVR+ExtraTreesRegressor 0.144381775656\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR 0.144389781605\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.14441488033\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.144420626762\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor 0.144427803346\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.144429344944\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor 0.144432383676\n",
"Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144440605122\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.144443272714\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR 0.144460158843\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.144477515737\n",
"TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.144562404925\n",
"Lasso+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.144587432852\n",
"Lasso+Ridge+ElasticNet+HuberRegressor 0.14459574539\n",
"Ridge+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.144641272377\n",
"Ridge+SVR+ExtraTreeRegressor+RandomForestRegressor 0.144667747336\n",
"LinearRegression+Ridge+RANSACRegressor+SVR 0.144669910465\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor 0.144672357774\n",
"HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144688675324\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.144708187143\n",
"Lasso+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.144713731819\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.144717953552\n",
"HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144722804504\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.144737982414\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor 0.14475243574\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor 0.144767314337\n",
"Ridge+ElasticNet+HuberRegressor+RandomForestRegressor 0.144782087784\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor 0.144806703785\n",
"ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.144819333561\n",
"LinearRegression+HuberRegressor+SVR+XGBRegressor 0.144842613016\n",
"TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.144864620799\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.144888020744\n",
"Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144901366668\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor 0.144902833073\n",
"Lasso+LinearRegression+RANSACRegressor+SVR 0.144928967807\n",
"Ridge+HuberRegressor+SVR+XGBRegressor 0.144929345773\n",
"LinearRegression+Ridge+ElasticNet+SVR 0.144933666399\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor 0.144936353448\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.144942018213\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor 0.144979291541\n",
"LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor 0.145004032277\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.145005046698\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.145016252921\n",
"LinearRegression+HuberRegressor+SVR+GradientBoostingRegressor 0.145019854451\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.145026913371\n",
"Ridge+HuberRegressor+SVR+GradientBoostingRegressor 0.145037569579\n",
"TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.145074679379\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor 0.145078580059\n",
"Ridge+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.145088897916\n",
"AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145106258598\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor 0.145136322961\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.145150602069\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR 0.145161607847\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.145178333134\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor 0.145200361628\n",
"Ridge+ElasticNet+RANSACRegressor+SVR 0.145201441657\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.145216736228\n",
"Ridge+ElasticNet+AdaBoostRegressor+RandomForestRegressor 0.145218509071\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor 0.145275836664\n",
"LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.145275875955\n",
"RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.145311362889\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.145313241795\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.145328066119\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor 0.145349264019\n",
"RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145354484373\n",
"Lasso+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor 0.145373645207\n",
"RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145374861078\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor 0.14537488432\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor 0.145390344272\n",
"Ridge+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145396578474\n",
"RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145402584422\n",
"ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14540738095\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145426623559\n",
"Ridge+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145436469124\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor 0.145448875462\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor 0.145464750986\n",
"Lasso+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145482404614\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.145485143316\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor 0.145496208048\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.145506782433\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor 0.14551018992\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.145520980694\n",
"ElasticNet+HuberRegressor+RandomForestRegressor+XGBRegressor 0.145525180439\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.145553057867\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.145598962497\n",
"Lasso+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145600182665\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.145612416595\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.145612858361\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145640042741\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.145662387064\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.145669929127\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.15004505615\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR 0.150059892654\n",
"Ridge+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.150079129995\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor 0.150101216443\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor 0.150144807345\n",
"ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.150157722561\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.150179245207\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.150210973165\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor 0.15021295161\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.150253292452\n",
"RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.150260672809\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor 0.150266084619\n",
"ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.150283116377\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.150315693032\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.150332519248\n",
"RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.150342303544\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor 0.150343958601\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.150345807684\n",
"ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.150366766492\n",
"TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.150382887399\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor 0.150385775844\n",
"ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.150424726812\n",
"RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.150433756883\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.150439481685\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.150461244495\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.150482064535\n",
"HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.150491363472\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.150505164432\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.150517386868\n",
"RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.150524094879\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor 0.150536533133\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.150570892122\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor 0.150584467566\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR 0.150600145901\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.150630071296\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor 0.150641301591\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.150643252632\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor 0.150659377258\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor 0.150675023735\n",
"HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.150730690444\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.150766342296\n",
"Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.150782526128\n",
"TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.150905580496\n",
"HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.150957952293\n",
"Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor 0.150991278551\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.15099544778\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.151040815016\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.151093508016\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor 0.151111991846\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.151129920793\n",
"TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.151135282356\n",
"Lasso+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.151135474242\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.151160427309\n",
"HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.151199593206\n",
"HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.151207192339\n",
"AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.151208224555\n",
"HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.151243948441\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.151267197208\n",
"LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.151319464751\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.151365884241\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor 0.151382686119\n",
"AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.151410942594\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.151440626952\n",
"Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.151479110708\n",
"ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.151482474229\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.15153879457\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.151568760392\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.151575343903\n",
"HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.151588831525\n",
"ElasticNet+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.151629367532\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor 0.151666859235\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.151667932053\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.151706271957\n",
"HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.151766302944\n",
"Lasso+HuberRegressor+SVR+AdaBoostRegressor 0.151773202548\n",
"ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.151825040098\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor 0.151863993685\n",
"HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.151864522101\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor 0.151900667406\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR 0.151901027128\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.15192780097\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR 0.151944000652\n",
"HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.15195850587\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.151978874483\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.151989624545\n",
"SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152024294169\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.15212281146\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.152147241425\n",
"RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.152217398469\n",
"Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.152306358046\n",
"ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor 0.152351082027\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.152404657412\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.15242120437\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor 0.15245563116\n",
"LinearRegression+HuberRegressor+SVR+AdaBoostRegressor 0.152483214465\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.152498495616\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor 0.152598537525\n",
"ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.152652132836\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR 0.152701146934\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.152702618722\n",
"HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.152754331116\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.152784312881\n",
"HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.152828822893\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor 0.152864404344\n",
"ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor 0.15289755958\n",
"TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.15292505144\n",
"SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152964571172\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.15299751047\n",
"ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.153028167268\n",
"ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.153032478982\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.153106739861\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.15311515872\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.153133361716\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.153150514986\n",
"DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.15318588745\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.153187848134\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.153228510964\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.153322835469\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.153355805771\n",
"DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.153412781385\n",
"Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor 0.153438470334\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor 0.153499544066\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.153530102756\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.153585186863\n",
"Ridge+HuberRegressor+SVR+AdaBoostRegressor 0.153586859675\n",
"Ridge+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.153591720536\n",
"TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.153638004237\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR 0.153639515235\n",
"RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.153710070685\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.153755371709\n",
"ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.153798180464\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.153805459717\n",
"HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.153828132419\n",
"DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.153861719834\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.153882516557\n",
"DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.153944577655\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.153986558657\n",
"ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.153988771576\n",
"HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.154001438946\n",
"Lasso+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.15408063712\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.154116229386\n",
"LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.154211456677\n",
"RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.154344906906\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.154346648249\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.154443354218\n",
"DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.154450593442\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.154479579432\n",
"DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.154503185138\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor 0.154673629847\n",
"RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.154699998186\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.154792998905\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.15479602439\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.15489169402\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.154967540467\n",
"ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.15498390403\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.155025409589\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.155053658587\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.155060156636\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.155072974187\n",
"Lasso+ElasticNet+HuberRegressor+SVR 0.155107939257\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor 0.155173472417\n",
"SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.155212395219\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.155249083532\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.155412416345\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.155531228786\n",
"DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.15555160463\n",
"DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.155561229069\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor 0.155570045785\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.155572802158\n",
"HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.155576661484\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor 0.155589271417\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor 0.155603811999\n",
"HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.155627116291\n",
"HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.15566730278\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.15584271542\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.155846632349\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.155875808271\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.156112620171\n",
"ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.156118055598\n",
"SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.156158834257\n",
"ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.156356799394\n",
"ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.156473579426\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR 0.15652090747\n",
"TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.156562298314\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.156573744845\n",
"HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.156640036602\n",
"ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.156680773331\n",
"HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.15689108854\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.156955388525\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.157013302673\n",
"Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.15706360146\n",
"HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.15707472605\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.15719997626\n",
"ElasticNet+HuberRegressor+SVR+XGBRegressor 0.157203431822\n",
"RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.157222634004\n",
"ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor 0.157241206763\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.157297711869\n",
"ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.157494732705\n",
"Ridge+ElasticNet+HuberRegressor+SVR 0.157641544497\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.157674545976\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.157687050249\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.15777944015\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.15785586277\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.157936505501\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.157951255546\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.158038481495\n",
"HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.158119496728\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR 0.158286506301\n",
"ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.158479109959\n",
"RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.158513762885\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.158619088481\n",
"ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.158675337949\n",
"HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.158938310513\n",
"DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.158963503581\n",
"ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.158975348584\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.159058695559\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.159101229532\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.159186365721\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.159644514088\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.159713595828\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.159870227935\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.160248601007\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.160482748109\n",
"DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.160558318778\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor 0.16058399318\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.160638500739\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.160687336289\n",
"HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.160912631099\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.160993987054\n",
"ElasticNet+HuberRegressor+SVR+RandomForestRegressor 0.161105291149\n",
"HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.161240549939\n",
"HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.161265057845\n",
"HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.161300043565\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.161304045547\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.161350873097\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR 0.161671031691\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.162015583402\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.162759772745\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor 0.162994718234\n",
"HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.163043070457\n",
"ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1632329786\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.16371773448\n",
"ElasticNet+HuberRegressor+SVR+AdaBoostRegressor 0.164280771655\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.164317579818\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.165264011775\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.165729677747\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.165981317969\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor 0.16613644967\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.167202943671\n",
"\n",
"Model Amount : 5\n",
"Lasso+LinearRegression+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.126491410988\n",
"Lasso+Ridge+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127066224745\n",
"Lasso+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127754407536\n",
"Lasso+LinearRegression+SVR+GradientBoostingRegressor+XGBRegressor 0.127759238795\n",
"LinearRegression+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127774882914\n",
"Lasso+Ridge+SVR+GradientBoostingRegressor+XGBRegressor 0.127827254158\n",
"LinearRegression+Ridge+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127893921197\n",
"Lasso+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128068400055\n",
"Lasso+LinearRegression+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.128273155131\n",
"Lasso+LinearRegression+ElasticNet+GradientBoostingRegressor+XGBRegressor 0.128635730482\n",
"LinearRegression+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128683062887\n",
"Lasso+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128744503621\n",
"Ridge+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128934051405\n",
"Lasso+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.128999865946\n",
"Lasso+ElasticNet+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12900717217\n",
"LinearRegression+Ridge+SVR+GradientBoostingRegressor+XGBRegressor 0.129026664539\n",
"Lasso+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.129190495603\n",
"LinearRegression+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.129230528599\n",
"TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129279484079\n",
"Lasso+Ridge+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.12937224753\n",
"Lasso+Ridge+SVR+RandomForestRegressor+XGBRegressor 0.12937232061\n",
"LinearRegression+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129379950822\n",
"Lasso+ElasticNet+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.129388787253\n",
"Lasso+LinearRegression+SVR+RandomForestRegressor+XGBRegressor 0.129390799847\n",
"LinearRegression+ElasticNet+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129478157123\n",
"Lasso+Ridge+SVR+GradientBoostingRegressor+RandomForestRegressor 0.129487774185\n",
"Lasso+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129564040746\n",
"LinearRegression+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129564166372\n",
"Lasso+LinearRegression+SVR+GradientBoostingRegressor+RandomForestRegressor 0.129592254507\n",
"Lasso+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129628210218\n",
"Ridge+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129725507909\n",
"Lasso+Ridge+ElasticNet+GradientBoostingRegressor+XGBRegressor 0.129746913872\n",
"Lasso+LinearRegression+ElasticNet+RandomForestRegressor+XGBRegressor 0.129767635838\n",
"Lasso+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129828476189\n",
"Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.129927676594\n",
"Lasso+LinearRegression+ElasticNet+GradientBoostingRegressor+RandomForestRegressor 0.129931820634\n",
"Lasso+Ridge+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130021720248\n",
"Lasso+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130044975279\n",
"LinearRegression+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.130069495807\n",
"LinearRegression+ElasticNet+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130078888333\n",
"LinearRegression+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130112328757\n",
"Lasso+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.130117721882\n",
"Lasso+LinearRegression+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130131459286\n",
"LinearRegression+Ridge+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130166965\n",
"Lasso+LinearRegression+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.130200640092\n",
"LinearRegression+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130238238668\n",
"Lasso+LinearRegression+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.130339935629\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130365059354\n",
"LinearRegression+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130399367255\n",
"Lasso+Ridge+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.130412542832\n",
"TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130426730774\n",
"Lasso+Ridge+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130473913303\n",
"Lasso+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130533005311\n",
"LinearRegression+Ridge+SVR+RandomForestRegressor+XGBRegressor 0.130536561883\n",
"Lasso+LinearRegression+SVR+ExtraTreesRegressor+XGBRegressor 0.130543870227\n",
"Lasso+Ridge+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.130547300036\n",
"Lasso+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130548370687\n",
"Lasso+LinearRegression+Ridge+GradientBoostingRegressor+XGBRegressor 0.130588031229\n",
"Ridge+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130634389898\n",
"Lasso+LinearRegression+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.130648395012\n",
"Lasso+LinearRegression+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130655062254\n",
"LinearRegression+Ridge+SVR+GradientBoostingRegressor+RandomForestRegressor 0.130660560351\n",
"Lasso+LinearRegression+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.130711736556\n",
"Lasso+ElasticNet+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.130733517646\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130748901204\n",
"Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130755729385\n",
"Lasso+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.130757863406\n",
"Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130770707727\n",
"Lasso+Ridge+ElasticNet+RandomForestRegressor+XGBRegressor 0.130788933032\n",
"LinearRegression+Ridge+ElasticNet+GradientBoostingRegressor+XGBRegressor 0.130803685706\n",
"Lasso+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.130814160737\n",
"Lasso+LinearRegression+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.130834862931\n",
"Lasso+Ridge+ElasticNet+GradientBoostingRegressor+RandomForestRegressor 0.130862006387\n",
"LinearRegression+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130887229515\n",
"Lasso+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.130908603981\n",
"LinearRegression+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.130948533318\n",
"LinearRegression+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130987039594\n",
"LinearRegression+Ridge+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.131004338308\n",
"Lasso+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131007441333\n",
"LinearRegression+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131012148841\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131036146465\n",
"Lasso+Ridge+SVR+ExtraTreesRegressor+XGBRegressor 0.131079570426\n",
"Lasso+Ridge+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131171404419\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13117663366\n",
"LinearRegression+Ridge+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131186050441\n",
"Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13119957634\n",
"Lasso+Ridge+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131219883253\n",
"Lasso+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131237970407\n",
"Lasso+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.13127677331\n",
"Lasso+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131287285602\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131290601206\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+XGBRegressor 0.131297571228\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.131318947608\n",
"Lasso+LinearRegression+Ridge+RandomForestRegressor+XGBRegressor 0.131324669703\n",
"Lasso+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.131335213516\n",
"LinearRegression+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131344432628\n",
"Lasso+Ridge+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.131364173668\n",
"LinearRegression+ElasticNet+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.1314021506\n",
"Lasso+ElasticNet+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.131410581261\n",
"TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131418767522\n",
"LinearRegression+Ridge+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.131423839689\n",
"Lasso+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131424581912\n",
"Lasso+Ridge+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.131480131608\n",
"Lasso+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131480830942\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131503437113\n",
"LinearRegression+Ridge+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.131529371571\n",
"Lasso+LinearRegression+Ridge+GradientBoostingRegressor+RandomForestRegressor 0.13153705424\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreesRegressor+XGBRegressor 0.131544681506\n",
"Lasso+Ridge+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131550704789\n",
"LinearRegression+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131578449325\n",
"LinearRegression+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131581930559\n",
"Lasso+Ridge+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131585945233\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.131592698057\n",
"Lasso+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131592857621\n",
"Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131593085389\n",
"Ridge+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131595933429\n",
"LinearRegression+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131607305049\n",
"Lasso+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131615145155\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.131628065066\n",
"Lasso+LinearRegression+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131652977183\n",
"Lasso+LinearRegression+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.131670281072\n",
"Lasso+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.131722275021\n",
"Lasso+LinearRegression+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.131738455591\n",
"Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.131755648378\n",
"ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131757927371\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131775475423\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor 0.131785580921\n",
"Ridge+ElasticNet+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131815084566\n",
"LinearRegression+Ridge+ElasticNet+RandomForestRegressor+XGBRegressor 0.131817666486\n",
"Lasso+LinearRegression+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131838373415\n",
"Lasso+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131853001324\n",
"Ridge+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.131897136366\n",
"LinearRegression+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131900016695\n",
"LinearRegression+Ridge+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.131902695049\n",
"Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.131903344099\n",
"LinearRegression+Ridge+ElasticNet+GradientBoostingRegressor+RandomForestRegressor 0.131905101685\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.131915807709\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.131928982534\n",
"Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131944939936\n",
"LinearRegression+Ridge+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131954866935\n",
"Lasso+Ridge+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131957301836\n",
"Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131979614846\n",
"LinearRegression+Ridge+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131980886443\n",
"Lasso+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131982680762\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132010176735\n",
"LinearRegression+Ridge+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132011199092\n",
"Lasso+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132032448287\n",
"Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132037937865\n",
"Lasso+LinearRegression+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13204337302\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+XGBRegressor 0.132046157378\n",
"Lasso+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132076473977\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132077862508\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132090351031\n",
"Lasso+ElasticNet+SVR+GradientBoostingRegressor+XGBRegressor 0.132110699962\n",
"ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132112885467\n",
"Lasso+Ridge+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132117990717\n",
"LinearRegression+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132130943992\n",
"Lasso+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132160630245\n",
"Lasso+LinearRegression+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132184404636\n",
"Lasso+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132205279782\n",
"LinearRegression+Ridge+SVR+ExtraTreesRegressor+XGBRegressor 0.132217050921\n",
"TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132217092539\n",
"Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132234500336\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132259160347\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.132261649307\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132293542418\n",
"LinearRegression+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132295378266\n",
"Lasso+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132324962217\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132327577703\n",
"Lasso+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132335915391\n",
"TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132371756598\n",
"Ridge+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132373313384\n",
"LinearRegression+Ridge+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.132404502366\n",
"LinearRegression+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132419731259\n",
"LinearRegression+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132421872905\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132425037513\n",
"Lasso+LinearRegression+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1324285656\n",
"Lasso+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132457892359\n",
"TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.13245925239\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.13248320711\n",
"Lasso+Ridge+HuberRegressor+RandomForestRegressor+XGBRegressor 0.132526972161\n",
"Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132544493351\n",
"Lasso+Ridge+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132574701371\n",
"LinearRegression+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132600683461\n",
"LinearRegression+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132609392246\n",
"LinearRegression+Ridge+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132626311966\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132626513219\n",
"Lasso+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132643208258\n",
"Lasso+ElasticNet+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132650473635\n",
"RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132682114106\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132691649112\n",
"Lasso+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132694796897\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132711958789\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132748854723\n",
"Lasso+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132769441777\n",
"LinearRegression+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132798753981\n",
"Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132801049597\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+XGBRegressor 0.132835896231\n",
"Lasso+LinearRegression+HuberRegressor+RandomForestRegressor+XGBRegressor 0.132854359098\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+XGBRegressor 0.132862371522\n",
"Ridge+ElasticNet+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.132889058559\n",
"LinearRegression+ElasticNet+SVR+GradientBoostingRegressor+XGBRegressor 0.132893545279\n",
"LinearRegression+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132925475209\n",
"TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132926652287\n",
"Lasso+Ridge+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132941887539\n",
"Lasso+LinearRegression+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132978222914\n",
"Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133006379008\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.133012975833\n",
"Ridge+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133041675939\n",
"Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133061391421\n",
"Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133068306271\n",
"Lasso+Ridge+ElasticNet+ExtraTreesRegressor+XGBRegressor 0.133069202845\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.133081390674\n",
"LinearRegression+ElasticNet+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133091173578\n",
"LinearRegression+Ridge+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133094929386\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133098124947\n",
"Ridge+ElasticNet+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.133106427476\n",
"Ridge+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133113670388\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.133119444731\n",
"LinearRegression+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133144102602\n",
"LinearRegression+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133146792935\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133158654206\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+XGBRegressor 0.13316478768\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.133174123974\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor 0.13317759395\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133185166713\n",
"ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133188762405\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133199737324\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133202300325\n",
"Lasso+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor 0.133211957546\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133215952849\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.133245509978\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133249070203\n",
"LinearRegression+Ridge+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133259367519\n",
"LinearRegression+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133272510306\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133281352602\n",
"Lasso+LinearRegression+SVR+ExtraTreesRegressor+RandomForestRegressor 0.133282560398\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.133288862563\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133309785678\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133313552215\n",
"LinearRegression+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133320784128\n",
"Lasso+Ridge+RANSACRegressor+SVR+XGBRegressor 0.133323846311\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133331500638\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133337670829\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133352189536\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133356495949\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.133379215875\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor 0.133415274612\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+XGBRegressor 0.133422875972\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133431591425\n",
"LinearRegression+Ridge+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133433477962\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13343940512\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133464023826\n",
"Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133464877472\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133474791411\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133475701447\n",
"Ridge+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133499342148\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.133500804508\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.133511672294\n",
"LinearRegression+Ridge+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133515346061\n",
"Lasso+LinearRegression+Ridge+SVR+XGBRegressor 0.133534378089\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.13354024863\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.133547528724\n",
"RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13355042706\n",
"LinearRegression+Ridge+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133569753462\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.133571807489\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133573818268\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133589255933\n",
"ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133598146685\n",
"Lasso+Ridge+TheilSenRegressor+SVR+XGBRegressor 0.133623887908\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133634404415\n",
"Lasso+Ridge+SVR+ExtraTreesRegressor+RandomForestRegressor 0.133635101904\n",
"Lasso+Ridge+RANSACRegressor+SVR+GradientBoostingRegressor 0.133644867936\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.133658940517\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133669523016\n",
"Lasso+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133691600823\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.133693698912\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133694802366\n",
"Lasso+LinearRegression+Ridge+ExtraTreesRegressor+XGBRegressor 0.133728126516\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.13373091838\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133735595218\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133740766703\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133746592974\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133750413864\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreesRegressor+RandomForestRegressor 0.133754625421\n",
"Lasso+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133756520055\n",
"Ridge+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133763182343\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.133770004583\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133778748571\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133793873561\n",
"Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133818778672\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.133820216399\n",
"Lasso+Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor 0.13383333222\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133844687289\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133862387961\n",
"Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133874811334\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133890243908\n",
"Lasso+LinearRegression+Ridge+SVR+GradientBoostingRegressor 0.133894323553\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133900584565\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.133910865768\n",
"Lasso+ElasticNet+SVR+RandomForestRegressor+XGBRegressor 0.133934819981\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.133950508946\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133972925837\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133976405138\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.13398219449\n",
"Lasso+LinearRegression+Ridge+ExtraTreesRegressor+GradientBoostingRegressor 0.133982871523\n",
"Lasso+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133985300842\n",
"Lasso+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133992618931\n",
"Lasso+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133998938757\n",
"Lasso+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor 0.134005046524\n",
"Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.134017258283\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134018684374\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.134019188809\n",
"Lasso+Ridge+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134019242054\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134037422961\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134052754634\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.134064736151\n",
"LinearRegression+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134068478129\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134079429439\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+XGBRegressor 0.134080759034\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.134082560085\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134082828492\n",
"RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134087722018\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134093827198\n",
"Lasso+LinearRegression+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13409844117\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134099884644\n",
"Lasso+LinearRegression+ElasticNet+SVR+XGBRegressor 0.134103351345\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134107078774\n",
"Lasso+LinearRegression+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134118268912\n",
"Lasso+ElasticNet+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.134132250918\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+XGBRegressor 0.134135639092\n",
"Lasso+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134152907511\n",
"Lasso+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134153049278\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+XGBRegressor 0.134162782305\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13418376725\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.134184434616\n",
"Lasso+ElasticNet+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134225099671\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor 0.134237129245\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+XGBRegressor 0.134237432667\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134247526712\n",
"Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.134248741085\n",
"Lasso+LinearRegression+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134292296316\n",
"Ridge+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134292412677\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.134294401479\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.134296090454\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+XGBRegressor 0.134319165447\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134324137361\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+XGBRegressor 0.134326721687\n",
"Lasso+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134327095058\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.134334379224\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+XGBRegressor 0.134347045953\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.13434890305\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+XGBRegressor 0.134350412985\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.13435267556\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.134352994063\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.134359054628\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+XGBRegressor 0.134360125278\n",
"Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13436347095\n",
"Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13437214915\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.134375874625\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor 0.134389253889\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134391607935\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134393508161\n",
"Lasso+LinearRegression+ElasticNet+SVR+GradientBoostingRegressor 0.134412072887\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134417030254\n",
"Ridge+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.13442311594\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+XGBRegressor 0.134423235919\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.134457315985\n",
"TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13445965754\n",
"Ridge+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134460108477\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134463885038\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134489022413\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13449161897\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+XGBRegressor 0.134495362307\n",
"Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134517403643\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.134522441021\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+GradientBoostingRegressor 0.134532774605\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor 0.134535974331\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.134544782068\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.13454496268\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13459270866\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134593569967\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+XGBRegressor 0.134593739328\n",
"Lasso+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134604344753\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.134609425844\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134614434\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+GradientBoostingRegressor 0.134639297956\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134659460964\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.13466370064\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134666639002\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134668591229\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13468067492\n",
"LinearRegression+ElasticNet+SVR+RandomForestRegressor+XGBRegressor 0.134681442037\n",
"Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134682816657\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134683370989\n",
"Lasso+Ridge+ElasticNet+SVR+XGBRegressor 0.134692551906\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134716483061\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.134723402022\n",
"Lasso+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.134726319753\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.1347272215\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+XGBRegressor 0.134736167287\n",
"LinearRegression+Ridge+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134745140727\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.134753444313\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor 0.13475385724\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134761951362\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+GradientBoostingRegressor 0.134763194363\n",
"LinearRegression+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor 0.134764523011\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134764624689\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134770538507\n",
"Lasso+ElasticNet+SVR+ExtraTreesRegressor+XGBRegressor 0.134772718711\n",
"LinearRegression+ElasticNet+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.134777713894\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134780285507\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134783413246\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+GradientBoostingRegressor 0.134787550332\n",
"TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134833948008\n",
"TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134855282174\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134863896801\n",
"Ridge+ElasticNet+SVR+GradientBoostingRegressor+XGBRegressor 0.134865406927\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+XGBRegressor 0.13486651062\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134872980023\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor 0.134876933057\n",
"Lasso+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134881364672\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134882318748\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134882401989\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134896448844\n",
"Lasso+Ridge+ElasticNet+SVR+GradientBoostingRegressor 0.134909609667\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134922914803\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13492564637\n",
"Lasso+Ridge+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134926159471\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134933105825\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134957549773\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134958542408\n",
"LinearRegression+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134961606051\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134966061186\n",
"Lasso+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.134967010516\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134968430309\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.134975798179\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134976654722\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134976722721\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.134976840117\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134997649147\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135009140806\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+XGBRegressor 0.135014778049\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.135036397883\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.135040099983\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+GradientBoostingRegressor 0.135054339664\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135058660011\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13505959197\n",
"TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135061383844\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135066776139\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.13506906723\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.135074084949\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.135074419719\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135076397658\n",
"Lasso+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135080977406\n",
"Lasso+Ridge+ElasticNet+ExtraTreesRegressor+RandomForestRegressor 0.135086307304\n",
"Lasso+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.135089697286\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.135091948873\n",
"ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.135092861462\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.135093854944\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.135095406295\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135105326711\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135110038973\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135116315227\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.13512015666\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135132027877\n",
"LinearRegression+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135132756491\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor 0.135139009449\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135140861755\n",
"Lasso+LinearRegression+Ridge+SVR+RandomForestRegressor 0.135147109304\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.13515333066\n",
"Lasso+Ridge+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135157263381\n",
"TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1351690878\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135180112968\n",
"Lasso+Ridge+RANSACRegressor+SVR+RandomForestRegressor 0.135181559647\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.135198356716\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.135224804018\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135226357427\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135247989439\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135251336068\n",
"Lasso+Ridge+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135254786965\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.135259567609\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135259651222\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135259955401\n",
"Lasso+Ridge+TheilSenRegressor+SVR+RandomForestRegressor 0.135266651472\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+RandomForestRegressor 0.135269030946\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135275270308\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+XGBRegressor 0.135287401732\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.135323776024\n",
"ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135326187788\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135327248588\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135331096837\n",
"Ridge+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135352595362\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135361597975\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135362077193\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135370392989\n",
"ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135379631868\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.135419409508\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+RandomForestRegressor 0.135426407945\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135437955501\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.135444664001\n",
"ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.135455947436\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13545764427\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.13546606745\n",
"Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135468438289\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135482739919\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+XGBRegressor 0.135486166357\n",
"Lasso+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135487264619\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135489869075\n",
"Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135500701944\n",
"LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+XGBRegressor 0.135510815662\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135510824094\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135511398133\n",
"LinearRegression+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135511448473\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.135512402023\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135516282975\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135517196619\n",
"LinearRegression+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.13551816487\n",
"Lasso+LinearRegression+Ridge+ElasticNet+XGBRegressor 0.135532008305\n",
"LinearRegression+Ridge+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135543947157\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135555759655\n",
"Lasso+LinearRegression+Ridge+ExtraTreesRegressor+RandomForestRegressor 0.13556500257\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.135584610161\n",
"LinearRegression+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13559738656\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+RandomForestRegressor 0.135602068107\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135618505365\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135627075554\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.135628011643\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13564968247\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135652598263\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.135662831907\n",
"LinearRegression+Ridge+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13566783037\n",
"LinearRegression+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135673516387\n",
"Ridge+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.135681010561\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135681413044\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+XGBRegressor 0.135699116821\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.135705533573\n",
"LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135716960711\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135734823474\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135737475\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+XGBRegressor 0.135750828221\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135754784337\n",
"LinearRegression+Ridge+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135755271359\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135758636992\n",
"Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135761589403\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+GradientBoostingRegressor 0.135763509406\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135765893456\n",
"ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135770973946\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.135775071751\n",
"Ridge+ElasticNet+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135775234069\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.135775793852\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.135778067811\n",
"Lasso+ElasticNet+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.135781345457\n",
"Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135782689313\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+XGBRegressor 0.135783921028\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135785788143\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+GradientBoostingRegressor 0.135792436346\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.135793160641\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135793814864\n",
"Lasso+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13579801181\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135801387158\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135808754398\n",
"Lasso+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135812485779\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.13581258584\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135831675514\n",
"Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135832378562\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.135834325972\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.135841903895\n",
"Lasso+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135846795213\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+XGBRegressor 0.135847786102\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+XGBRegressor 0.13584795607\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.13584826744\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135857271581\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+XGBRegressor 0.135858061583\n",
"Lasso+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135862106079\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135877820337\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135878184236\n",
"ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135879189311\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135879519831\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+XGBRegressor 0.135880430863\n",
"Lasso+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135881614604\n",
"Lasso+LinearRegression+Ridge+ElasticNet+GradientBoostingRegressor 0.135882342076\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.1358861667\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor 0.135897243525\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+RandomForestRegressor 0.135910091718\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.135914812903\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.135919070579\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135933326197\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+RandomForestRegressor 0.135933439068\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.135940832651\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135956854957\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135982258563\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135993028618\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135993296591\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+RandomForestRegressor 0.136004727226\n",
"Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136013380323\n",
"LinearRegression+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136016356832\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor 0.136016825809\n",
"Lasso+LinearRegression+ElasticNet+SVR+RandomForestRegressor 0.136023673832\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor 0.136039944181\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136044257904\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+XGBRegressor 0.136047637977\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+RandomForestRegressor 0.136049504604\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor 0.136049814764\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136052539365\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.136054581639\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136056442486\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.136056800182\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor 0.136070018733\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+RandomForestRegressor 0.136081909505\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136084835117\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.13608676452\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor+XGBRegressor 0.136087569611\n",
"LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.136092022953\n",
"Lasso+LinearRegression+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13609400632\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136095406541\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.136098885683\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.136101217046\n",
"Lasso+LinearRegression+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136102021604\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136105411594\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136113789613\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor 0.136123212505\n",
"RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136123586061\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+XGBRegressor 0.13613435867\n",
"LinearRegression+Ridge+ElasticNet+SVR+XGBRegressor 0.136135701414\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.136138715787\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136143004295\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136156452498\n",
"Lasso+LinearRegression+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136158238522\n",
"RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136165038619\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136171640054\n",
"Lasso+LinearRegression+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136172818012\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136173807743\n",
"RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136174637508\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.136175350833\n",
"LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136180155325\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136187684845\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+XGBRegressor 0.13618933621\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136190792176\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+RandomForestRegressor 0.136212513996\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.136222799183\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136236754242\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+XGBRegressor 0.136282640951\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136287591612\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136294671292\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+XGBRegressor 0.136298563176\n",
"Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.136298865669\n",
"LinearRegression+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136311604995\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136319092472\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136326024067\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136334034128\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136341755027\n",
"RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136354030135\n",
"Lasso+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136359362526\n",
"LinearRegression+Ridge+ElasticNet+SVR+GradientBoostingRegressor 0.136367856551\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136380945834\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136381217563\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136382922564\n",
"LinearRegression+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136386221099\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136389561782\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.136391312351\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.136394730894\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+XGBRegressor 0.136395119822\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+GradientBoostingRegressor 0.136397902732\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+GradientBoostingRegressor 0.136408455717\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.136433596447\n",
"Lasso+ElasticNet+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.136435060155\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136435248825\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.136435336115\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136439757046\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136442200235\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136445555014\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136448313004\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.136454428387\n",
"Ridge+ElasticNet+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.136459284702\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+RandomForestRegressor 0.136459646343\n",
"Lasso+Ridge+ElasticNet+SVR+RandomForestRegressor 0.136479304842\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.136483376006\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+XGBRegressor 0.13648713508\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor 0.136487861544\n",
"Lasso+ElasticNet+HuberRegressor+RandomForestRegressor+XGBRegressor 0.136500945647\n",
"Lasso+Ridge+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136506620647\n",
"Lasso+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136511949348\n",
"Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136512185659\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.136513490702\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136517221963\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+RandomForestRegressor 0.136523416951\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136528161827\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor 0.136533480409\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.136533876597\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136538044055\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+RandomForestRegressor 0.136545721661\n",
"Lasso+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136550090409\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.136550804659\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.136553588196\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+XGBRegressor 0.136554284706\n",
"Ridge+ElasticNet+SVR+RandomForestRegressor+XGBRegressor 0.136556784033\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.136571588606\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136573866142\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.136576851915\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136577872707\n",
"Lasso+Ridge+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136580333109\n",
"Ridge+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor 0.136585133399\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136588211968\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.136591620247\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.136600521519\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.136618141252\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RandomForestRegressor 0.136621249752\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor 0.136624573949\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+XGBRegressor 0.136662969541\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.13667145625\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136681756342\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.136688826494\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13669121643\n",
"Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136692696757\n",
"Lasso+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136694523157\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136697517368\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RandomForestRegressor 0.136705588284\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136712454824\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.136715257071\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+RandomForestRegressor 0.136717505174\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.136727275568\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor 0.136744253734\n",
"Lasso+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136745277358\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.136753691108\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136759520604\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136762929351\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+XGBRegressor 0.136765621255\n",
"Lasso+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136788421007\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.136796325206\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136809489988\n",
"RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13681189249\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136826132858\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.136832602398\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.136863985492\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136869332061\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136879981921\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136883996285\n",
"Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136890185034\n",
"SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136893393285\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136901981019\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136918133931\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.13691831714\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.13692212245\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.136923023036\n",
"Lasso+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.136925807264\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136934010552\n",
"ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.136936482516\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136959909652\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor 0.136963005508\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+XGBRegressor 0.136963696084\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+RandomForestRegressor 0.136970470627\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136971098189\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136977354784\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136987368136\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RandomForestRegressor 0.136990169768\n",
"ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.136991189488\n",
"Lasso+Ridge+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136997624097\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137004539785\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+XGBRegressor 0.137011423894\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor 0.137036962091\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137038980408\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.137040630669\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreesRegressor 0.137051810186\n",
"LinearRegression+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137074153373\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13707754941\n",
"ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137080009269\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+XGBRegressor 0.137080130264\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor 0.137081242333\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.137083093163\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.137083916995\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137090213102\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+RandomForestRegressor 0.137091949937\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.137099570768\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13710504235\n",
"LinearRegression+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137107951617\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137115164134\n",
"Lasso+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137116594382\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.137121262671\n",
"LinearRegression+ElasticNet+HuberRegressor+RandomForestRegressor+XGBRegressor 0.137124715512\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+RandomForestRegressor 0.13713505456\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137137691987\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137144622297\n",
"LinearRegression+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13714596133\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137148282064\n",
"LinearRegression+Ridge+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137154408438\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.137157637387\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137162070892\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137170766112\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13717927685\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.137184498814\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.137186568066\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13718893636\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137190609996\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.137195980015\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137210586786\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137213843161\n",
"LinearRegression+Ridge+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137214833021\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.137215925571\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor 0.137222905893\n",
"Lasso+ElasticNet+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137224311699\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137232433338\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor 0.137233629863\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137242758393\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreesRegressor 0.137243869078\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.137252468374\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137261885695\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.137266729234\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+XGBRegressor 0.137285076505\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137289504421\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137293764392\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.137298210895\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor 0.137311087781\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13731521024\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor 0.137317576822\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137322410277\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+XGBRegressor 0.13732727387\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137327582936\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.137327745447\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137342324595\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137346419893\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137349039236\n",
"Lasso+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137351591985\n",
"Lasso+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13735392074\n",
"Lasso+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137355103801\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+XGBRegressor 0.137365803842\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor 0.137373350633\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.137375958031\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RandomForestRegressor 0.137376836771\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137385269804\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor 0.137386404662\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137392267515\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137397367994\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137401414067\n",
"ElasticNet+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.137405456077\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137410240449\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+XGBRegressor 0.137412186681\n",
"Lasso+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137431970404\n",
"LinearRegression+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137435930206\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+XGBRegressor 0.1374418105\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.137442176758\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137448456976\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13746236968\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+XGBRegressor 0.137473459206\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.137483248863\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137490035838\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.137492923193\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137493306439\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13749400302\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor 0.137511624021\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.137511773236\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor 0.137514127095\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137515364923\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+XGBRegressor 0.137516697053\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor 0.137521149014\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.137521664982\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.137526557581\n",
"Lasso+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137534180975\n",
"ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.13753897469\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+XGBRegressor 0.137540249157\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.13754126603\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137543431263\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.13756582822\n",
"RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137567327371\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137577014893\n",
"Ridge+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137582119668\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137586305805\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.137589057895\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+XGBRegressor 0.138651112142\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138658378706\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.138661010431\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.138661658298\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.138666149721\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+RandomForestRegressor 0.138668364717\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor 0.138680896304\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.138688007187\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138690884585\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.138703061887\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.138720003313\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138726091604\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.138729703239\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.138731813593\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor 0.138734795519\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.138734809549\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138737389137\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.138750089448\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor 0.138764884631\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138767031322\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13877359229\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138798472818\n",
"Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138799444641\n",
"Lasso+LinearRegression+HuberRegressor+SVR+XGBRegressor 0.13880626401\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor 0.138811017123\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138812167032\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.138815129659\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.138823219493\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.13882588705\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138829532582\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.138829609875\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138843216332\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor 0.138844182883\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138847648096\n",
"LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.138853233371\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor 0.138855825859\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor 0.138857299302\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.138857304467\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor 0.138857664359\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor 0.138863410632\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor 0.138865977758\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138869706437\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138878290391\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138880204849\n",
"RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138883400731\n",
"Ridge+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13889133764\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138908994081\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.1389110956\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor 0.138914661778\n",
"Lasso+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138922244217\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138923817591\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138924827752\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138924902115\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138927441371\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138942988077\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.138945027015\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138952388085\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+XGBRegressor 0.138963493094\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138966222613\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138972269635\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138978541411\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138984052606\n",
"Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138986443462\n",
"LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138987023786\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor 0.138987971248\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.138999590976\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor 0.139003714661\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13901182441\n",
"Lasso+ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.139013910295\n",
"LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139017815274\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139020017754\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139023344907\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor 0.139025105209\n",
"Lasso+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139025723957\n",
"Lasso+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13902848643\n",
"Lasso+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139034628656\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor 0.139046488924\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139050042887\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor 0.139051157182\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139051931525\n",
"Lasso+LinearRegression+HuberRegressor+SVR+GradientBoostingRegressor 0.139052260122\n",
"Lasso+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139054644267\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.139055341344\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139061778908\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor 0.139065479487\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.139066898909\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.139073455358\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.139087589058\n",
"Lasso+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139088914459\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139095135389\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139105513589\n",
"Ridge+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139105976008\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor 0.139107347161\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.139111925896\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.139124569714\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.139125805105\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.139127022434\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139128500096\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139130922761\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139131776078\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139136735251\n",
"Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139148808835\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.139158305593\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139164296262\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.139170056221\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.139171359618\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor 0.13917551674\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+RandomForestRegressor 0.139185156631\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.139189961957\n",
"ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139193652278\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139199570201\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13920163648\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139216394972\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139239020099\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.139239792367\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.139246846966\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.13925200671\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139256593382\n",
"Lasso+LinearRegression+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13925793885\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13925832994\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.139267800809\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor 0.139270234651\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.139274322207\n",
"SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139276554173\n",
"SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139283826388\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor 0.139295845923\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+XGBRegressor 0.139297595637\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.13929809631\n",
"TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.139299593562\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.139302921954\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor 0.139304932032\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139306922975\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.139348069019\n",
"LinearRegression+Ridge+HuberRegressor+SVR+XGBRegressor 0.139354218231\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor 0.139357083596\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139384594537\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139390388516\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139390593115\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139393277608\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.139395056593\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+XGBRegressor 0.139395673639\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+RandomForestRegressor 0.139397248578\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139405754415\n",
"Lasso+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.139405944712\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139416649745\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor 0.139419880417\n",
"Lasso+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139422773024\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139435087892\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.139437473739\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.13943846727\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139441190131\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139451853144\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139455236247\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor 0.139459969532\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139460998821\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.139472418372\n",
"Lasso+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139475215671\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.139476664114\n",
"Lasso+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139483347428\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139483900636\n",
"LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139503164441\n",
"LinearRegression+Ridge+HuberRegressor+SVR+GradientBoostingRegressor 0.139532533631\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139535605202\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139542260484\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor 0.139558592601\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.139558938535\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139559357279\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.139561313699\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139564856962\n",
"TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139569705524\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.139593533548\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.139593707743\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.139609500509\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor 0.139612605708\n",
"LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139616001918\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139617729097\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139621809319\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139626650119\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139635151147\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.139635404416\n",
"LinearRegression+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139640774378\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor 0.139645439252\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.139646998761\n",
"LinearRegression+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13965736497\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.139661070646\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.13966206813\n",
"Lasso+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.139670684768\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+XGBRegressor 0.139672689356\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139672860461\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.1396731964\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139674549142\n",
"Lasso+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.139679004486\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139679398367\n",
"TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139682670683\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.13968393713\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.139688916776\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.139689014515\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.139690495674\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139707924862\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139710191273\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor 0.139711990734\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.1397121908\n",
"TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139719351336\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139721062462\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139722668308\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor 0.139723376211\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+RandomForestRegressor 0.139732779371\n",
"Lasso+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.139737906509\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor 0.139742255891\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.13974690275\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.139750428562\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139752241182\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139756309369\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.139757010397\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139757998026\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139764399431\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor 0.139771783447\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139774126356\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.139776178766\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139779760306\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor 0.139781497056\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139785577825\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13979393556\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.1397953791\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139812175805\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+GradientBoostingRegressor 0.139815041251\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139819925121\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139820709627\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139822739535\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.139825226114\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139828057418\n",
"LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.139843867563\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+RandomForestRegressor 0.13984492484\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor 0.139849723463\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.13985450099\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.139869045404\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.139869530576\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.139872040948\n",
"Lasso+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.139881735917\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139890588216\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor 0.139891932065\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139895533331\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor 0.139896088911\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.139897178515\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139898081164\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.139898164433\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.139899188438\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.139908525623\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.139915375596\n",
"RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139915759758\n",
"LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139924478907\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.1399430178\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139949139788\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.139949557151\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139955541754\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139963648613\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+XGBRegressor 0.139965549012\n",
"Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139966501828\n",
"Lasso+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139966894202\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor 0.1399670276\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139967219013\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.13997661841\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139980748368\n",
"Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139982801052\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.13998280615\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.139987131324\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139989924727\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139993035539\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.139996382891\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.14000607808\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140006437978\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.14001202636\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor 0.140014976242\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.140026060481\n",
"Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140027884174\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140032036011\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140034822003\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+GradientBoostingRegressor 0.140036459821\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140040652107\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140044736059\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140046441874\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.140050127283\n",
"RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140050578152\n",
"Lasso+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140059525213\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140066309794\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140071297192\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.14007518889\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor 0.140076989534\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor 0.140101697834\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.140110897838\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.140111222747\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor 0.140117491659\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.140121628063\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+XGBRegressor 0.14012417676\n",
"HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140131219441\n",
"Ridge+ElasticNet+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140134468946\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.140135619768\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.140137469565\n",
"Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140140709541\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140142675482\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140152585151\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140158368044\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140163727885\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.140164595688\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.140167834806\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.140168045679\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor 0.140178143387\n",
"Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140181402966\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140182329811\n",
"ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.14018388555\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor 0.140186952659\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140190671692\n",
"LinearRegression+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140191917397\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+RandomForestRegressor 0.140195769685\n",
"Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14019598505\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140198558483\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140199370337\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.140202240219\n",
"ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.140202416482\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor 0.140207833636\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor 0.140215936835\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.140219358847\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.140231518536\n",
"ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140240265888\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140240987034\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.140246823832\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140255234017\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.140256381853\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140257762161\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140271223107\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+GradientBoostingRegressor 0.140272666497\n",
"LinearRegression+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140274307817\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.14027504665\n",
"ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140275152795\n",
"RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140278673167\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140279488895\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140283231925\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140310971252\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.140314254735\n",
"Ridge+ElasticNet+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140316150139\n",
"Lasso+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140317281833\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140341243439\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.14034394747\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.140362215303\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.140370522895\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.14037410949\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140379988663\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140386988898\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140387242643\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140389255468\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140389551859\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140392035813\n",
"Lasso+Ridge+ElasticNet+SVR+AdaBoostRegressor 0.140393426532\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140402560668\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140405933524\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.140408810067\n",
"Lasso+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140412146341\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140412162742\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140415766801\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140424003059\n",
"LinearRegression+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140429294187\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.140430221872\n",
"LinearRegression+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.140445078165\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140452904063\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.140457086779\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.140459152603\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor 0.140459627131\n",
"Ridge+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140460369579\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140467211421\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor 0.140467764956\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.140473941674\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.140485545225\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.140490686786\n",
"ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140497927258\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140498420298\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140513864236\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.140515094943\n",
"LinearRegression+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140516367842\n",
"LinearRegression+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.14051829891\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140521396477\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor 0.140521835187\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140527652742\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.140528331038\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140529844024\n",
"Ridge+ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor 0.140532364279\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140534266336\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+XGBRegressor 0.14054717063\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140547489576\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.14055051126\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RandomForestRegressor 0.140551742285\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.14055206819\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+RandomForestRegressor 0.140554818579\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140560025421\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140570526741\n",
"Lasso+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140578450922\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.140578645405\n",
"RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140584653447\n",
"Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140586737292\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140591693911\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.140591860578\n",
"Lasso+Ridge+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140594698826\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.140595439609\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140611529171\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.140615685309\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.140618398848\n",
"Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140620890663\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.140628079873\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140628872818\n",
"Lasso+LinearRegression+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140633002128\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140635863802\n",
"RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140637637263\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.140637860556\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140641228591\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor 0.140647500744\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140649569197\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.14066209733\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140667937594\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140671609135\n",
"Lasso+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140676060114\n",
"RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.1406906022\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140692821737\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor 0.140693926984\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140700788862\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.140702376603\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140708465502\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor 0.140709860847\n",
"Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.140709991058\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14072401881\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140727549898\n",
"Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140728525514\n",
"Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140736349295\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor 0.140736361903\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor 0.140745285203\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.140745788517\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.140749189922\n",
"Lasso+Ridge+HuberRegressor+SVR+RandomForestRegressor 0.140751949783\n",
"Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.14075964645\n",
"Lasso+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140761507829\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.140768871246\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1407727927\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.140777337461\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor 0.140782426609\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.140786450214\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor 0.140790406232\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140797432106\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.140801651814\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140806760222\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.140807227422\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140816263363\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140822306814\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor 0.140828281435\n",
"Ridge+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.140833691498\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140847018272\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140848569216\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.140857918256\n",
"Ridge+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.140860116169\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.140862770921\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140865469582\n",
"LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140870509654\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor 0.140878112306\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.140888137932\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.140891549551\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140891551035\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14089465143\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.140897013785\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140897860041\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.14091102984\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140916521906\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.140919605361\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.14092059498\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140921264784\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140922007447\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.14092206589\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR 0.140926091407\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140929775926\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140930407251\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140940099181\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor 0.140944599183\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140945101858\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.140955146195\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.140959898078\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140967590742\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR 0.140970392727\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140974720625\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140982750837\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140987117554\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor 0.140987134829\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.140989252707\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140989335242\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140993520772\n",
"TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141005226964\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.141011286082\n",
"ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141015496276\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141023156459\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141035404134\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.141052368978\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141056526091\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.14106305009\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141066872127\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+RandomForestRegressor 0.141068645793\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.141073509427\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141081543821\n",
"LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141085021079\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.141085722902\n",
"ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141086370347\n",
"Lasso+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141088000797\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.14109556732\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.141113820697\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.141116233593\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.141122122415\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141124243756\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.141125265034\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.1411307747\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.141144615163\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.141144656295\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.141150497953\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor 0.141152916001\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141155603204\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.141157132697\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.14117228666\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141173880869\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.141185546641\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141188480692\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.141197370579\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor 0.141205266087\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+XGBRegressor 0.14121134268\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.141212742955\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor 0.141218381813\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.141237930347\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141244236375\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141247151858\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor 0.141247739509\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.141251092051\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.14125858063\n",
"RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141267124743\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.14128135214\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.14128281978\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.14129482145\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR 0.141314817718\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.14132817415\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141329120759\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.14133237198\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR 0.141339951032\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.141343865739\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.141343928733\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.141344253914\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141350436759\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.141352021876\n",
"Ridge+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.141358800782\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor 0.141370088017\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor 0.141376400812\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor 0.141379458812\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.141407655578\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor 0.141408521377\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141410654886\n",
"Lasso+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141414750401\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141419207062\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141419735893\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141428958035\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.141434098697\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.1414495685\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.141456848888\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141460044066\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141460084367\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141471695588\n",
"RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.141473320704\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.141474917322\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.141505796821\n",
"Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.141506503561\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141516867687\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141517397742\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141528624359\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.141531468547\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.141546015695\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141546636331\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.141562506673\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor 0.141563606648\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141564278284\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.141588661536\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141592719901\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.141601245048\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141602828545\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.141631949044\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.141633308411\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.141634347316\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.141634853968\n",
"Lasso+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141637356634\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141638870057\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141642747047\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141643933746\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.141645835201\n",
"Lasso+LinearRegression+HuberRegressor+SVR+RandomForestRegressor 0.141646519794\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.141647632616\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141648571739\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141656301211\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor 0.141658350822\n",
"LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141667684276\n",
"Lasso+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14166777912\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141669033494\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.141671259081\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141673237732\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.141674647777\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.141675935185\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.141676493939\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor 0.141681999291\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.14169004801\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreesRegressor 0.141690339563\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor 0.141693693648\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141693751654\n",
"SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141700157919\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.141706627401\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141709439983\n",
"LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141712347272\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.141712956718\n",
"Lasso+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141713268151\n",
"Lasso+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141715177469\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.141715462949\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141715655424\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14173365686\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141742618599\n",
"Lasso+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141746113732\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.141753293758\n",
"ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141753458214\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor 0.141760096685\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141760273237\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.14176171795\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.141762444159\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR 0.141777265812\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141786207027\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141786518792\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR 0.141788549376\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.14179340672\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor 0.14179954166\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141801938999\n",
"SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141806341644\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.141816043348\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.141816398669\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+RandomForestRegressor 0.141819646412\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.141828490342\n",
"Lasso+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141852134401\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.1418525374\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141866217618\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.14186840706\n",
"RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141868897207\n",
"Lasso+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14187208075\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.141878159411\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141883532568\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141893186433\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141893322013\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.141893636206\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141896211466\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.141912788401\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141928903702\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.141934701225\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.141949220501\n",
"TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141954294414\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141959988622\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141962143717\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.141962495656\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.141962936545\n",
"RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141969603219\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR 0.141984465979\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141986222162\n",
"TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.141992349072\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.141993698196\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142001960716\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.142006205217\n",
"ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.14202171568\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142029528791\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.142034745727\n",
"LinearRegression+Ridge+HuberRegressor+SVR+RandomForestRegressor 0.142034924456\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142035279176\n",
"TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.142040194605\n",
"LinearRegression+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142044358524\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.142050408078\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142051417598\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.14205164959\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.142052303465\n",
"LinearRegression+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142057315761\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142057378901\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.142058038441\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor 0.142059376577\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.142060924456\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor 0.1420630355\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.142064232366\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.142069942171\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142077523188\n",
"DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142078699326\n",
"Lasso+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142083621444\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142085911479\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142085920985\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142089168227\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.14209059712\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142091291137\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142093615679\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.142100408639\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142102082477\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142109074405\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142110221876\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor 0.142110786701\n",
"LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142110935873\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.142113516511\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.14212382022\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.142127469158\n",
"TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142132717899\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.14213707001\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142137434326\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.142139954496\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.142141589175\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142157599939\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142158317234\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.14215909893\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.142160949384\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.142167757443\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.1421739487\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142174293246\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.14218220239\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.142183834401\n",
"Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142187617448\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.142193182394\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14220277138\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142210147251\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.142210665119\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142210804187\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142218936693\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.142223251812\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.14222414958\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.142235913869\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142245456358\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142245833444\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142250157004\n",
"Lasso+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142257985061\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.142262040068\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142272333788\n",
"Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142273533047\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.142280427233\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142280760463\n",
"Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142284327396\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142293707911\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.14229428807\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.14229640633\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.142304935034\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.142305948325\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor 0.14231471758\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142315614864\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.142329595115\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR 0.142331121386\n",
"Ridge+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142333646271\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.142333746351\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.14233733466\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.142344655063\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor 0.142353609309\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14236537889\n",
"Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142375465863\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142379913895\n",
"ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142385413437\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142385731228\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.142386133084\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142388480171\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.1423906202\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142392797389\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142397843433\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.142399746078\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.142406879961\n",
"Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142408648939\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.142408829516\n",
"Ridge+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142414937766\n",
"SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.142415860673\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.142421473982\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.142421510682\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.142428890644\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142431523798\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.142432303415\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.142438035586\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142441042905\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142447882754\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.142455668901\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.142456022655\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR 0.142461346055\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142469128985\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR 0.142469546373\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+XGBRegressor 0.142475013473\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.14247845671\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.142484731569\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142490390275\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.142497055425\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.142503310515\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.142506238288\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.142506528412\n",
"Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142507228484\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.142512378667\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.142512981809\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142523235287\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.142524381154\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142556783726\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142561975534\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.142581478242\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.142584601733\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.142586883218\n",
"SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142592028817\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142593166557\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor 0.142593551155\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142604890079\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142607152289\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142611560402\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.142625200079\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142627725213\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.142635986355\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.142637845494\n",
"TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142642500724\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.142648232872\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.142654202019\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.142658490142\n",
"HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142666075697\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14267093696\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142679644148\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.142681802666\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor 0.142687401377\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.142693877561\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14270028262\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.142708330342\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.142710501032\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142715214305\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor 0.14271743867\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.142727845762\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142727891057\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.142729258518\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142730669719\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor 0.142734653965\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR 0.14273500884\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142737305749\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.142746844302\n",
"SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142766845375\n",
"Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142775534791\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142777254807\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.142787708258\n",
"RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142789802171\n",
"RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142792067063\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14279474257\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.142794884344\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142799565859\n",
"TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142803024619\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142804224627\n",
"Lasso+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14280944676\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142814301507\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.142826102941\n",
"SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.142828707101\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142845921023\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142848428889\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor 0.142848941674\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.14285891611\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142860196791\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142861676477\n",
"ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.142871391366\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142877371615\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142889855485\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14289751349\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.142898695892\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.142903696535\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.142907220639\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142908114065\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.142910193846\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.14291847591\n",
"TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142919630485\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142919971222\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor 0.144205461476\n",
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"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor 0.144223938447\n",
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"Lasso+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor 0.144339586764\n",
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"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14434933039\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144350699008\n",
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"RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.144357925301\n",
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"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.144361667853\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.144364614423\n",
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"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144381803299\n",
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"LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144400924049\n",
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"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor 0.144414624337\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.144419631299\n",
"Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.144430338239\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.144433745257\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor 0.1444447492\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144451031339\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.144457309582\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144463890254\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144483549943\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144488042599\n",
"RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.144494610122\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.144512203869\n",
"Lasso+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.144519571719\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144521814826\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.144526407669\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.144543573711\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144573453972\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.14458300838\n",
"LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.144587139644\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor 0.144592958631\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor 0.144600654669\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.144600749789\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.144603171873\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.144604703271\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144610851994\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144613684325\n",
"ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.144618277307\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.144621067266\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.144628092006\n",
"ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.144629420837\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.144647216875\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.144647234287\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.144649106956\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.14467126703\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144671277179\n",
"RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.14468529646\n",
"RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144689788228\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144695112903\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor 0.144705595482\n",
"LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144715644517\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor 0.144723103999\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR 0.144724011448\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144732424015\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.144736337151\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.144737693905\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.144741109487\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.144742687341\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144746810732\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1447494903\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.144763494387\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.144764146538\n",
"ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.144779530615\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.144779700993\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor 0.144793486914\n",
"ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.144803216605\n",
"HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.144803481421\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.144814429068\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.144830154249\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.144830396724\n",
"Ridge+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.144833689631\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.144839344013\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.144844480391\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144845526729\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.144884826167\n",
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"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.14490098279\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.144902114742\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.144908991172\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.14491000975\n",
"Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144910023818\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.144913410364\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144922203635\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor 0.144924305653\n",
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"Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.144940527421\n",
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"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.144946833091\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.144948143645\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144953987162\n",
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"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.144958530903\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144963000386\n",
"Ridge+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.144971559911\n",
"ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.144982737404\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.145003661195\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145017718754\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR 0.145049475935\n",
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"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145064602655\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145065862823\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.145080532784\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.145096026453\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor 0.145098924447\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.145118249522\n",
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"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.145123226495\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.145124883685\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.145124927888\n",
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"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145154372986\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145176480541\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor 0.145177412563\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145197824011\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor 0.145199103322\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.145200495792\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor 0.145206013792\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145217853921\n",
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"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor 0.145232102843\n",
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"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.145259604506\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145277331671\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.145285534058\n",
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"RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145289868125\n",
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"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.145294269319\n",
"SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145303413126\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145303836139\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.145308261139\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145327085803\n",
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"Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14532917132\n",
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"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.145359464173\n",
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"SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14536742372\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145367546229\n",
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"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.145379541831\n",
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"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.145404661907\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR 0.145408245254\n",
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"ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145432918918\n",
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"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR 0.145441363073\n",
"HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.145454210664\n",
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"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.145461840868\n",
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"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor 0.145472017584\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.145478636151\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.14547940669\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14548491555\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145487276028\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.145493755046\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.145495144997\n",
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"ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145515554075\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.145527989121\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.145532155144\n",
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"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145544950184\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.145551234522\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145557324873\n",
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"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.145560528274\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.145568373623\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.145571796404\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.145574417556\n",
"RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14557941079\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.145580573545\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145588935728\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14559078877\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.145592908383\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.1455946556\n",
"ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.145595048768\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.145602582395\n",
"ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.145618344739\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145627621766\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145630006879\n",
"TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145640522726\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.145642794345\n",
"TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14566131495\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145662220541\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.145665084061\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.14566830185\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.145672826174\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.145673903721\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.145679805719\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145691643906\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145694019553\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.145696660303\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145699848529\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.145717241715\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.145726138115\n",
"AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145731581893\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.145752167917\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14575817643\n",
"RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14576921682\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145776579587\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145777782165\n",
"Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145778537506\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.145780325785\n",
"Lasso+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145787523496\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR 0.145794946962\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor 0.145800022823\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.145810033491\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.145816509376\n",
"RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145826018523\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.145836993595\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor 0.145837293144\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.145842976602\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.145849508005\n",
"Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145853700329\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145859517186\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.145872405533\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.1458743641\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145897779867\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.14591738493\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.145922857762\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.145940227418\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.145941989242\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145952608786\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14596272023\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.145976115965\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.145976903403\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145977021102\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.145983407491\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.146004201309\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.146007554916\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.146023099028\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.146027965148\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146030993255\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146033591221\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.146044259623\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146044907297\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.146054675759\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146060356233\n",
"Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14606720209\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.146070013766\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.146076911849\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.146077921141\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor 0.146079049125\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146080007934\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146095112247\n",
"Lasso+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146101802878\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.146116769601\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.146128149246\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.146148828302\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146155316238\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor 0.146174517109\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.146178041664\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146195168178\n",
"Ridge+ElasticNet+HuberRegressor+SVR+XGBRegressor 0.146210453772\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146213430665\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.146214328468\n",
"ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14621583127\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14621682635\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.146248145622\n",
"HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.146249056217\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146249269258\n",
"ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.146251982207\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.146252542742\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146252690979\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.146255392682\n",
"SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146270736084\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146281392312\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146298616019\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR 0.14629956072\n",
"Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor 0.146305264038\n",
"SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146313237437\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.146316677104\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.146317784219\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.146339692147\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146347644931\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.146357400679\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146376525756\n",
"TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146384773479\n",
"Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146392966996\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.146406723556\n",
"ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.146424177371\n",
"ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.146430637628\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146438818103\n",
"Lasso+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.146439943496\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146446878748\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146453543729\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146467940424\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR 0.146478119553\n",
"LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.146488892016\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.146503389765\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.14656528086\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.146571924184\n",
"ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146573921342\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor 0.146577740763\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.146581416546\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.146596413236\n",
"Lasso+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.146601987963\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.146626738415\n",
"Ridge+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.146634590053\n",
"LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.146640834014\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146647129445\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.146653002569\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14665536029\n",
"TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.146657257063\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor 0.146673361802\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.146674018438\n",
"HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146676445439\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146677172461\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.14668139375\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.146684830028\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor 0.146691051736\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146693958378\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.146695076064\n",
"ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146699258021\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor 0.146699583267\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146732392135\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146737160583\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.146749244542\n",
"DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.146760412981\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.14676992998\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.146772464932\n",
"HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146791265803\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.146815162197\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.146816982427\n",
"Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.146821135202\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.146837747805\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.146840451889\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146840816818\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.146843014113\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146852157567\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146857402244\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.146858731395\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.146860253078\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146870018218\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.1469031359\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146905944629\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.146912453629\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.146917841964\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.146929157048\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14693262795\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR 0.146945877528\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.146966716197\n",
"Lasso+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146966890468\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.146968816332\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.146971157132\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.147045968262\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR 0.147050985396\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.147065720195\n",
"RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147083089078\n",
"Lasso+ElasticNet+HuberRegressor+SVR+RandomForestRegressor 0.14708405746\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.147095730257\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor 0.147102170797\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.147107369141\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+SVR 0.147143686738\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.147158574722\n",
"RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.147171472503\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147190918195\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor 0.147192650905\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.147204279181\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.147211008556\n",
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"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.147245397122\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147254228848\n",
"ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.147258870954\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.147278629741\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.147279452956\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.147289621968\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.147300118215\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.147310124592\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.147317027127\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147318256396\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.147322823473\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147323059827\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.147327625587\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147333497957\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.147352870013\n",
"DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.147378809063\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147390693091\n",
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]
},
{
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"text": [
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"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR 0.150015959917\n",
"ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.150026543004\n",
"ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.150045274009\n",
"HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.15005349794\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.150119578149\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.150149488702\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.150150666089\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.150154966752\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.150161839388\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.150181053503\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.150191025034\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.150217890258\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor 0.150294625205\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.15031770449\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.15033041793\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR 0.150410164625\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor 0.150438541711\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.150497693093\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.150534732012\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.150540160189\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.150577865545\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.15064055034\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.150655749175\n",
"DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.150686537113\n",
"DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.150734718202\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.150767540157\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.15078656497\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.150795079491\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.150808271374\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.150829632948\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.150836755238\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.150841581799\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.150868329657\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.150894785503\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.150930505951\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.150979429021\n",
"HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.150998255841\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.151023177954\n",
"HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.151024392742\n",
"HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.151048271663\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.151074345391\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.151097312446\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.151101227019\n",
"HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.151104933709\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.151117622191\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.151125966624\n",
"HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.151126893678\n",
"HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.151155641708\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.151192404551\n",
"ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.151202041301\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.15123982892\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.151240966926\n",
"Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.151247892322\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.151284777471\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.151306618609\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.151352495123\n",
"HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.151398708009\n",
"HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.151438251342\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.151534514249\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.151601129873\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor 0.151603743038\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.151618392275\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.151631348352\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.151639978859\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.151660834422\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.151691953767\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.151693636089\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.151724988233\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.151725266264\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor 0.151742549822\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.151753998589\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.151808720187\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.151830032199\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.151884280307\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.151902966857\n",
"TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.151907934043\n",
"RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.151927183451\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.151985145269\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.152005811718\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.152019669172\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.152033507941\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.152055848356\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.152108587754\n",
"DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.152117209891\n",
"DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.152136006916\n",
"Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152143304475\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.152156075681\n",
"Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor 0.15217193344\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.152172358464\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.152311733466\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.152350873221\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.152362885293\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.15238823826\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor 0.152455400105\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152482251591\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.152500908629\n",
"RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.152513763076\n",
"HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.152514916453\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.15252429869\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.152527576711\n",
"RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152543576744\n",
"SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152581674333\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.152598418603\n",
"HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.152598693381\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.152649027669\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.152681670788\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.15271592021\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.152727163158\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.152759877315\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.152766133093\n",
"ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152839698549\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.15285299465\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.152853144768\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.152918880399\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.152976347562\n",
"ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.152984396539\n",
"ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.153008828582\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.15304158013\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.153099968131\n",
"ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.153115667221\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.153150575721\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.153278208992\n",
"ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.153296008844\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.153400888044\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.153441605988\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor 0.153480692692\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.153553863042\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.153571514863\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.153598322662\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.153754545404\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.153855746321\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.153899255064\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.153984697364\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.154075733176\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.154131824625\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.154190139877\n",
"ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.15424707354\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.154256368412\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.154365159062\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.154454418219\n",
"ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.154457989592\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.154558372217\n",
"ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.154628133522\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.154655997018\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.154690375187\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.154795820137\n",
"HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.154807209477\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.1548120942\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.154815446283\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.154822444694\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.154868548287\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.154894461158\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.154930145925\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.154932391714\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.154984249748\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.155175198205\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.155223919763\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.155263132311\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.155567343343\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.155634113085\n",
"HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.155676015052\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.155717038386\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.155727904088\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.15595610911\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.156111994086\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.156118561539\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.156284417879\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.156546743788\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.156576249708\n",
"ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.156807793856\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.156903420377\n",
"HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.156983426316\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.156989487644\n",
"HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.157001314908\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.157025479919\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.157026930793\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.15708573351\n",
"ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.157112649085\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.157430013906\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.157538367522\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.157758314304\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.158017078608\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.158134873874\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.15863691371\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.15885126279\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.159487024896\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.15987085312\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.160154669137\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.160226779739\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.160646646673\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.160871368032\n",
"\n",
"Model Amount : 6\n",
"Lasso+LinearRegression+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127371402793\n",
"Lasso+Ridge+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127721759178\n",
"Lasso+LinearRegression+ElasticNet+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127861476483\n",
"Lasso+LinearRegression+Ridge+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128143488738\n",
"Lasso+LinearRegression+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.128256805558\n",
"Lasso+LinearRegression+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128327069923\n",
"Lasso+LinearRegression+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12847255068\n",
"LinearRegression+Ridge+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12851181326\n",
"Lasso+LinearRegression+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12851892378\n",
"Lasso+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128545119097\n",
"Lasso+Ridge+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128577553274\n",
"Lasso+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128598434351\n",
"Lasso+Ridge+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128650767908\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128741829139\n",
"LinearRegression+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128751913096\n",
"LinearRegression+Ridge+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128882694953\n",
"Lasso+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128935022145\n",
"Lasso+Ridge+ElasticNet+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128937105842\n",
"Lasso+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128977506999\n",
"Lasso+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.128995105057\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.129096694129\n",
"Lasso+LinearRegression+Ridge+SVR+GradientBoostingRegressor+XGBRegressor 0.129159982646\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129190096045\n",
"LinearRegression+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129230829787\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.129236432577\n",
"Lasso+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129249826764\n",
"Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129318754128\n",
"LinearRegression+Ridge+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129358477635\n",
"LinearRegression+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129433496811\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129480244112\n",
"Lasso+Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.129491091578\n",
"Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129518347531\n",
"Lasso+Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.129536823906\n",
"LinearRegression+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12953687709\n",
"Lasso+Ridge+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129557671271\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.129559033942\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.129619688634\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129622499716\n",
"LinearRegression+Ridge+ElasticNet+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129630265028\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.129640816652\n",
"Lasso+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129766919891\n",
"LinearRegression+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129766980958\n",
"LinearRegression+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129768854565\n",
"Lasso+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129816566235\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129828468789\n",
"Lasso+LinearRegression+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129843033551\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129883994942\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.129921536845\n",
"Lasso+LinearRegression+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129928286123\n",
"Lasso+LinearRegression+Ridge+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129958834483\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130019237024\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130020051543\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130035065586\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130050926818\n",
"Lasso+Ridge+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130056408648\n",
"Lasso+Ridge+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130058008917\n",
"LinearRegression+Ridge+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130096458254\n",
"Lasso+LinearRegression+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13011932158\n",
"Lasso+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130124236757\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.1301374406\n",
"TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130154974458\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130194553446\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130196885896\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130200640792\n",
"Lasso+LinearRegression+Ridge+SVR+RandomForestRegressor+XGBRegressor 0.130218456467\n",
"Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130230980829\n",
"Lasso+LinearRegression+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.130279107286\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130281092908\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130284911784\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130296880918\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130327180939\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130352963516\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130394236497\n",
"Lasso+LinearRegression+Ridge+SVR+GradientBoostingRegressor+RandomForestRegressor 0.130401543646\n",
"Lasso+LinearRegression+ElasticNet+SVR+GradientBoostingRegressor+XGBRegressor 0.130415662272\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130424691017\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130434108815\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130445624378\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130500324803\n",
"LinearRegression+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130506909711\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130542404427\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130542531787\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130565014267\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130606632312\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.130623348647\n",
"Lasso+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130623550796\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130627714062\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.130664779506\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130685927516\n",
"Lasso+Ridge+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.130704432324\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130704488164\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130709732051\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130716431754\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.130716857542\n",
"Lasso+Ridge+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.130719014284\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130721232534\n",
"LinearRegression+Ridge+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130737712477\n",
"Lasso+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130747113672\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130751592722\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.130762568821\n",
"Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130773311139\n",
"Lasso+Ridge+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.130780632452\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130790960919\n",
"Lasso+LinearRegression+Ridge+ElasticNet+GradientBoostingRegressor+XGBRegressor 0.130792389864\n",
"Lasso+Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.130803623573\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130806368651\n",
"LinearRegression+Ridge+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130825062826\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130825913574\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13083548807\n",
"Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13083905497\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.130840802545\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130849466193\n",
"Lasso+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.130881124708\n",
"Lasso+Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.130888632856\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.130940219492\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130941219097\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.130985988385\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.130991244391\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131002275769\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131004129449\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131016004401\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.131039745763\n",
"Lasso+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131039759696\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131113476808\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131113956844\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.131114371627\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131117221886\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131131750074\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131149283358\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131153584394\n",
"LinearRegression+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13115696252\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131162503452\n",
"Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131168092747\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131176531841\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.13117653191\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131184175344\n",
"Lasso+Ridge+ElasticNet+SVR+GradientBoostingRegressor+XGBRegressor 0.13118450267\n",
"Lasso+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131186315846\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13119678752\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131204303823\n",
"Lasso+LinearRegression+Ridge+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131231586448\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131245696345\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131253313121\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131274648556\n",
"Lasso+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131278449559\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131303564723\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.13130456063\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131305667611\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131317544077\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131319622741\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.131328256087\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131337914777\n",
"Lasso+LinearRegression+Ridge+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131358734243\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13135975567\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131377078435\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131379283234\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.131387199528\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13140631497\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131407813947\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131412154818\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.131418376128\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131422288008\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131428233001\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.131433228682\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.13145636812\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131456714873\n",
"Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131462651586\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131467255164\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131475732433\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131482205371\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RandomForestRegressor+XGBRegressor 0.131494607186\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.131495755182\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131519752023\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.13152266417\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131530689614\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131531026821\n",
"LinearRegression+Ridge+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131535071336\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.13153741102\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131554483304\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131561212081\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131565313396\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13157498591\n",
"LinearRegression+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131583880032\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131610200731\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131616739812\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.1316218033\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131625526186\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreesRegressor+XGBRegressor 0.13163026397\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131634119375\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131634297043\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.131636275372\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.131640569696\n",
"LinearRegression+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131641527235\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131644416122\n",
"Lasso+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131645922214\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131647229949\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131648034789\n",
"Lasso+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131654097431\n",
"Lasso+LinearRegression+Ridge+ElasticNet+GradientBoostingRegressor+RandomForestRegressor 0.131657512655\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+XGBRegressor 0.131666480206\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13166657223\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.131666653733\n",
"Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131667393043\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131672228667\n",
"Lasso+LinearRegression+ElasticNet+SVR+RandomForestRegressor+XGBRegressor 0.131709613845\n",
"Lasso+LinearRegression+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131720470137\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131724488057\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.131746806663\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.131754796206\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131772699204\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131783768976\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131784348051\n",
"Lasso+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131784909592\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131787289427\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131788192386\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13180224067\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131805710918\n",
"Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131817257192\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131819269983\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131824998017\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131828676498\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131846001343\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.1318487258\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.131856908518\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.131857889308\n",
"Lasso+LinearRegression+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131861873933\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.131863969459\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131868434039\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.131871564327\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13188486093\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131889046394\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131904387038\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.131908060571\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131908757811\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131918394099\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131932850551\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13193296997\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.131939302829\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.131941524588\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131945080608\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.131947910851\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131949102928\n",
"Lasso+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131964538768\n",
"Lasso+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131967504634\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.131985041526\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132000613093\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132009286195\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132028206663\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13203391705\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132037618083\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132040607044\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132045642291\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.132045990659\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132050808675\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132062983909\n",
"Lasso+Ridge+ElasticNet+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132069680317\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132076442758\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132084912188\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132090994732\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132117859857\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.13212481442\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13212557646\n",
"Lasso+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132127435531\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132131712064\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132134264329\n",
"LinearRegression+Ridge+ElasticNet+SVR+GradientBoostingRegressor+XGBRegressor 0.132145688628\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132146274117\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.132148353422\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132166173763\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132167029456\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132167982582\n",
"LinearRegression+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132168574426\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132170014569\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132174747027\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132175861478\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132180153823\n",
"LinearRegression+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132182279809\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132194859911\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132197430175\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132205638669\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132206157938\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132209012492\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132209044121\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132209169661\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132216774442\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132218030078\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132228400685\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132258742997\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132260426009\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132267909197\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132276803295\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.132286254139\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132290625508\n",
"Lasso+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132301804942\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132304881742\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132306060908\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132309940472\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132313125563\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132347818736\n",
"Lasso+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132350570061\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132354438394\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13235797275\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132365377174\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132374418239\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132379982769\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.132405650501\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132408358452\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132416991032\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132423500785\n",
"Lasso+Ridge+ElasticNet+SVR+RandomForestRegressor+XGBRegressor 0.132426704122\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132437098749\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132441632583\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.132450803094\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.132451226746\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132460112623\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13246053189\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13246761298\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132472455422\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132474661588\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132475533771\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.132478825755\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132491884436\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13249294428\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132509972626\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132515266319\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132520874207\n",
"Lasso+Ridge+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132523242327\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132529000928\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132534154708\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132535524892\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132544298867\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132563275449\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132569368123\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132572079596\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132575054515\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.132575665345\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132576160899\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+XGBRegressor 0.132578068666\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132591983693\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+RandomForestRegressor+XGBRegressor 0.132595594189\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132598608158\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132601611454\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132602506434\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132608129934\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13260961926\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132615751582\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132616481106\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132618374905\n",
"LinearRegression+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13262003884\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132623067002\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13262486658\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132628840834\n",
"Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132629851624\n",
"Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132649282341\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13266059737\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13268903846\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132691358648\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132695164019\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132696966087\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132697947936\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132703737928\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.132713748891\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132720685178\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132735529245\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13273694838\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132737994773\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13273899068\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13275082633\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132751450884\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.132760254681\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13276066786\n",
"Lasso+LinearRegression+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132774019931\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.13277849849\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132802308304\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132804666069\n",
"LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132809687312\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132809687999\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132816933616\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132822897233\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132825160668\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132825796532\n",
"TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132833632951\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.13283616071\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132853376455\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132854092764\n",
"Lasso+Ridge+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132862737697\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.132862831531\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132868496433\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132869311928\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.132894702913\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132897342141\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132909065702\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132914943011\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13291602859\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132918680007\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132920631095\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132921866686\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132924153909\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132932409152\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132934863261\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132940391855\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.13294371297\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132952358239\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.132953853165\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132956247927\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132958744487\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.132970551753\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.132986774257\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132994696157\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132995081145\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133004440516\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.133009140915\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.133009428214\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13302202588\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133022315168\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133022888804\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133028906783\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13303176652\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133032456127\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133034906338\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133039598936\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133043288456\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133045997335\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133047805218\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13304830888\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133054171691\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133054319921\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133056136995\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133056254817\n",
"Lasso+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133074433266\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133076131685\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133084480018\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133097128594\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133109199007\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13311588318\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133117330747\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133118187381\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.133121108446\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133123085375\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133126791245\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133128368278\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133143354296\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+XGBRegressor 0.133145704933\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133157038212\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133157096005\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13316291418\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133166194721\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133166555354\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.133176258729\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133177307494\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133182564054\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133188927165\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133191905081\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133199535146\n",
"LinearRegression+Ridge+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133206805982\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133210451658\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133213163001\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133217727929\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133222374791\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133223729716\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.133223954414\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133225948527\n",
"Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133227747733\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.133231785723\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133235880279\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+XGBRegressor 0.133238986232\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+XGBRegressor 0.133239398673\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133243410637\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133244872396\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.133245723168\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.133266161991\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.13326637398\n",
"Lasso+LinearRegression+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133273339518\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.133274450686\n",
"LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133291151259\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133296978063\n",
"Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133297815687\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+XGBRegressor 0.133302478522\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133303380325\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13331320246\n",
"Lasso+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133316341736\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133317942126\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.133320251291\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.133329218422\n",
"Lasso+Ridge+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133330133198\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.133330407193\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133343268399\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133352043236\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133352283357\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.133353049388\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133354623761\n",
"LinearRegression+Ridge+ElasticNet+SVR+RandomForestRegressor+XGBRegressor 0.133357452872\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+XGBRegressor 0.133375985119\n",
"Lasso+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133390200188\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133402467705\n",
"Lasso+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133408184536\n",
"ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133410243843\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133415005431\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133421118089\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133422668314\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133423970926\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133432755134\n",
"Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13343330939\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133441840575\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133442426031\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133447638183\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133447940205\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13344839645\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13344846755\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133454479862\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13345683277\n",
"LinearRegression+Ridge+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor 0.133464082992\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133470605073\n",
"Lasso+Ridge+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133481970379\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133483662569\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133484080991\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.133484324621\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.133496785516\n",
"Lasso+Ridge+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133500691859\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13350145995\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreesRegressor+RandomForestRegressor 0.133505107887\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor 0.133508812809\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133512750624\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133513549785\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133515621639\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133519631097\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133533852189\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor 0.133537257386\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133539065029\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133570914803\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133572909914\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133573560501\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133582726461\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133586315782\n",
"Lasso+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133592896166\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133597316105\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133600257503\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133606085054\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133606242489\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133608883917\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133610586189\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133615370472\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133615393288\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133619246293\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133621538346\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.133626579847\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.133635490646\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.13365591309\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133657574037\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133660362112\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreesRegressor+XGBRegressor 0.133669242625\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133670743061\n",
"Lasso+LinearRegression+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133678534514\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133680099034\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.133684889489\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.133699187439\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133700370715\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133705252366\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133707004602\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.133712634258\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133720080076\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133720648593\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133721920362\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+RandomForestRegressor 0.133723254206\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133725394362\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133732926576\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133744688384\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.13374486664\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133747019205\n",
"LinearRegression+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133754483571\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133757748129\n",
"Ridge+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133758898293\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133762476617\n",
"Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133765300508\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133767020242\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133770193581\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133774189932\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13377810694\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13377951755\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133794817059\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133804012422\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133816715481\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133817470599\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133819736905\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133820682976\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.133823139958\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133831369968\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133833743995\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133837603983\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133844664022\n",
"ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133870968934\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.13387276421\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133873341434\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.133879537369\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.133886030536\n",
"RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133904468045\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.133905618539\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133911504848\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133924107665\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133926913117\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133927830055\n",
"LinearRegression+Ridge+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133930562194\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133932061351\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133939068085\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133944009599\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13394415792\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133962238362\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133963295498\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.133964507473\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133977173314\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13398019211\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133980479628\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133982825192\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133992450035\n",
"ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133997720526\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133998760289\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13400540178\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134008074894\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134009458873\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134011556337\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134016378581\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.134016764296\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.134019231455\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13402667053\n",
"LinearRegression+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134027338139\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134028408924\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.134029964324\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.134036232258\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13404534362\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134046695938\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134048770108\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134051920637\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134055213284\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134062472662\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134070314999\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134074853662\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134077706237\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134078116689\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13408251878\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134083175285\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.134089026653\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134100374779\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134100684033\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134106674996\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134111775591\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.134113582515\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134114628203\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.134115649518\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134116897383\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134117210921\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.13411923652\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134119469382\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134125137194\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.134133115156\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.134134011379\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.1341382334\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134141385533\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134144517388\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134146624711\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.134148440205\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134158443423\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134163725748\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134174058459\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.134174220687\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.13417990108\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+RandomForestRegressor+XGBRegressor 0.134180785117\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134187745699\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13419836713\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134198547062\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134199019045\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134204172665\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134209904856\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134215694804\n",
"Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134217126171\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134219740509\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134221792444\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134223685753\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13422610881\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134228948438\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13423877632\n",
"Lasso+LinearRegression+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13424072232\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.134244704724\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13424611152\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.13424976895\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.134250101019\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+XGBRegressor 0.134250417558\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.134255302216\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.134258876577\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134259536965\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134262465354\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13426412342\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134264882625\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+XGBRegressor 0.134265806393\n",
"Lasso+ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134266630364\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134268357841\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134269550323\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134269921165\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134270442696\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134271792299\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.134274910074\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134285074454\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134286501802\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134287627893\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134291183535\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134291511118\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.13429357256\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134297691774\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+XGBRegressor 0.134300106355\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134302598718\n",
"Lasso+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134304339797\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13430876254\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.13430978438\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134310043113\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134316680493\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.134320053573\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134323564504\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134328125579\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.134329418532\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.134330518735\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134337056492\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.13433940912\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.1343447398\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134356486632\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134357645094\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.13436026532\n",
"Lasso+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134365951033\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.134366579253\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134366618309\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134367139154\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134367751197\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134370940377\n",
"Lasso+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134372814861\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134385619936\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134387727077\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134387950183\n",
"LinearRegression+Ridge+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.134391133225\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134397741301\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.134399267297\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134402101477\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134410188181\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134421189297\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.134426132364\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+XGBRegressor 0.134443909205\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134444015501\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134447987318\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134448067476\n",
"ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134449748556\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134451961343\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134458633331\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134463621782\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134468672007\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134476270023\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134478044763\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134480132872\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134480498531\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134483261128\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134484206566\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134490704977\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134493125492\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134495310657\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.13449948334\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13450135385\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134502585862\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134504884497\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134506815165\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134514400728\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134514667836\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134515251381\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.134516363052\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134520531304\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134521828186\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134522053878\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134523373601\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor 0.134525820438\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134526631359\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.134528657292\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134529558498\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134529951064\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+XGBRegressor 0.134532147163\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13453510961\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134535949933\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.134540485425\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134540824169\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134547752996\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134548378435\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.134555940112\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134556363128\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.13455696708\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13455830713\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.13456364366\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134573477586\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134574519119\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134575446527\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+XGBRegressor 0.134581725922\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.134584456417\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134587731702\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134590174844\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134591111293\n",
"LinearRegression+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134593176167\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134593572114\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+XGBRegressor 0.134595093719\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134602193452\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134603927783\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.13460500101\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134607392064\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134607776701\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134609926283\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134615891159\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134618009348\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134622256891\n",
"Lasso+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13462804834\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134633417812\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134639818265\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134649785955\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134652677367\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.1346545324\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134660068343\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+XGBRegressor 0.134662921004\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134666301321\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor 0.134669056806\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134671986307\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134675088429\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.134676271624\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.134681091403\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134686223392\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134696935571\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.134698025386\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134703013172\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134705242738\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.134708974573\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.134711706646\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134717730861\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134718113656\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134720561369\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.134721174951\n",
"Lasso+Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.1347244829\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134725381585\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134727371632\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134735478512\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134735957883\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134736182912\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134736471319\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134741217721\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13474805933\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134752564211\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134753868502\n",
"LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13475530672\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134758158432\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.134759541984\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13476648157\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.134769137148\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134773663573\n",
"Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134776032153\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.134776227675\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134783545584\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134784631775\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+RandomForestRegressor 0.134804473596\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134806447915\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.134808455997\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+XGBRegressor 0.134809569696\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.134809578762\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134822599869\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134823142178\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.13482781422\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134834637073\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.134835227356\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134841196996\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134841865446\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134849570614\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134861653062\n",
"LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134862731376\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134865626183\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13487082176\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.134873563105\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor 0.13487371451\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.134874209272\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134875303522\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.134877967247\n",
"Lasso+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134882037994\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134888097928\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134891047401\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134901094344\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134915702292\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134916142336\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134920519753\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13492201661\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13492394887\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+RandomForestRegressor 0.134925004896\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.134925408974\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134927437531\n",
"LinearRegression+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134928808924\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+GradientBoostingRegressor 0.134931621443\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134932323078\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134936333297\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134940783213\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.134941793433\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134945479403\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134948134544\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13494926158\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+XGBRegressor 0.134953264227\n",
"Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134959899854\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134961154722\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134965072048\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134966139268\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134971813803\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.134972996947\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.134976877778\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+RandomForestRegressor+XGBRegressor 0.134978586646\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.134981240462\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134984468535\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.134988992559\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134991175361\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134994602349\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134995600428\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134997019861\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.135002177152\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.135003895156\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135006543818\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135007446216\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135009812886\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135013646941\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135014890209\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135021851436\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135022204041\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+XGBRegressor 0.135028739455\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135032317362\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135032993361\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135034288355\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.135034988921\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135034989307\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.135046586465\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.13504748406\n",
"LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135050608261\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.1350527345\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.135056168729\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135071805529\n",
"Lasso+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135076735754\n",
"Lasso+ElasticNet+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135078760667\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.135084283466\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135085381502\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135092748195\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.135095793107\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.135095942328\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135096939518\n",
"Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135098706504\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.135098851111\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135103521033\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135103991929\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135104156063\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135105326048\n",
"Lasso+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135110466833\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135112329369\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.13511343316\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135114614295\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135114760115\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.135115693784\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.135117225047\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135120932564\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135126588255\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13513722652\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13514208227\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.135142445162\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.135146513963\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135155565661\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135164590067\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.13516505613\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.135167522251\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135173915602\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.135176465903\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor 0.135181327105\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.135182750336\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.135185635605\n",
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"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135193277995\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.135769975131\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135774646722\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135776805799\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+XGBRegressor 0.135778094662\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135788011879\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135788433273\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135792428568\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135795519776\n",
"Lasso+LinearRegression+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.135802214886\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.135804523952\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135805504585\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135805628852\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135806857502\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135808841763\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135809695921\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135813748223\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135815877403\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135819735128\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135822771201\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135828582008\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135830453867\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.135840550739\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135843170561\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13584483701\n",
"LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135844926655\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135845560863\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13584605217\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135847319589\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135847359023\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135849571995\n",
"Lasso+Ridge+ElasticNet+SVR+AdaBoostRegressor+XGBRegressor 0.135855985267\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135857663125\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.135860949923\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.135864996026\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135871844502\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.135872745092\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135876636101\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135877325596\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135878118937\n",
"Lasso+Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.135882187839\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13588353773\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135886021331\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135886560842\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.135887037357\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135888087484\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135895039873\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135895500814\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135898477249\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135899394043\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135901667504\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.135904728291\n",
"Lasso+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135906282607\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135906564677\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor 0.135912812385\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135912988173\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135913115539\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135915499551\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135922066813\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135926745825\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+XGBRegressor 0.135928375889\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135928940653\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135930941927\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135931293819\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135934828824\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135935984026\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.135937163891\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135941482955\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135947169053\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor 0.135948316067\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135951270742\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135954526398\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135961994717\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.135963373162\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135963384705\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.1359640577\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135964681967\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135965759496\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.135965893417\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135967061226\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor 0.135967474835\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135971183457\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135972840142\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.135975911419\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135976395693\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135977586288\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+RandomForestRegressor 0.135980991954\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135987163178\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135989539643\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.135995881006\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136001849101\n",
"Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13600300805\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136007553953\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136019078254\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136020044364\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136026256036\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+XGBRegressor 0.136027639649\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136034897776\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136035078821\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136036316486\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136036664966\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136044257239\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136045384644\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13604910095\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+XGBRegressor 0.136051206766\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136053853844\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136054234606\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136059785999\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.136064779037\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136066435283\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136068453957\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13607069087\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.136075332758\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136077685387\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136083425112\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136086786722\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136095520404\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136096083619\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.13609721085\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.136098451624\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136099526215\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.136100483918\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136103600341\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.136104639981\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+XGBRegressor 0.136106483559\n",
"Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136113765217\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136115524916\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.136116015168\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136117206382\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136126808657\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136127510102\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136133563137\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136140345183\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136142598779\n",
"Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136149632763\n",
"Lasso+LinearRegression+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136150373007\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.136150748841\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136151666815\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136151916657\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136153901865\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136154074582\n",
"Lasso+LinearRegression+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13615963962\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.136160010896\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136163492064\n",
"LinearRegression+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136163570247\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+XGBRegressor 0.136165873352\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.13616598453\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136169247205\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136170319064\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136170727944\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136173024912\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.136173560179\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136177549563\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.136178578184\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136181236306\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136182604295\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136185119942\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136185186022\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136185362579\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.136185582257\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136192294713\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.13619403392\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+XGBRegressor 0.136196452194\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136201439652\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136213897908\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136214709032\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136215051808\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136215251583\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136215489044\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13621799042\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136224375228\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136230371726\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor 0.136230416679\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136234450366\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136242463077\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136243220056\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136243739308\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136249181885\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136251994734\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.13625384201\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.136254165739\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.136256900179\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.136258837551\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.136261022603\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136266837849\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136266936896\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.136274250778\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136276709509\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136277002832\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.13628264211\n",
"Lasso+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136287160866\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136289384201\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136295769904\n",
"LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136299581388\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136300623399\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.136301637776\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136305290752\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13630791623\n",
"LinearRegression+Ridge+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.136310594367\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136311175799\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136312893678\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136313089163\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.136314911141\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136321801357\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136323483872\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13632763021\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.136330085309\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13633130686\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.1363313327\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136332596787\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.136334019749\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.136334518662\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.136336434884\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136340543536\n",
"Lasso+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136344224743\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.136344892539\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136345852988\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136345892624\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136348507985\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor 0.136349448462\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136349964997\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.136350593402\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136351018191\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136353653554\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.136358101427\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136358926888\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.136359367808\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.136361911208\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13636646534\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136366937117\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13636775716\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136377537349\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.136379853606\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136379918143\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.136382057651\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136389039144\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136391520645\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136392286818\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.136394261099\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136394923139\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136395307832\n",
"LinearRegression+Ridge+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.136396167543\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136398524021\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.136401714426\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136402982614\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136403941415\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136407385761\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136410177143\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136412651338\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136413393165\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136417832224\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136422286636\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136423479559\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136423888265\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136426151162\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+RandomForestRegressor 0.13642644778\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.136426585598\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.136429799684\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136430283214\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136436500372\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136448180463\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136453236323\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+XGBRegressor 0.13645362714\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor 0.136455116713\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.136460021649\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136461733111\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136462832335\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13646331364\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.136463730117\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13646970384\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136471521271\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136471991219\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136474155303\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13647544912\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136480103446\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136480579287\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.136481291192\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+XGBRegressor 0.13648419419\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor 0.136485164887\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136486442209\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13648935872\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136490351026\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136490726916\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136492086978\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136495386149\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13649717653\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136499703982\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.13650059921\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136501386561\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136506335527\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136507615172\n",
"ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136513758866\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136518184476\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136519398675\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136523614691\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136523657411\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+XGBRegressor 0.136525925005\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136526747799\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13653046054\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+RandomForestRegressor 0.136531812948\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136532721357\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136532911738\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.136535541374\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136540664027\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136545023536\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.1365463806\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136550111174\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136551649312\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13655374164\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136554459703\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136555936638\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136558891721\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136562150558\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136563762209\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136563783973\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136563933711\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136566806314\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.136569983247\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136574554124\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136574978704\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136576603341\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136580663352\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136581086523\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136586506854\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.136588036632\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136588658322\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136589311007\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136593464423\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136594083439\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136601355875\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136602787118\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136605270373\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136606930774\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.136617495206\n",
"Lasso+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13661775624\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor 0.136620838136\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.136621456871\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.136623909045\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136625517519\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.13663438224\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136634941092\n",
"Ridge+ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136635102607\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor 0.136637680872\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13663958538\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.136641109324\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136641124542\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+RandomForestRegressor 0.13664203806\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136642578805\n",
"RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136643656567\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.136647819059\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136651962887\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136654621523\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.136659530515\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136661379176\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136662167178\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13666533619\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136669280528\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136669726661\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136672914432\n",
"Lasso+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136676123227\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136679509394\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136683977961\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.136694607636\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136695151565\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136695171325\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136696013226\n",
"Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136696822865\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136699168794\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136705172367\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136707160455\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136709284428\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136716018745\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136722477828\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+XGBRegressor 0.136724237665\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136725941832\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.136728432568\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.136729009294\n",
"Ridge+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136729586761\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136735686238\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136738478855\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136751896408\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.136757451363\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136760992933\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.1367618515\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136771650553\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.136771915146\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136783121431\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136783997079\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136785610726\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136788436946\n",
"Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136790564571\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.136791657272\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13679512923\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13679583208\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136798142613\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136799356913\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136809316419\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor 0.136811323563\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.136816652374\n",
"LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136818290093\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.136825247052\n",
"Lasso+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136828162773\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136831869127\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136831960155\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136833866278\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.136840988103\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136842799676\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136853879129\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136866982762\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+XGBRegressor 0.136868116478\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136868394908\n",
"Lasso+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13686871249\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.136871163071\n",
"Lasso+Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136876352242\n",
"LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136881888964\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136884831624\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136886820063\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136888143802\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136888700994\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.136889702562\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136893932762\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13689395856\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136894861204\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13689612833\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136898672365\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.136900323538\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136903915403\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.136905756149\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136906167955\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136906409833\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136908648502\n",
"Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136910080052\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136910801309\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136911254438\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136912774389\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136914035094\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136917800505\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+XGBRegressor 0.136925420745\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136926880515\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136932606609\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136933062631\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.136936058648\n",
"Lasso+Ridge+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136938277505\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136939917049\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor 0.136940750852\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136943998213\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.136949458156\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.136956858357\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.136958325608\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136958910415\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136958932698\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136962575567\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136963312896\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.136963323667\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136965565735\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136965566595\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.136966732068\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.136967390915\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136969331354\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136969906809\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136970602394\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136970798443\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor 0.136971724005\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136973617617\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136979361087\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor 0.136981763018\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136986867102\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136987567444\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136992453611\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136995060407\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.13699602979\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.136997145926\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136997993876\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.137007161884\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.137012186882\n",
"Lasso+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137013445297\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137018367016\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137020838414\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13702205485\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137028100241\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137029714145\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137033646762\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+XGBRegressor 0.137034907613\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137036244255\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137036470275\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.137036843896\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137037886019\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137039041246\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137039404433\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.137039683717\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137041883483\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.137043164864\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.137045525065\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137050169606\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137054510755\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137054811054\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137057077364\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137059917312\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137063160904\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13706318663\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137063880669\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137068691905\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137073942684\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13707545818\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137076210578\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137079427027\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.137082377335\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor 0.137082460928\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.137083949775\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.13708456467\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+GradientBoostingRegressor 0.137086007822\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.137088817318\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137089325884\n",
"Lasso+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137092336835\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137092811847\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137092848297\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137096030953\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.137096675325\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.137098312806\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137107392696\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.13710809996\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137108218122\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.137110331224\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.137111043599\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137111977403\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+XGBRegressor 0.137117629936\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137121497452\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137121967274\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137127328878\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.13712792304\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137128008855\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13712918369\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137130118053\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137131217341\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137134576917\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor 0.137136077882\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137136211193\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137137104658\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.137137528782\n",
"Lasso+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137138102212\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137145857087\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137147870445\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137150161068\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137150736165\n",
"LinearRegression+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137152194009\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.137154125246\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137154313854\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137154377973\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.137156162983\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137157737948\n",
"Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137163912795\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137163977309\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137164872116\n",
"Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137172946432\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137174666034\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137177292005\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137177456139\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137182969155\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137187987937\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137190070551\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137194955755\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137195330165\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.137196223695\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.137197601129\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.137205141443\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137208814057\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor 0.137211699513\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137213801498\n",
"LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137214739155\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.137214923852\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.137215952038\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137217653771\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.137221260872\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137223130037\n",
"Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137226542484\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137227467125\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137230243104\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+XGBRegressor 0.137234608233\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137238546692\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+RandomForestRegressor 0.137243805986\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137245131155\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137264175548\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137268356529\n",
"LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137270033151\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137272353726\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13727396044\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137276103606\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.137276280485\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137279930331\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137288457849\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137289001458\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+XGBRegressor 0.137291681398\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137310650997\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.137315189218\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137321756887\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137322080921\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.137323992059\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.137328679325\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137331459979\n",
"Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137334277411\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.137339212935\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13733959557\n",
"Lasso+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13734069808\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137340794877\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137341942114\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137343565184\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137344254547\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.137344450075\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137345339963\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137352554296\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137354066972\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.137355367909\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor 0.137361277445\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137364503559\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.13736466962\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.137368612735\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137371143909\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137371856634\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137373109177\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137376342175\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137380071518\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor 0.137381342832\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137383091648\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.137385914149\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13738653908\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137393905628\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.137394363236\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137394991007\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137395824785\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137396939328\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13739874008\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13740121984\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137405456126\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137411553853\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137421367354\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor 0.137422570695\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137423097589\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.137424832261\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137429261397\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137433848217\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.13743417966\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137436355631\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137437979858\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137438417001\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13743999215\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137440357327\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137444780448\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137448366765\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137448376199\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.137449344608\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137450184149\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137450946544\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.1374519662\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137453024935\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137453150674\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137454045476\n",
"RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137454396111\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor 0.137456090239\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137456325598\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137457035309\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137459996418\n",
"Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13746079186\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137464851122\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.137465604086\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.137466104388\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137469128424\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137469896027\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+XGBRegressor 0.137476815997\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137480062356\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137480663195\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137483770113\n",
"Lasso+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137484514852\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137485344491\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137486549482\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137489432992\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137490664856\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137492002079\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137499266228\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.137502400659\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137503130522\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137505787747\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137510296972\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137513272809\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137514558043\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137515604334\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137515718535\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.137517730856\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137518293861\n",
"LinearRegression+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13752049951\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137521635169\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137524142156\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137526758392\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137528504089\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.137529771825\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137531912348\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.137534673484\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.137535882493\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137539686438\n",
"LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137539949973\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.137543159502\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13754471891\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.137545485262\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137546075692\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.137548486187\n",
"LinearRegression+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137550819161\n",
"LinearRegression+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137558070249\n",
"LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137563874923\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137566594292\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.137569409723\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.137572974858\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137578043655\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137583699771\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137584935384\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137584953107\n",
"TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137585611128\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13758655403\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13759599547\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137599051183\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137599255386\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137600624409\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137603707778\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137605657684\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.137606118048\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.137609815318\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137611980793\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137616120799\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137618611001\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137621955918\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.137622403553\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.137622833282\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137634648233\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137642422041\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137645446128\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137650064246\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137651910514\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.137653816218\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137654061658\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137655065396\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.137656799007\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137657856897\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13766310455\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137664802389\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137665493598\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.137668941249\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.137672227035\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137674909432\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.137678994733\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137682811723\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137684918606\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137686731299\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.137687237129\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.137689076008\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137690232312\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137692309088\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137692938224\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137693291847\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.137693875042\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.137697369008\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+RandomForestRegressor 0.137703230507\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137704192255\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.137707004036\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137707843502\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137708669626\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+XGBRegressor 0.137709272542\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137710829465\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137713909202\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor 0.137715116737\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.137715255928\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137715362418\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13771729362\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137720670824\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.137721092047\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137725487051\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137725958044\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137726150003\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.137744606802\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137745203522\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137748811711\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137753405484\n",
"Lasso+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13775769719\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137761031562\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.137761777968\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137763548234\n",
"Lasso+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137763844116\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137764972957\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13776529742\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor 0.137767751061\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.137769446358\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137769850972\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.137771566339\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137772102125\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137774021022\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137774604341\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137774674729\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137777077474\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.137779664479\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137780689674\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137785832093\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137787717872\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.137799210961\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.137800344927\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.137801728768\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137811579701\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor 0.137814929153\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137817104527\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137818048283\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137818241539\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137819029681\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13782035661\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137820599934\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137823239595\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137829974406\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.137830885427\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137833652358\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137833662653\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137837325703\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137842358415\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137845349182\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.13784910506\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137849643805\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137850355545\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137851885703\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.137852164968\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.137855257198\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.137861552922\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137863336129\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137865536019\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137868789589\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137875527948\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137875868839\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.137878033816\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor 0.137880817066\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.137880853128\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137884038631\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor 0.137885001472\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137887545182\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137890130426\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137890565193\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137892459211\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.137906210917\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137906806691\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.137911639033\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.137913377236\n",
"ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13791673601\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137918337259\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137918658146\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13791891694\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137921435044\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137922958526\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137930228277\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.13793137972\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137936565055\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137937038955\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.137939197125\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.137945239731\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137946518458\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137947715293\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RandomForestRegressor 0.137948342812\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137948888529\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.137949621917\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137950444418\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.13795081897\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.137951258268\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137951988236\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137957503077\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.137957541354\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137959274308\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137960489233\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.137960638592\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137960676567\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137963570028\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137964051503\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.137964902304\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137967160544\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137967760738\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137968862498\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137976004331\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137976912247\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137977599157\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137978749432\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.1379796992\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.137981441289\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.137981460273\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137983018604\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137984139056\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137986425711\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.137986686232\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137987151401\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137987586754\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137990787404\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137991078276\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137991078745\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137992083228\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.137997944254\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137999026732\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138006260753\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138012546523\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.138014865871\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138021440975\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.138022320963\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138023779361\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138025045208\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.138028720839\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.138029228901\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138033156002\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.138033932072\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.138041979414\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138044553366\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138045762225\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.138050028634\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138054714024\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13805513698\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.138056541665\n",
"LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138056596643\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138056596826\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor 0.138057813832\n",
"LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138058122459\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13805955007\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor 0.138061204084\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.138064607988\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.1380650574\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138066885356\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138069073958\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138072610338\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138073868546\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.13807701495\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138077595114\n",
"Lasso+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138078282987\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138078879535\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.138079330467\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138079904856\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138080763107\n",
"Lasso+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138081107222\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138081746103\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138087102408\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138087926895\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138091590937\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138092637506\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138095312906\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138095916558\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138096632732\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138101406866\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138102797788\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138106246577\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138110391271\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor 0.13811301251\n",
"Lasso+Ridge+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor 0.138113112717\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138113139054\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138115648147\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor 0.138129509207\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.138131447956\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor 0.138134385535\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138134872446\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138138193446\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.138142736032\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.138142937221\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138146401081\n",
"Lasso+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138151226707\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.138152752069\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138154448093\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138155364492\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.138156836186\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138159739541\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.1381641118\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138165982131\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138168868073\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138170877478\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.138178580457\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.138184621584\n",
"Lasso+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138185745489\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138188391747\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.1381891766\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.138198187175\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138200300573\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.138201223596\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.13820445765\n",
"Lasso+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138212286162\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138212472085\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138212766288\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138214608898\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13821545471\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138215610515\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.138220013327\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138223200704\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.13822623696\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13822774837\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138232406695\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138234281024\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138239582096\n",
"Lasso+LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.138245126952\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138245252115\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.138252304059\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138254087099\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138254254319\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138254943826\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor 0.138260778722\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138263115358\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.138263221253\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138264674298\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138265926648\n",
"Lasso+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138266058704\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+XGBRegressor 0.138267407827\n",
"Lasso+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138268541729\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138270488077\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138273708147\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138277478171\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138277607341\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138282059872\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138284942585\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138287593335\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138287661157\n",
"LinearRegression+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138290156423\n",
"LinearRegression+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138293530435\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138295659095\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138302848022\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138306649668\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138309102876\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138311595195\n",
"Lasso+LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.138312209294\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138315789633\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.138316128416\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.13831615482\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138317405181\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138318418509\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor 0.138318918888\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138319160134\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138321069638\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138321711456\n",
"Lasso+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138324548233\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138326693468\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13832825085\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138334405382\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.138335961694\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.138338471442\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138348850971\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.138350960809\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138351943876\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138353906489\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138355329846\n",
"Lasso+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138356183524\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor 0.138359637156\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.13836303201\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor 0.138363189375\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138364283344\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.138367108131\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138369199533\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138370371973\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138371314113\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.138373624395\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138375038216\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138375184305\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138377573074\n",
"Lasso+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138380266597\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138380391879\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.138383902165\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138384004261\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.138384128734\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13838593911\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138387041367\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.138391528529\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138394016235\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138395918382\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138396713773\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.138397232507\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138397990395\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138401719786\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13840388303\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138406717895\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138409680946\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138410793815\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138411285512\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138414042434\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138416082844\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138421827158\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138422052746\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138426729657\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138427169294\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138428716774\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.138430086765\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138430514415\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138431688751\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138432386177\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.138436881599\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138437214824\n",
"RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138442572306\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.138448085933\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138450383782\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138451942507\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138455460982\n",
"LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138456975702\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138459711406\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138464334823\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.138467338977\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138469394211\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138470625601\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138473145215\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138474746115\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor 0.138478816007\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138479189699\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138479728411\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138481402302\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138482235434\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.138487735077\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138488487512\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor 0.138489845971\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138492191492\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.13849260951\n",
"LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13849444965\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.138495224292\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138495580394\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13849569244\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138496551223\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138499005684\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138501007711\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138508633753\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138509404438\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.138520321643\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138521435571\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138527012232\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138527275946\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor 0.138527326537\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.138530386823\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138530527881\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138535931036\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.1385367849\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138538700944\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.138538712338\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.138538941909\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138541648655\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138544482518\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138544704149\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138545913755\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138548386787\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.13854998115\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138554230394\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138556466325\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.138557122337\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138558791135\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138567026257\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138568829898\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.138569765207\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.138571156218\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138571225641\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.138576437065\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.138577574104\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138577724324\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138582026812\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.138583889659\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138585663161\n",
"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.138590942096\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.138591288603\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138593745126\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138594536513\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138594950121\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138595577085\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138598531595\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138600706777\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13860349557\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138603510282\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.138604776623\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.138605360468\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor 0.138606139635\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138607168392\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138607421184\n",
"Lasso+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.138608264146\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138610699182\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.138611493016\n",
"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.138611618604\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor 0.138612137616\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138613447472\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.138619315882\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.13862079176\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13862342393\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138624371285\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138626306478\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138627024494\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138631958331\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138632457077\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138633016917\n",
"Lasso+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138640731675\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138643610482\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138645463563\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138651219565\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138655558063\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138658302241\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138662812432\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138669112042\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138671720288\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138671769616\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138674081368\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138676980756\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.138678421829\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138679076856\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138683774785\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138686221489\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.13869224836\n",
"Lasso+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138695815189\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138703388134\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138705458895\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138706325005\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138706902812\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138708110641\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138711113119\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138715859712\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138716427302\n",
"Lasso+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138719558697\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138728609688\n",
"TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138732721597\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138735792083\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138736563908\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138739284461\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.138740244865\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138741088175\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138744711491\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138746539029\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.138751438272\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138752100071\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138752556807\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138754187933\n",
"TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138754191879\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138756861672\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.138758586861\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138759269091\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138761176423\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.138761863584\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor 0.138763482834\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138766275456\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.138769525742\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138770644167\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.138771369804\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138771535694\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138772896847\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.138773592075\n",
"Lasso+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138774663799\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138776302295\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138777255039\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor 0.138778401006\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138779503417\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138780436601\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138781749484\n",
"Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138784842278\n",
"Lasso+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138785457528\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138785658416\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.138789613679\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138803795193\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138803842821\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138805171322\n",
"Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138805553795\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138807775736\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138808668551\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13880882668\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138812499068\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138815546595\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138817626198\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138819044711\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138821856921\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138823518446\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138824325371\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138830219899\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.138831082541\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138833473986\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138838448099\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138844395503\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138849994452\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor 0.138852356258\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138859492845\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138859981789\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.138860506784\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.138863017553\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138863913534\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.138865334713\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+RandomForestRegressor 0.138868114627\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.138870666036\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13887351392\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor 0.138874542363\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138882799924\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.138884976308\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.138885976886\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.138889548923\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138890195077\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138896413809\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.138906717432\n",
"Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138909754933\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.138911493906\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.138911733977\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138916483053\n",
"Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138932980417\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138934647788\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138935831097\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.138942125615\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138954266405\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138956105821\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.138956239896\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138960121467\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.13896054449\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138961358671\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138961588541\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138962219895\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13896275617\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.138964891722\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138965765945\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138966639366\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138979263585\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138982250192\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13898295332\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138988630027\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138990719631\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138991918964\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.13899335251\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138995354583\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139000600761\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139002649375\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139004738415\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139005691912\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.139006507843\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139007573158\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139008156354\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139009566102\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139011212029\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139012121816\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139015348658\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139016970158\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139018783821\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor 0.139019902936\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139020419525\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.139021078201\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139025653269\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139028388368\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139030853728\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.139033669709\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139033799327\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139034456487\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.1390373129\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139037935784\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13904161678\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139044065645\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139046805582\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.139048051005\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor 0.139048826679\n",
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"Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139482576981\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.139485768044\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139488278001\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.139492714161\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139492911498\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139493234366\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+XGBRegressor 0.139494285329\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139497359682\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139499706306\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139502107029\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139505963883\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.139506787883\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.13950963469\n",
"Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139510405046\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.139512770913\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.139516531485\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139520673145\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.13952152999\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139522527008\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.139527158093\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139530775796\n",
"LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139531276226\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139535333735\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139535597853\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.139539473037\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.139544163177\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139549059661\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139551931309\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139556927292\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139560220829\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.13956085867\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.139563232523\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139567892208\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.139568376017\n",
"Lasso+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139570289203\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139571550697\n",
"Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139574023467\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139576894008\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+XGBRegressor 0.13957813971\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139578556598\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor 0.139580242196\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139582395155\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139585537696\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139586403112\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.139589799259\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139592844322\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139593657797\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139598690052\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139601517055\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139601666742\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139602259185\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.139607349939\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139611952884\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139613782347\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139614870282\n",
"Lasso+Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139618637746\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139620508391\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor 0.139620584956\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139621859006\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.1396246165\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139625730938\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139626983292\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.139630652938\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139636319291\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139638979092\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139640563899\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139646067557\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139646306353\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139646735917\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139647496761\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.139647786893\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139649080845\n",
"Ridge+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139649609038\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.139650279482\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139652149311\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139654966918\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139656899284\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.139657790532\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139661713926\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+XGBRegressor 0.139662639248\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139667652747\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139673229767\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.1396753759\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139676037209\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139676364441\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139677487124\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.139680834267\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor 0.139681903667\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139681982317\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139686173021\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139690549368\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139690613833\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139692723228\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139692986016\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139693244912\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139698973147\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.139699026902\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139701758373\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139704699778\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.139706477694\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.139708885031\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.139709554448\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13971046404\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor 0.139711617863\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139716047177\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.13971625377\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139718000288\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139718514138\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139720574606\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.139723317825\n",
"Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139724423853\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139725904923\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139731485977\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.139732031998\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139732074712\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139737662635\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139738946134\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139740803217\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.139743095454\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139743401213\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139747901606\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139753681441\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139754435009\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139757077243\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.139758995165\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13975899938\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139760689772\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139762176414\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139764032724\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor 0.13976594585\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139767244485\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139768188522\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139770155955\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139778625376\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139779531803\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139781205787\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.139785701965\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139787363698\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139789202478\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.13979335927\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139794237618\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13979447283\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139796497324\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.139797884784\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13979988933\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.139800769787\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor 0.139801645302\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor 0.139803418352\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139804191985\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.139818330533\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139818415157\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.139820955308\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139824875006\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139825750328\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13982921643\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.139831518504\n",
"LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139835613699\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.139842134186\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139845104055\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.13984567333\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.139846783772\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139848418251\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139850451512\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139850572229\n",
"ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13985150216\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139853026882\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139853463069\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139853801241\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139854301379\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.139858523144\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139859523198\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13985984134\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139862033241\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139863908622\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.13986601193\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.139866146847\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.139868264534\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139870585952\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139872493699\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13987284428\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139875089878\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139875291998\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.13987681972\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.13987829785\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139879367037\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139880405732\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13988464738\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13988489901\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13988725851\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139889217422\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor 0.139890973656\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139898400914\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139901981123\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139911849418\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139912152697\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.139917929708\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139918369639\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139920189144\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139923875147\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.139924435571\n",
"Ridge+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139930489998\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139934899527\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.139935997695\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139938131741\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.139940377907\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139941580745\n",
"LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139945390118\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139946689193\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139946878844\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.139952800108\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139952817088\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139955592661\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139955696213\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139958425793\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139959201103\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139960700831\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139962039354\n",
"Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139962303044\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139965218759\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139966638195\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139968522907\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.139972256887\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139976166953\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139980135265\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139981425838\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.13998993652\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139995263962\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.140000035573\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140002808695\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.140003451275\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140005033438\n",
"Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140005282055\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140006456397\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.14001389257\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140015197209\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140017302687\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140020007474\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140021413673\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140024799263\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.14002750442\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.140029084981\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140034665223\n",
"Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140044987035\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.140046079794\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140057263993\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140059542457\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140061597258\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.140064469024\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.140066561117\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140068265957\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140073268393\n",
"TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140077921572\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140079178902\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140086730108\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.140090294323\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.140092472085\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140094186286\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140094344036\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140098609021\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140099744688\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140106001875\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140106090577\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140109769135\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140117172921\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140118340966\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140124022854\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140128588247\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140128948016\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.14013129204\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.14013347399\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.14013490026\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140136521694\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.14014151586\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140142757286\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140145063909\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140146141459\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.140146235145\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140149600609\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.140149904905\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140150978119\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.14015117466\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140154434801\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14015485441\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14015924702\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140160911478\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140161272518\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140163269152\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140163591365\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140165272712\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140166375107\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140168896517\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.140173582461\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140176121642\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140177513601\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.140184153119\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140185256219\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140187221133\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140191550475\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140193636462\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140196231352\n",
"Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140199359298\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140200627555\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140201818379\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140202643122\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140204002289\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.14020494729\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.140206440079\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140208452046\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140208638697\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140211464795\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140211922266\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.140212933479\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.140213479083\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.140213535834\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140214908958\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140215464792\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140216136829\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140220919778\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140222922065\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.140226144944\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140227779121\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140233653606\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140238036763\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140241462639\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.140241722126\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140243433055\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140244392722\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14024854514\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140252711831\n",
"RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140254632702\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140254715731\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140261864925\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140263213143\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.140269973165\n",
"Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.140272169481\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor 0.140273669333\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.140275447862\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140278398785\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.140284462493\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140286431434\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140287208315\n",
"ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140288367928\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140293283102\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor 0.140294474969\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.140294523539\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140297026309\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140300648004\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140303884667\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140304375224\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR 0.140306218348\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140306890456\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140307426018\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.140308283574\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140309666659\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140313629789\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140320201445\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140326862413\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.140327614891\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140330229695\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140333918443\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140334641642\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140335411506\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14033963974\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.140340770664\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor 0.140342314675\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140345240452\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140347400241\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140349802539\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140350011598\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140353948305\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.140361295258\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140362932571\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140364032341\n",
"Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140367110914\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14036848344\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140373061811\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.140373710932\n",
"TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140379644273\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140381032807\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140382946164\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140383091055\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140389148038\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140392701553\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140394517416\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.140394636169\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140395088257\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.140395483048\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.140397147768\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140397409011\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140397896561\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.140399304077\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.140403285167\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.140404765861\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140405142702\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140410205514\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140414566605\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140417862032\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140424338302\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140426613625\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.14042729894\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140432121289\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.140433746251\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140441172417\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140442330349\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140447053229\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140450747672\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140453314083\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140454896423\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.140469441895\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.14046947428\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140470573045\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140477252618\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140478350704\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140480973865\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140481278936\n",
"RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140489683897\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140489807056\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.14049492187\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140496031595\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14049693037\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.14050363234\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140503841229\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140506685616\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140508170909\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140508354698\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140515301633\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14052629726\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.140530283101\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140531121376\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140539447454\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140540462128\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.140541098649\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14054449132\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.140547868654\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.140548395323\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140555055971\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.14055595377\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140556004923\n",
"Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140563127255\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140571606662\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.140579020737\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140579279369\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140586009665\n",
"Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140587096604\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140592789047\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.140592933717\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140593857373\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor 0.140594811319\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140597269557\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140599737265\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140601140446\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140603170487\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140605549856\n",
"Lasso+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140607204803\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140610627921\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.140615326486\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140615917968\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.140618837463\n",
"Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140620037125\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140625233591\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140625238446\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140627455321\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.140628183845\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.140631945218\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.140633434227\n",
"LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140634097432\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140634170604\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140636954306\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140638000394\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140640785356\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR 0.140642491825\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140644141088\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.140648528866\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140652281406\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140652386497\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140652572495\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140654124136\n",
"Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140654246709\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.140660046608\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.140660826756\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.140663954279\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.140664696724\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140668079389\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.14067156051\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.140673453659\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140676409237\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140682890218\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140684252554\n",
"Lasso+ElasticNet+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.140687349102\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140688606706\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.14069387797\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140694171361\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140694261144\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140696495046\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14069745156\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140698074018\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140701393288\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.140701407344\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.14070605356\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+XGBRegressor 0.140706201353\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.140710831302\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.140712774276\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140712865233\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.140720013338\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.140725302518\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140727662495\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.14073094137\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.140734486328\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140735055159\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.140741798492\n",
"Lasso+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.140742174632\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140742207068\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140756178555\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140757829296\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140758673424\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140760499332\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.14076577295\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140769780986\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140770224713\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14078190944\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140786112844\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140788816046\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140791962321\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140795731516\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14079580875\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140799848015\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.14080456217\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140806468327\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140807033759\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140815955268\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140821065922\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140822488075\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140824794385\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140831148815\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140832408375\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140832500542\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140833989426\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140834475675\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.140839422423\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140841461594\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140844780932\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140848281687\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.140848559061\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140849792378\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140849914212\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor 0.140853620256\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140859195797\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14085993634\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140859977694\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140861346353\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140861810791\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.140868543157\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140868998148\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.14087224008\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.140874369397\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140874467648\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.140879578496\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.140881524361\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140881871247\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.140882861354\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140891736777\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.14089724816\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140898524939\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140906381387\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140908169191\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.14090920871\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140910940361\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140913466469\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140915419246\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140915639571\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140917352487\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.140921761477\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR 0.140923433893\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140936719706\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140936730766\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140937344938\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140942288607\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.140946932329\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.140949059395\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.140957342196\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140959176379\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.140960089619\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.140962418995\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140965770442\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140971854024\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140974717329\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140976849616\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140978010447\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.140982107092\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140999002665\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140999661534\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.141000146568\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.141000174989\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.141003163849\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141003200729\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.141005765485\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141007981413\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.141008072209\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.14101038676\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141010875462\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor 0.141012516192\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141015919959\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.141017936716\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.141022418248\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor 0.141027446545\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141031628222\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.1410345224\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141036033346\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.141036772643\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141038591675\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141040583556\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141041601285\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141044357174\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141045265771\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.141046823145\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141047576663\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.141050524722\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141056860348\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141057760228\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141058442884\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14105983984\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.141067764302\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141073818582\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor 0.141073837946\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14107433666\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141074604228\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.141076102782\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141078230383\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.141082683381\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141085005837\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141087011702\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141088359384\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.141089850734\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.141092453582\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR 0.141097337366\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.141098834692\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141105391556\n",
"Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141112296383\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor 0.141112315663\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141113396386\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141114764019\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141116215513\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141119581532\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141120451167\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141125413949\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.141125577523\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141126341728\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141129590173\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141130411959\n",
"Lasso+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141131546839\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.141132147456\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141132524062\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141132578668\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141134480059\n",
"RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141134574032\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141140504724\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.141146321317\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.141147994746\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141156785917\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141156788888\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141157817239\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14116007902\n",
"Lasso+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141161384015\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.141162529168\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141163300785\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.141165703944\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.141166347573\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14116636354\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141171320365\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141171396131\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141172142701\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141179423957\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141180462878\n",
"Lasso+LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.141180638627\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.141181619469\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141185918939\n",
"RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141189694367\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141189878982\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141192109141\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.141192991656\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141194071216\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.14119505816\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141197491775\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141198067768\n",
"Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141201302457\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141203185304\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141205273453\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141212610861\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.141219934608\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14122058425\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.141226172279\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141228088524\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141234898263\n",
"ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141235601053\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141235847495\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141244311048\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141249100497\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.141250837222\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141258088486\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141258410939\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141258725171\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141262217788\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141262903756\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141268363027\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141280631514\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.141283403134\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141286913172\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.141295320659\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141299792653\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141299878064\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141303960686\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141306169734\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.14130653147\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.141306756256\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141309594408\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.141313667305\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.141321479292\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141327558142\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.141327721459\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.141329422166\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141330941723\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141332169727\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.141332842294\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141333649805\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.14133387978\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.141334502328\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141336700005\n",
"Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.14133850994\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.141338906912\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.141341978793\n",
"RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141342496762\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.141343281711\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141346412808\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141347012945\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.141349650572\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.141351094991\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.141357496042\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.141359535988\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.141360910939\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.141361115028\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141361987781\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141371397365\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141378610335\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141379155365\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.141379438953\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141386167617\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141388233599\n",
"ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141392908629\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.141398682894\n",
"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.141405065832\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.141405762186\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.141406002425\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141406432609\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141411057549\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141412495562\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141413586173\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.141413712302\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.141417700101\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.14142039519\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.141428617451\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.141429110958\n",
"ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141436255993\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.141436733105\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.141439483019\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141441060489\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.141447934696\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141448048915\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141449508565\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141451790127\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141452206487\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141457590607\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141457804693\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.14146177305\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141472940087\n",
"TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141474220837\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141474475671\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141475636434\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14147924563\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141481043792\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.141484106961\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.14148622293\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.141490643032\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141491700369\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.141492017243\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141492334296\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141500553586\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.141501589753\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14150207358\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.141507052819\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141508221323\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.141512675434\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.141513558058\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.14151460214\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141516315782\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14151931542\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141522192905\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141526113166\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141526468511\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141528794446\n",
"SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141537119737\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.14154146863\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141544879625\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14154735551\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor 0.141550774079\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.141552019834\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.141552461838\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141552834442\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141560328007\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.141561034608\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141562110914\n",
"Lasso+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141562164133\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.141562603742\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.141563327608\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141563796834\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor 0.141564850441\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141567773274\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141574408244\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.141576284444\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141578334276\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141580426837\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141585073539\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141585212186\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.141595198164\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141606231665\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.141609469198\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14161171339\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.141612808593\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.141616876827\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141618381994\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.141621619517\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141621670727\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141623083459\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141624438084\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141625323055\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141628746135\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141631096679\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141639207637\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.14164164296\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141643997606\n",
"Lasso+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141645935453\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.14164683\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141647622225\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+RandomForestRegressor 0.141655972157\n",
"Lasso+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141657284128\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141657595167\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141664599737\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.141666288075\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141667003596\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.14166851743\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141670312575\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141671896002\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.14167375027\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141675107517\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.141675431127\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141687770289\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.141694059533\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141701214133\n",
"Lasso+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141705987644\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141716394112\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.141719473153\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141720180554\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141727587386\n",
"Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141734717193\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141735522292\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.141738139552\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141741437609\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+RandomForestRegressor 0.141744194151\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.141744343313\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141745476809\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141745933959\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141747073868\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141759640192\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.141763596751\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141765577527\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.141771516287\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.14177336691\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.141774344678\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141780906093\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141782133697\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.141782789216\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141784937727\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141785851287\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor 0.141787031963\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141787131196\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141787950937\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141789641315\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141793062829\n",
"Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141794591281\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141794984709\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.141797240016\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.141799170016\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141800870243\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.141801422487\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.14180347883\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141805068228\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141805305686\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141806152441\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141810943014\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141815994573\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141816701446\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141818159181\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.141824183001\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141826345859\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor 0.141832919637\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141834107787\n",
"Lasso+LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.141837784118\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141843314445\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141847974376\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.141853737769\n",
"HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141857124372\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141858706094\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141870813668\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141876184109\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141878112308\n",
"Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141878262867\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.141878328238\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.14188240562\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141888541349\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141891017374\n",
"LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141897267272\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141898509354\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.141901378397\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141911011074\n",
"ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141911066347\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141913099656\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141913292721\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141915165813\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141915641148\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141920690541\n",
"ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141921639433\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.141922316569\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.14192468834\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141925676904\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141929595702\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.1419299623\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141934806786\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.141936819024\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141939395311\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141940295708\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor 0.141942343576\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141943550294\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141949236514\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141952155304\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.141952403561\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.141956885495\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.141957862342\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141958075391\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141962299704\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141963550308\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141970348855\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.141971133033\n",
"LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141973838103\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141974955656\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.141976329894\n",
"LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141979440924\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141979528172\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.1419899045\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141993102768\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141993629394\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141999799086\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142001952222\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142005268275\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.14200832309\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142008559878\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142010198819\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor 0.142014104094\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142018201058\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142018724713\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142020298345\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.142023486616\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142025387335\n",
"LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.142032928934\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142041692384\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142045685772\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.142046871019\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142053373043\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.142053800944\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.142054195855\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.142059982057\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142060816092\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142068833703\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142069202953\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142071062667\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.142073806444\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14207414409\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142075881667\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.14207866576\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142083485274\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142083874077\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142086363673\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor 0.14208866022\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14209357378\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142105490307\n",
"Lasso+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142109787999\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.1421103824\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.142120949108\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142124964143\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR 0.142125085063\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142128607831\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142147678725\n",
"Lasso+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142158638232\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142172590917\n",
"Lasso+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.142173832355\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142177734809\n",
"LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.14218279199\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142190660199\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142190840757\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.142193537379\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.142202104325\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142204186789\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142205926206\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142207445764\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142208358593\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.142210929809\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142212724853\n",
"ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.142212733984\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.142213065443\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142215631414\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142215758229\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142219047078\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.142219454914\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142220961821\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142221097951\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.142223263239\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142228802725\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor 0.142230844412\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142234306013\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142241990538\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142253279335\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142255999134\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.142259217449\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.142260065888\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142260536275\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.142260615422\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142262418651\n",
"Lasso+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142262764094\n",
"Ridge+ElasticNet+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.142270956006\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.142273736211\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.142277273666\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor 0.14228548253\n",
"Lasso+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142288246667\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14228883278\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.142290585904\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.142292473754\n",
"RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142293304827\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142296017906\n",
"Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.142302283206\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142307237549\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142307624546\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.142308554197\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142309395543\n",
"Lasso+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142309704588\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142310723474\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142312104482\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142313883475\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142314680252\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.14231659895\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142318339725\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142321551285\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142322742771\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142327420431\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142328999373\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.142329187378\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142340664342\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142341616156\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142344792553\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.14234553441\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142346718342\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142350408775\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14235543321\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.142356314818\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.142359580167\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.142362037967\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor 0.14236213417\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142366659546\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142369460189\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.142369940201\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14237460115\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142376612134\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142378921537\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.142381271923\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142381701858\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.142383659901\n",
"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.142385035907\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142385898154\n",
"SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.14238879701\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.142389414821\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142390286198\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142392970924\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor 0.14239359088\n",
"TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142393649535\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142395723235\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.142405369968\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.142405991703\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.142407481736\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.142413074709\n",
"Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142413594198\n",
"LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142414311364\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142417196265\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142417438815\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142420023828\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor 0.142423681685\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.142424704266\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.142427513065\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.142431914106\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142434408301\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142438134633\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142443075988\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.142444004988\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.142452160612\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor 0.142462939758\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.142463937029\n",
"LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142468644019\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor 0.1424719436\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142475457609\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.142481213994\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142486962688\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142493658402\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142494260614\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.142495288535\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142496868001\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14250016523\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142505395613\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142513700181\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.142522198828\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142523750068\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14252516403\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.142531228797\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142537013342\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142539699731\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142540710532\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142542955578\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142550210074\n",
"LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142553988696\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.142560975512\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142571004293\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142577412802\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142583837553\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14258399116\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142585934203\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142589638436\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142592433204\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.143257372537\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143257411235\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143257794755\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.143264688195\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143267878746\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143270298348\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143270540588\n",
"Lasso+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143272584311\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.143277757714\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143282849904\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor 0.143286186513\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143289844192\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143297513967\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.143299521473\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143301680332\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.143313339288\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.14331785594\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143318532671\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143324310986\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.143326820082\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.143330054759\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.143331936558\n",
"SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143336422306\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143337486097\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143338177732\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.143342748332\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143345255793\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor 0.14334773367\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143349518641\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143355588452\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143361360224\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.14336446497\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143366506727\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143372871001\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.143382532235\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14339004677\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143390601446\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143392050881\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143394626751\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143399079488\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.143414203226\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.143417304104\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143422256577\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.143425752059\n",
"HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143431965734\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.143440917045\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.143441283819\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.143444467203\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143446837948\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.143456335817\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143462176327\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143472071666\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143473447953\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.143474421726\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor 0.14347598989\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143483132141\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.143485965456\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143486237305\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143488528557\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143490287277\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143493964724\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.143502624071\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143514349843\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.143521856044\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143523449472\n",
"Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14352367999\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143532672312\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.143533640508\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143535192267\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.143537897747\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143547134438\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143547338742\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.143549357461\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143554786809\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14355618167\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143558354072\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143565974221\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.143566457903\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor 0.14357114709\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.143578047015\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.14357861032\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.143578984099\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143579920094\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.143582012703\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR 0.143583889102\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143586834276\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.143590066911\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143595126078\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143599530488\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.143610318962\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.143617702746\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.143619774961\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143623490771\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143624076778\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143635261976\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143635378162\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143641581748\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143641777248\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143644283561\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143646099534\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.143648271328\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.14364908385\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143653092559\n",
"ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143659036694\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.143660471359\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.143662232268\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143663386951\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143665457674\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.143665486824\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.143669856064\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143670176193\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143677314631\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.143679386526\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143680970204\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143685593991\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143687665363\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.143688761311\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.143689393791\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143691889442\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143696889523\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143698302001\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.143699321287\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143709175821\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143709736544\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.143712224976\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.143713330333\n",
"ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143713509571\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.143715309509\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.143716868927\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143716891119\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.143718619383\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143723936742\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.143724481492\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143731397377\n",
"Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143732491414\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.143738339037\n",
"Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143740683258\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14374349786\n",
"Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143747703594\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143754098545\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.143759162993\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.143760923996\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.143769154982\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143770410126\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143771432866\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143772513766\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143773089573\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143773359601\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.143776644718\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14377801813\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143783256642\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143784139169\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143784958289\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.143797747864\n",
"ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143802780232\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143810311807\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.143823571337\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143824604901\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.143841145677\n",
"ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143846423922\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR 0.143848280063\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143850581053\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143855522075\n",
"SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.143862686742\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143871533187\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143878762145\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143882209315\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143885324608\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143890238792\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143892698131\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143892702922\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143893874191\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.143895198696\n",
"ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143897406205\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor 0.143897811616\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.143900663366\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.143902258464\n",
"Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.143911218344\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.143913161935\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143913250996\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor 0.1439142447\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143919938201\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.143927114627\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.143928338339\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.143929606935\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143931030864\n",
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"TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.143933015869\n",
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"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144177624468\n",
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"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.144207344593\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor 0.144245624934\n",
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"RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144301330288\n",
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"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.144350111868\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor 0.144355355399\n",
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"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.144839161836\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144840490209\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144840674895\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.144843863128\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.144847743531\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.144856805085\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.144859334892\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144865265847\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.144874026217\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144874592887\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144877894766\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144883492749\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.144888260117\n",
"ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144891686933\n",
"ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144913742328\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.14492550939\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.144935133826\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.144947495288\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.144951182406\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR 0.144956881267\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14496545788\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.144977859488\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144986750522\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.144998553974\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor 0.145008344387\n",
"ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.145009114102\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.145010927198\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.145020768638\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145021241646\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.1450225982\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.145032560432\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145040761136\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145043472626\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.145049121818\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145049291494\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145059944902\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145060271937\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145061481209\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.145064616489\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.145081200906\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145086313595\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145094570057\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145097759724\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.145098618575\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145099586193\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.145106352165\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145111057854\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145115854067\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14512263427\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145124267739\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.145124520063\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145138757537\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.145145461659\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145151655255\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145157977587\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14516087178\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.145162562154\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145168644511\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.145168752842\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145170941438\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145175736851\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145177729299\n",
"Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.145190219539\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145190738698\n",
"Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.145198069435\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145200167547\n",
"RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145202236474\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145208356126\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14521929035\n",
"HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145219608053\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.1452395881\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145245230216\n",
"RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145247155082\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145262980404\n",
"HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.145263747055\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14526403856\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145264057324\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145264912847\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145268924151\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.145269396149\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145276877765\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145277006008\n",
"HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145285590991\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.145289484848\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145290091919\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.145306358291\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145310422358\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145312169715\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145319077964\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145322424817\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14532938485\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145334226879\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145334768947\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.145335335102\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145341305718\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.145344610368\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145359573992\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145360671679\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145365554444\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR 0.145373699269\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.145376609988\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145382317207\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145382662491\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.145394767501\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145406169521\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145409329937\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145412895793\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14541440064\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.145416578327\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.145419069196\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.145420916739\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145421936751\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14542285795\n",
"Lasso+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14542431899\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor 0.14543080782\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.145430834727\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14543363412\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145444525644\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145447025666\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145460148153\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145460260562\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.145472408762\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.145492506292\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.145495018685\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145496254892\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145506509574\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.14550730317\n",
"HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145508726629\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145521274869\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145521389102\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145531338755\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145541188734\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145554307214\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145557454566\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145572944708\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145586778404\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.145591257673\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145592887111\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145607689392\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145615426741\n",
"Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145617854036\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145619918973\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.145631061626\n",
"RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145653239889\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.145653822039\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145654308608\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145655983165\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.1456586428\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145683575553\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.145695867855\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145721697948\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145730755193\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145732008023\n",
"LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145738070859\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.145744623512\n",
"RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145748959978\n",
"HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.145749517806\n",
"Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145754421534\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor 0.145783633575\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.145815337691\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145821301764\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145822692778\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14583738618\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145844160002\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.145845252409\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145848055348\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145849995672\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145855572555\n",
"DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145859297914\n",
"Lasso+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145864017051\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.14587645689\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.145883083538\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14588357716\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145899384069\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145901167564\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145904655682\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor 0.145905008056\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145909196266\n",
"Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14591957508\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14592622447\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.145926732598\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145928716445\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.145960958543\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.14596581737\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145967292111\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.145971551917\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145972390542\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.145976705349\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145977359164\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145992838061\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145996561414\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146005230074\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.146015717469\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14601648091\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146038879759\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146066601954\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146069135177\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.146079101683\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.146090087322\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14609409942\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.146094158527\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146112764281\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146131448245\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146132828912\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.146137816808\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.14614201456\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146144847975\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146165851524\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146166179373\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146168668646\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146175101228\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.146187368261\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14618752424\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.146189144556\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146202733741\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146215109816\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146223679529\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.1462323579\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146233895557\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146239204592\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146251542949\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.14625484663\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.146260457712\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146262739112\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.146266790508\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146273922591\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146282304587\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.146294132466\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14630055304\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.146315442032\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146340105171\n",
"TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146347422173\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.146363138142\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146364661383\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146368508659\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.146375536679\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146380010197\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146395421856\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.14640005285\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146419774082\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146433555942\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146440545735\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.146451253485\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.146453847152\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.146460391675\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.14646827778\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146489396142\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146491289878\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.146506451618\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14651331446\n",
"HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.146517217546\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146518788169\n",
"TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146520073197\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146524738687\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146524767702\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.146528232298\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.146531670927\n",
"Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146558261203\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.146564383623\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146566629503\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14657270848\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146587059669\n",
"ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.146591166847\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.146599062158\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146638521198\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.146642151781\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.146644253922\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146669231704\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146677655827\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146686846199\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146699862905\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146700565946\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.146701108444\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.146704799657\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.146705334482\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.146709740347\n",
"Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146717310051\n",
"HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.146718338198\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.146733156322\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.146737551382\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.146740319942\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.146744926876\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.146753788901\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.1467623285\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146773432964\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.146777669721\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor 0.146783658593\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.146796314433\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.146800300786\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.14681787271\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.146823456521\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14683127705\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.146843666626\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.146843670776\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.146847921966\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.146885815315\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146886568862\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146896832122\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.146899524811\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.146902554028\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.146907543594\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.146907881349\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.146917187708\n",
"Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14691857428\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146919416104\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146920526783\n",
"ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146920872587\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146923079361\n",
"ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.14693160275\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146932959149\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146941882357\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146956952839\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146958553827\n",
"SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146962352853\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.14696471768\n",
"RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14696530098\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146981631173\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.146988168595\n",
"SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147016132602\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.147018655637\n",
"RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.147024069325\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.147032253571\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.147040269526\n",
"DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.147054476332\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147058851633\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14707200988\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.147074291465\n",
"Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147100198815\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.147103623532\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147105907597\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.147122955427\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.147139371109\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147159672543\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.147166841282\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.147184274584\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147210672676\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.147228814132\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147264617546\n",
"ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147267773433\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.147286496949\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.147287371055\n",
"ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.147313442808\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.147353601653\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.14736041967\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147360846727\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147376699773\n",
"ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147390420642\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.14739256513\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.14739740961\n",
"ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.147397488584\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.14740561572\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.147414756761\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.147416633857\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.147429068935\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.147429725787\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147431599499\n",
"ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147435951497\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147463095751\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.147477947388\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147481833878\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.14751047232\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.147520666167\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.147529039807\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147558230327\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147558682906\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.147564470464\n",
"ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.147569140934\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.147606195972\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.147622042335\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147626238404\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.147629776036\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.147632114728\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.147651962953\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147652631134\n",
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"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.151765902647\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.151801223829\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.151862941154\n",
"HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.151908815933\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.151928347893\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.151960338809\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.151981499828\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.152019075734\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.152023292719\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.15213453114\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.152153824588\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.15224416441\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152259238851\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152381087604\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.152388246518\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.152485918372\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.152490058695\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.152505982765\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.152655872183\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.152734976491\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.152825473182\n",
"ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152958071038\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.153126352058\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.153278722058\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.153302256408\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.153500731916\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.153555391942\n",
"HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.15355966004\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.153723916007\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.153750949434\n",
"DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.153827944268\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.153845745528\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.154125413923\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.154355973924\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.1549660747\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.15526726915\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.155376674022\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.155767189015\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.15591460802\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.156000491912\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.156251909883\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.156803915688\n",
"\n",
"Model Amount : 7\n",
"Lasso+LinearRegression+Ridge+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127926929743\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128338766579\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128498970421\n",
"Lasso+Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12850727843\n",
"Lasso+Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128617448673\n",
"Lasso+LinearRegression+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128678504517\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128743442145\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128775633939\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128858770117\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.12900298311\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129004367382\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129022532969\n",
"Lasso+LinearRegression+Ridge+ElasticNet+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129040822305\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129041447558\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129093216733\n",
"Lasso+LinearRegression+Ridge+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129130465301\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129168068582\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129188555534\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129277679294\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129281729259\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129288237441\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129288689771\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129308510433\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129377952805\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12939115631\n",
"Lasso+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129418120148\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129432022154\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129434187478\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129435160115\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129459445352\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.129491221358\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.129493844125\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129541989169\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129610007186\n",
"Lasso+LinearRegression+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129619120533\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129627733054\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.129656621495\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129678231651\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.129709222\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129711719948\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129720816743\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129752760551\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129761156612\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129771982561\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129781215914\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129796523322\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129800528613\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129820553039\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129823779897\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129843794773\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129863514496\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.129864168997\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129887771707\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129889366333\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129919426272\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129923036891\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129930668875\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129950764653\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129962325857\n",
"LinearRegression+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12996536869\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129970116372\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129971870267\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129986667786\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130001869824\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130006382485\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13016597028\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130171019467\n",
"Lasso+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130214024843\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130233332345\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130237954462\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130263781233\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130275420438\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130277198507\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130296526282\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130304735958\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13030638023\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130308886641\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130312907271\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130315091187\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130340459481\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130365889162\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.130372287151\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130376367905\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130377714537\n",
"Lasso+Ridge+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130393575025\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130395453253\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130409625403\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.130414056815\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130457404661\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130481790269\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130482756462\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130487448308\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130496419253\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.1305028745\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.130509588473\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130523165878\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130530649412\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130535782571\n",
"Lasso+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130539802256\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130539898552\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130562091892\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130569583968\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130583984499\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.130607486352\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130615791843\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130617146458\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130621464613\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.130622049421\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.130623089117\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130655522881\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130664825479\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130670999307\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130677358898\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130686837333\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130689634175\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130709014951\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13074064118\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130747980191\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130755333863\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130759758376\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130762163239\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130762639603\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130765750731\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130773773168\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13077562408\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130791490317\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13079794785\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130806802633\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13081622729\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130837706734\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.130839784197\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130848995983\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130863210087\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.130865042745\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130867718694\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130867788949\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130877875636\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130940066815\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130945530248\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130951804929\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130963647582\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13096536035\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13097160238\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.130979859321\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.130997165954\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131002475254\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+GradientBoostingRegressor+XGBRegressor 0.131018341488\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131020054324\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131020068675\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131030127168\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131048230733\n",
"LinearRegression+Ridge+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131059608228\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131064008366\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131069400968\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131093648037\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131096296783\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131102919546\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131104187597\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131112323069\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.131116499692\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131122119141\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131128872074\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131131119544\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131138843608\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131140028271\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131140794654\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131155263091\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131158959575\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131164459362\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131168022048\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131169858974\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131170185023\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131188619946\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131191664161\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131192593356\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131193701327\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131194861904\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131206253559\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131213381731\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131217747242\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131219322248\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131230660073\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131236596966\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131243355291\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131245260785\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131254315826\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131278146797\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131281327017\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131282581365\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131294124624\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.131295007516\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131300069491\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131305299348\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131309831513\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131312979685\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131314427808\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131314590034\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131316038898\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131321853311\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131325005862\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131327457544\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131331088389\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131332573631\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131333892571\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131336740579\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131355391563\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131360642911\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131366617966\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131377692112\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131383917699\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131392234236\n",
"Lasso+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131399433346\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131406729509\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131422758348\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131432906474\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131433771932\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131440853983\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.131444690468\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131448484853\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131452873655\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131456486149\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131464926828\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131466415469\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13147363966\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13149386512\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131519969371\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13152259383\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131530534495\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.131531417708\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131533090335\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131538692839\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131540374963\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13154150131\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13154190156\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131563912241\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13157681877\n",
"Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131578852593\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13158605926\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131592374408\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131594383373\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131608605074\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131616002528\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131618155031\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131620279023\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131624378483\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131630989602\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131636027847\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131642892453\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131643088664\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.1316437749\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131645318408\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131649601466\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131657858458\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131657989387\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13166589404\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131674919157\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131676383929\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131690295773\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131691016433\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131694040473\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131718483025\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.131719508437\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131731932703\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131733531129\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131734448147\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131737025583\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131743461302\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131744955409\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131764281906\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131768090473\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131773249608\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131780093023\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131781153499\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.131781790255\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131789229915\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.131795665376\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131797330802\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131801854637\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131804868156\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131816203341\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13182195474\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131823906994\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13182583372\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131826866668\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131833832582\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131839388628\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131840535122\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.131875370508\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131876220013\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131878497262\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131880749206\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131881422411\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131882277176\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.131884497583\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.131887161156\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131890910521\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131891857481\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.131892201707\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.131896338169\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131902889132\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131905688616\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131909356503\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+RandomForestRegressor+XGBRegressor 0.131923241267\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13192346842\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131928919955\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131934790779\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13193745062\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131948703713\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131949249929\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131951389372\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.131960913834\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131960930603\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131961174248\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.131964133993\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131968555635\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131968659804\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131978486992\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131983729271\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131988509384\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131991364468\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.131997776711\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13199900618\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132010272238\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132011775611\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132020270046\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13202241554\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132022685294\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132028431931\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132039355359\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132039836812\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.132046979267\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132047454301\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132058300592\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13205935078\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132065871551\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132071037865\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132074452538\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13207518096\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132088798728\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132089602488\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132090837087\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132095690981\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.13209769911\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132100703622\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132103052033\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132103662802\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132105225509\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132112715496\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132113684723\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132118809651\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132123907374\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132124407095\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13213398392\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132139847531\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132148449651\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132155497219\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132157307754\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132162705111\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132179775095\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132187985047\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132188217981\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132188682398\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132191445071\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132194953941\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132202447964\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132202734944\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132203860849\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132209743469\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132217586786\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132234487642\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.132235221182\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132237316768\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132237825726\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132247171732\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132247565566\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13225282095\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132255850894\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132257655068\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132258132855\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132259240358\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132261471328\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132266003797\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132277922492\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132278963237\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.13229518249\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132296627198\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132297012669\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132298368808\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1322994095\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132301919413\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132302071932\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132303022749\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132305789562\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132309072057\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132312840178\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132312926261\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132319979296\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+XGBRegressor 0.132320363232\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132320815567\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132327832674\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132330889537\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132331155146\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132332230683\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132332345797\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132336478424\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132339313504\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132343794046\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132346539138\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132348769338\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132349082592\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132350620007\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132354620076\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132359850206\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132375292348\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132376314163\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132384697164\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132385024415\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.132386621547\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132388568076\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132388924288\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132389899952\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132397622769\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132400868262\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132405506551\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132406680459\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132410596077\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.13241094268\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132412063387\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132412332681\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132416478814\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132417462258\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132418091397\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132422587604\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132425954481\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132438716587\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132443597028\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132448918351\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132470785079\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132472792047\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132479027266\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132480788023\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132486157834\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132487496512\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132488976507\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132489341626\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132494721058\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132496323966\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132498962375\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132499283189\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132500592937\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132504729608\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.13250700258\n",
"Lasso+LinearRegression+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132508331566\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132509560551\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132510360872\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132512938199\n",
"Lasso+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132516411147\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132516740344\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132518335739\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132520924268\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132523758476\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132529280831\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132538296603\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132541757078\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132546377236\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132549454436\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132555592492\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132561411303\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132561554468\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132562431998\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132563171591\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor+XGBRegressor 0.132563472636\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132564137269\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132564903646\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132567260275\n",
"Lasso+Ridge+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132568171793\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132569364952\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132569968269\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132571390655\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132588216126\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132599135369\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132609751026\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132611439492\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132612151935\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132614266005\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.132614543582\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132621038435\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132637431221\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132642522278\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132645248796\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132649077759\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132650685417\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132651160941\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132656938435\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132658014213\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132662747189\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132665188125\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132669200991\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132670984633\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132672515235\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132673913735\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132674862633\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132676169553\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+XGBRegressor 0.132683273965\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132684202967\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132689361591\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132694150157\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132697780955\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132700745048\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132701473823\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132709758079\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132718557286\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132718814523\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132723214597\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132729815369\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132736140295\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132747243809\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132749728508\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132750770602\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132751626653\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13276669271\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132770861091\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132771845818\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132772428624\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132772639318\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132773088517\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132777691914\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13278322619\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132785682956\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132791832707\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132792980502\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132802163114\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132802763785\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132806779289\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132814471024\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132817066121\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132823724319\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13283017038\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132840384395\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132845280651\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132847383197\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132854169428\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132861724464\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132862410362\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132870823778\n",
"LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132874802913\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132876736366\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13287739659\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132881244135\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132884545391\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13288840331\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132888531742\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13289613377\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132896157155\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13289751147\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132898540929\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RandomForestRegressor+XGBRegressor 0.132899208674\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132905556159\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132905960137\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132913656158\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132917107169\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13291818238\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132923018512\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132923631754\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132926988999\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132928093399\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132930888194\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132933339806\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13293518989\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132935246447\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132939592022\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132939826823\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132941439336\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13294499202\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132947515413\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13294773952\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.132950491814\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132951041212\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132954303422\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13296222363\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132962953287\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132965736232\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132967649033\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132968650155\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132971449255\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132973241454\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.1329757666\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132980465039\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132980605966\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132986908786\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132991512337\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132993962391\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133000457145\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.133011111788\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.133016903065\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133019456616\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133022246994\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133023509786\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133023834669\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+XGBRegressor 0.133023950828\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133025643539\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133026324287\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133026829267\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133028607996\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.13302878778\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13302932573\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133031922846\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133039619091\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133042758025\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133046651369\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133047376315\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133051394967\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133056124687\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133056714256\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133060833695\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133062095748\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13306681152\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133067937271\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.13307981429\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133085392963\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133088357578\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.133096456621\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133096945276\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133098963548\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133100346709\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133102048089\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133107318295\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.1331121368\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133113172947\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133117380411\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13311944729\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133121094343\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.133121367149\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.133122093299\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133125875011\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133129430603\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133134654214\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133134803603\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133141881488\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133142453277\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133143203713\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133148520076\n",
"Lasso+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133151667878\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133152610082\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133154439881\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133165935703\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133167348411\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133170847965\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133171949847\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133178359994\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.133180543884\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133181367286\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133182085788\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133182678631\n",
"Lasso+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133187125598\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133188157602\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133194559442\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.133200307242\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133202857235\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133206425836\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133212432217\n",
"LinearRegression+Ridge+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133213391795\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133216690442\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133224104602\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.133235039938\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133242629935\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133244004844\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133244566818\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.133258707904\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133259169838\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133261839172\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133263360582\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133266956588\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133274472288\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133276541379\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13328316041\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.133283423764\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133284810776\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133285214648\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133285915905\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133287163591\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133288114064\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133291643193\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.133299055153\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133299247162\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.13330184932\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133302089193\n",
"LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133312935654\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133315088089\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133316323519\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133316919443\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.133320972232\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133322519928\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.133323065764\n",
"Lasso+Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133324459501\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133326827397\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133328599365\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133330000273\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133330001884\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133330906259\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133332039955\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133332552503\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.133332883289\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133343050418\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13334353588\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.133343671117\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133347663854\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133353359099\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133359196035\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133364218159\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133364726513\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133365145758\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133366150712\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133367413473\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133372863162\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13337448557\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133374926264\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133378795004\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133379425892\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133380864152\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133383895414\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133384508807\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133385496346\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133388467591\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133389506929\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133395610639\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133398024389\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133399924242\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133400285112\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13340621374\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13341408462\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133421542346\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133426501347\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133426718764\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133433690802\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133434935583\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13343691539\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133445311623\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133450110229\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133461078847\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133461502802\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.133464061523\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133464298872\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133468220534\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133469837124\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133472966679\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133474393692\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133475468295\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133478699759\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133481944505\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133483903949\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133484403697\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133486541079\n",
"Lasso+LinearRegression+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133488088753\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133493447316\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133493487358\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.13349633176\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133496531328\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133498765703\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133499244712\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133505342952\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133507276071\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133509088162\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133509120283\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133509306494\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133510403588\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.133512486613\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133512766664\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.13351355119\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13351593564\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.133516783261\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133524075836\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133529356812\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133531009727\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13353228953\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133536459185\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133536898005\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133537477553\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13353983245\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133544282013\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133544678036\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133547602734\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133551968312\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.133552588421\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133553196338\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13355509539\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13355813753\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133559442134\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133560646904\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133561184089\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133566946624\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13356801449\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133569986734\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133571661627\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133574607677\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133578861025\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133578959205\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133584354062\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133591463571\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133593421102\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133596904175\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133597253476\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133598554524\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133599726671\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133602157128\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133604076261\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133605842193\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133610482279\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133619924355\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133621217934\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133621743607\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1336241151\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133626990512\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133631755614\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133633392677\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133638548218\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133642446011\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133647556821\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133650919175\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133656080971\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133660566811\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.133665641698\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133666910509\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.133668519738\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133670987554\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133677183516\n",
"Lasso+LinearRegression+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133678023443\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.133681502583\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133681862265\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133684390001\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133685080642\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133686765309\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133689357745\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133693307941\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133694812879\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133695340431\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133695504686\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133698314426\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133699024148\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.13370835168\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133712151104\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.13371461728\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133719447987\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13372026033\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133720966483\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133724560795\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133725435286\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133726130264\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133729570053\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133731106375\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133739596053\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133740657389\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133743200448\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133746350971\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.133748257059\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133752920277\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133753247162\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13375503047\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133759387622\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13376193116\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133763817629\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133764705691\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133766576062\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133770706389\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.133773548939\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.133776167039\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.133779188586\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133782406298\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.133782538654\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.133783732266\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133788679097\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133792067547\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133794704417\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133797995853\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133798351168\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133798805787\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133799957709\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133800672905\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133802747167\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133803906687\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133805569224\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133806359696\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13380915218\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133814977303\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133815982197\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.133824033427\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133824965713\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133826112638\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133830673889\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133833580874\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133834191076\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133837730836\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133837847861\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133843364299\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133848926265\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.133852667528\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133855270289\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.133855527283\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.13385728395\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13385730488\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133858200912\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133862876859\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133864456767\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133864728791\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133866456864\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.133868763088\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133871933611\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133872718725\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133872975779\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133874815214\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133879220661\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13388052295\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133882893191\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13388695988\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133889551029\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133890269802\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133892610094\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133895895571\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133897556397\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.133899231801\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133899351372\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.133900633704\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133902547508\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133903014218\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133904804411\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133905301206\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.133915516338\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133916918614\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.133919519878\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133925788276\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133928491072\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.133929235233\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.133934726358\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133936493936\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.133941471417\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133943269394\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133948125259\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133948534776\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133951144385\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133951496675\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133953420015\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133954242934\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133955099678\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.133961200314\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133962363169\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13396288157\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133970788506\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133974378504\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133974943311\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13397555116\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133979654952\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133987045278\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133988837576\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133991604458\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133992266905\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133995175122\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133995291204\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133995830292\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13399722582\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133998243818\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133998872357\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13400535748\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134008398487\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13400953153\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134012838867\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13401349262\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134015102835\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.134018132698\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134018678006\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134019955432\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134020441553\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134022784131\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.134028225193\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.134029023769\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134029364402\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134031374948\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134036996163\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.134040214775\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134041485777\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.134045425305\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.134046500646\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134475109414\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134477182655\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134481467297\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134487116276\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13448856008\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134488703334\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134489289231\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134490324612\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134490629246\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134491741981\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134493471142\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134494198835\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134495468423\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134495564371\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13449599204\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134497218118\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134498230502\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13449893257\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134499197756\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134501940881\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134502095157\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134508314792\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134509621179\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134511874915\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134513834852\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134514194844\n",
"LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134514320249\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.134515552929\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13451778156\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13451921535\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134523106621\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.134523288982\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134524291524\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134524296573\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134525135151\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134528027878\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13452994425\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.134530645121\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134532402581\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134534199648\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134535933059\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134536959196\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.134538291228\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134541140052\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13454341823\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134544795163\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134545849139\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134550809689\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134550875032\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134551708512\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134552670793\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134553603037\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134557730243\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134558624542\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134558995203\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134560907683\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.13456357148\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134566703122\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13456751587\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.1345712582\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134572161579\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134573754872\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134578361441\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134580956279\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134582039583\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134587056714\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134589003036\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134590771745\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.134592002877\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.134593929878\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134594893021\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134595333857\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134596318945\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134596459783\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134599327327\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134599976229\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134600090883\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134601470745\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134601963676\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134603850192\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134604999721\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134606814504\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134607737024\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134607883372\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134608553829\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134608917473\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.134609193037\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134611142251\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134612593724\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134612652678\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134616095179\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134616155195\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134619772644\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134624329236\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134629332529\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134629751771\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134631485625\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134634606163\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134636215786\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134643748814\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.13464740836\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13464888832\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134650203106\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134651153334\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13465365866\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134654117974\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134656319581\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134656678292\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134657204416\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134659788502\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134660019317\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.134663753362\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134667019679\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134667934754\n",
"Lasso+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134673494294\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13467397748\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134676081025\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13467922303\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134680273246\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134684303685\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.134684703895\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134687024422\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134692669681\n",
"LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134693519096\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.134700327701\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134702016523\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134703330464\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134705905502\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134710043716\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134710923699\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134712062228\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134713189037\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134715369081\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134716781558\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134718212931\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134719491764\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134720916445\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134722586867\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13472372483\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134726383741\n",
"Lasso+Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134730157911\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134734734354\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134737037149\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134739362068\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.134739985113\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134740897346\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134741013651\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134744229906\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134744969978\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134745189092\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134748300857\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134749209591\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134750951216\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.134752084961\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134754734978\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134764369878\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134765190671\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134766211738\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134767616403\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13477096188\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13477267364\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.134776344901\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134777123451\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134779541637\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134780467439\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134789834011\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134791029796\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134791978197\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134796708218\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134803345093\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134807716611\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134809272201\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134810484572\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134811836966\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.134814937037\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134815287981\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.13481543531\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134815686073\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134815983032\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13481715722\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.134817403116\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134818031647\n",
"Lasso+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134818976097\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134819081052\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134820863225\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134823589923\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134823920769\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134824419823\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.134826673089\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.134829195796\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+XGBRegressor 0.134836785047\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134837019114\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134839482136\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.134842488465\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134842813832\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134844000556\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134845314438\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134845909125\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134845946908\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13484744164\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.134848753097\n",
"Lasso+LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134851026569\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134851099856\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134852832178\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134854403749\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134854410951\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134859416604\n",
"Lasso+LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134859868769\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134860338801\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134860593233\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.134861998279\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134865533406\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134867575966\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134869051273\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134869276897\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.134875404298\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134878866605\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134878887414\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.134881398835\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134882775176\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134887060152\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134887477082\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134889112581\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134889780781\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134891643185\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134898074033\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134898939413\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134901350253\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134901820877\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134906644983\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.134907104346\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134907773885\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134910258432\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134910500689\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134910969062\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134916888791\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134921205342\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134921911226\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134925957166\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.134926126717\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134928529987\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134929301893\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134929524262\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134930523628\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134934869826\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134937875577\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134938458327\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134941465894\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134943554081\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134945312329\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134945620718\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134947303182\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134948297594\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134950551502\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134955277026\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134955575752\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134957391179\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134958876019\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134959794087\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134963417211\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.134965975567\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134968331275\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.134970448026\n",
"LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134972482393\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.134972501826\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134976469991\n",
"LinearRegression+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134976636005\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134978200185\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134979448656\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134979849085\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.134980748081\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.13498075085\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134982009269\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134982316418\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134984830594\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134987025453\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.134989995569\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134992558186\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134993192136\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134995905326\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.1349970531\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.134998038864\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134998884067\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135000673913\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13500127413\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135001294887\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135002420173\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135006477364\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135010417423\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135010693721\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135012934614\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.135016937523\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135018102925\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135019328757\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13502249973\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135023616262\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.13502542331\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135027066606\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135029746905\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13503193448\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.13503264348\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13503884908\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135039167566\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135040715231\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13504296162\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.135046398549\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135049323434\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135053406197\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135054587932\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13505878555\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135060356744\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13506271049\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.135065101495\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135065212053\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.135065432679\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor 0.135067278903\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135067376963\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135068635454\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135069804793\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135070589938\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135071296524\n",
"Lasso+Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135072027334\n",
"Lasso+Ridge+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135074135739\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135074488076\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135080066679\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135080944233\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135081310483\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135082570789\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135088329343\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135088605155\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135088673272\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13508883401\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135090435298\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135093137799\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135093749441\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.135094077367\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13509487374\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135096178995\n",
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"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135112546691\n",
"LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135114026078\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135114351625\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135114791344\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135115933824\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135118307463\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135120506054\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135120615673\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135131185747\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135132215327\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13514010582\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.135141015817\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135141406121\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135144302198\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135146727513\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135147586905\n",
"Lasso+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135155495329\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135157221895\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135158866354\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135158999038\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135162413558\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135162950167\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135163378349\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135164760722\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.135176057027\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13517624263\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13517692719\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135180602316\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135181519176\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+XGBRegressor 0.135183174884\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.135183482669\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135183510416\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135185127264\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13518674557\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.135188368762\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135191437847\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135193696195\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135195656538\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13519921113\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135200378262\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135201800134\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135205305918\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135208782619\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.135216112935\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135216365729\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135216404594\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.135217352113\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135220209031\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135221210538\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135224356588\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135225180249\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.135225840965\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135229958992\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135230232351\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.135231824862\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135240540704\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135241943898\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.135244069839\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135245259533\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135247747189\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135248860406\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.135250261469\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135251491745\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135254022949\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13525548707\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135256055614\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135256932496\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135258194216\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135259587115\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.13525985925\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135260012425\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135260748344\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135266447412\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135266661515\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13526700038\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135274948782\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135275010149\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135275669641\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.135279716909\n",
"LinearRegression+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135281914834\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135282903069\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135285038362\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.135285178993\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135286197889\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135288571356\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135290227972\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135290624591\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135294558984\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135295487193\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.135297553301\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13529921557\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+XGBRegressor 0.1352993144\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135300808291\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135302957925\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135303865615\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135304401547\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135306855999\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135310539823\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135311371088\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135312424839\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135313931561\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135315251972\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.135318762766\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135319555314\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.135323154476\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135324696988\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135327165775\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135329032581\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135329775513\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135330261657\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135337156792\n",
"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135337869753\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135339738025\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135340528145\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135343179808\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135343918745\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135343956662\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135344584823\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135347703019\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135351708902\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135354561577\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135354945316\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135355356765\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135355438032\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135357130361\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135358126134\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135358470322\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135359421841\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135359692931\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135361693518\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135365296829\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135365538193\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135367206195\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.135368936861\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.135370978277\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135373256056\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135377708083\n",
"Lasso+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135377893529\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135377913925\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.135378598924\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135380221084\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.135383889647\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135386022701\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135386170987\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135387979804\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135390805586\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135392611129\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135396428052\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135396574368\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135401957844\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135403172109\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135404291662\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135405759908\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135406525644\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135407747118\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.135408083817\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135409429873\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135409622641\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135415670369\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135416292953\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13541896108\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135419473339\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13542382476\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135424095469\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13542668923\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135427400089\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13542913763\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135432186288\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.135434003992\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135434101347\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.135435445213\n",
"Lasso+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135436319899\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.135436852074\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135440307299\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135442102861\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135442744125\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135444939789\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135447454562\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.135450742648\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135451093303\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135452228763\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135453523099\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135456089379\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135459599241\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135463462998\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135463890634\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135465456754\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135467634593\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135470002933\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13547124596\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.135474592298\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135474895992\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.135474906485\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor 0.135475835642\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135476690226\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135478864049\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135479880848\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135480173725\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135480288035\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135486698641\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135487291062\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135489627206\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135489740874\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.13548994138\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135491147636\n",
"Lasso+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135492644292\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135492960794\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135493310068\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13549664709\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135497083163\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135499764523\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.135500235631\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135503636101\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135504627395\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.135505739669\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135506483467\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135507307733\n",
"LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135507959829\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135508169011\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135508179123\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135508295625\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135509237912\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135512149244\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135512453124\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135513255133\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135514738495\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135517702874\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.135519105728\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135520617966\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135521740415\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135522549157\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.135525563135\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135527695003\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135528111448\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135528505713\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135529414376\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135530926815\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135533041575\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+XGBRegressor 0.135533989123\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13553571565\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135537000669\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.135538102711\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135538941444\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135540913441\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135543862687\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135544263903\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135545405123\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135545612998\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.135546115512\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135548380074\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13555056668\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135551332054\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135553798839\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135556586852\n",
"Lasso+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13555802952\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135564711767\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135565489566\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135567504255\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.135574416849\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.13557647692\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135578467855\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135582621962\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135583186405\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135583367816\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135585161117\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135587932394\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135588090721\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135591237478\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135591580925\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.135591854712\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135592559102\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.135593351925\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135594513903\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135597907388\n",
"LinearRegression+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13559809954\n",
"LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135598906769\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135599117096\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13560069389\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135602718719\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135607278154\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135607940707\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135612131554\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135620644219\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.135624740716\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135624944618\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135625853327\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135628120552\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135629683026\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135630122004\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135634192619\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135637392683\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135637673628\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.135637795378\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135638312068\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135639975555\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135641059192\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135642588838\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135642861107\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135643131107\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135644120276\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135644689665\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.135648487799\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.13565121975\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.135652667429\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135654846936\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+XGBRegressor 0.135655969086\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135658199256\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135661136324\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.135661201086\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135664535757\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.135666165098\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135666703125\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135666774632\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135667990582\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135669809573\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135670507188\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135671803641\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.135672393975\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135675737834\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135676042286\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135676825866\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.135679566635\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135680008887\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135680826279\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135684298116\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.135685563355\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135687699915\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135692699337\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.135694764539\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13569735705\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135697954199\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135698833989\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.135700904037\n",
"LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135703634511\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13570539623\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135705397772\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135707437407\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135708658788\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135710432592\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135714526932\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135714954385\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135717657777\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135717904876\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.135718621539\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.135719184842\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135719309362\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.135722005212\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135722073458\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135723159016\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135723784976\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135723952556\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135727922982\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135727995796\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135733615457\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135735248614\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135736355291\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135737753936\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135738888143\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor 0.135741938638\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135742975437\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135744050677\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.135744760364\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13574664485\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135749778318\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135751729313\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135751779007\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135752411698\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135752493219\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135753017891\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135754400694\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135755430953\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135756070568\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135758239799\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135758666682\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135764156342\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135764417407\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135768541763\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13577547219\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135775483767\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.135777164433\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135779250802\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.13578060295\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135780905707\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135780910976\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135781981129\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.135784007702\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13578821446\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135789514025\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135791812503\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135795291884\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135795323024\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135797016764\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135798965914\n",
"LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13580123369\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135801353124\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135802122852\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135803289866\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135804093775\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13580652468\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135808286\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135808394095\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135810708858\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135814384494\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135814537159\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135817044328\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.135817166982\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.135817479716\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135817667469\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.135818241755\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135818419798\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13582617377\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135827453264\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135830282257\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135830817246\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135833389584\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135835337726\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135837489583\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.13584386647\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135849113714\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.135849637674\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135852224852\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13585261419\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13585369883\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135856871972\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135856876541\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13585749905\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135858185858\n",
"LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135859872962\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135863042751\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135871539253\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135874949969\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135878846981\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.135879548569\n",
"Lasso+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135879684057\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135880178921\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135881716053\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135882718836\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.135883446153\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.135889100746\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135890012526\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13589217508\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135893457067\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.135895129262\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.135899693618\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135900049388\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135900878127\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135903416177\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.135903494024\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135907108906\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135908963877\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135909457091\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135910572943\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135912755769\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.135912881274\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135913102599\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135915737623\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135917785419\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135918638156\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135919522871\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135920973227\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135921018251\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135923517123\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.135924463146\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135924917669\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135925231275\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135931590764\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135932052287\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135933311942\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135934640631\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135935300311\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135936249355\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135936452163\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135938095183\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135938540837\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135940986809\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135943249499\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135944653168\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135945081441\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135951294891\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135952317833\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.135953032762\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.135953394725\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135954030872\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135956059389\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.135957056151\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135957391578\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.13596040663\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135963023165\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135967543176\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135968782415\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135971287676\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135973348602\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.1359750594\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135978237522\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.135980437892\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135981344409\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135981924077\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.135982895967\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135984186088\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135990594791\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135991290137\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135992617414\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+RandomForestRegressor 0.135996764629\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13599727198\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135998688607\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136000519438\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136001857966\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136001946846\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136002950858\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136005028875\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136006978642\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136009085891\n",
"Lasso+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136009865906\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136010018599\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136010109369\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136011112744\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136012564409\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13601284391\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136012986402\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136013616348\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136013719504\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13601501796\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136016043744\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136016121946\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.136018363032\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136020810884\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136024010174\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.13602579119\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136026863969\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136026957739\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136027718262\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136030237722\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136031635596\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor 0.136032845689\n",
"LinearRegression+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136032864087\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136033088738\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136033110384\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13603434912\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136035426224\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136036377705\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136038848848\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136040877448\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136042608143\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.1360442172\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136046074903\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136047702495\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136050526306\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136052335312\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136052680796\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136052818915\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136052863597\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136058149678\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.136061722455\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136068377557\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136068716557\n",
"Lasso+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136068854005\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136070329478\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136071768538\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136072171939\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136072321877\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136073594101\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136073850959\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136077046692\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.136077749361\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136078305588\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136079670874\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136080703029\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136084139185\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136084680784\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136086314169\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136086408936\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136086545373\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13608729187\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136087620098\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13608775013\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136089814071\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136089922204\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136090169058\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136091917197\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13609302221\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136095058191\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136095577835\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.136095877515\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136097322727\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136097597762\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136098910288\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136101026784\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136101825048\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136103748918\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.136103834321\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136109013078\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136111358777\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.1361132406\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136118360825\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136118738203\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136122440717\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.136124469271\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13612456614\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136126565101\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136128573132\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136129850642\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136129917987\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136131730819\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136133432037\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136135280036\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136136768023\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136138160103\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136138797602\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136143454561\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136148369783\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136148748507\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136151683954\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136153249988\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136153281416\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136154085953\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136154128287\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136155580726\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136156630309\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136161086575\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13616296821\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136166103499\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136167745519\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136173326785\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1361736782\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136175767247\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136176449218\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136177449955\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136177924564\n",
"LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136179458564\n",
"Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136179758363\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136190120914\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136190282663\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136193741942\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136197020013\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+XGBRegressor 0.136201056429\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136202104678\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.1362033811\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136203574069\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136206725137\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136208208109\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136208316898\n",
"Lasso+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136209449451\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136209557255\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136212376285\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136215263793\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.136216306446\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.13622032983\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136220516368\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136221149554\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136222595159\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136224164952\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136224695087\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136228787589\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.136229196465\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136235614251\n",
"Lasso+Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136238120938\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136238156221\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136239033135\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136241764149\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136242849521\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136242883748\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136243924293\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136243949449\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136244796588\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136246148237\n",
"LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136246917236\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136247097799\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136247324523\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+XGBRegressor 0.136248956533\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136251697911\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136253492839\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136254145687\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136254204609\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136258137104\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136258578304\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136258912636\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.136261572978\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136262266802\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13626291072\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13626463313\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.136266711267\n",
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]
},
{
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"text": [
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"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136541740921\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136544868926\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136548434275\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.136548750804\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136553536471\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136554720472\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136555331501\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136556977689\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor 0.136557100561\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136557162534\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136557662028\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136562956989\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136564027549\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136564311209\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136564705448\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136565404578\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136566690992\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136574855577\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136575153846\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136576260931\n",
"LinearRegression+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136577061551\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136582311199\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136583595302\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136584394507\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136584636298\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136584873301\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136586010247\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.1365889436\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+XGBRegressor 0.136591408121\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136592462162\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136592925044\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136593907039\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136595081143\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.136597749094\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136597987077\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.136600647408\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136601930277\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136602189124\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136603344767\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136603802463\n",
"LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13660443106\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136607249383\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136608301914\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136613266011\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136614028259\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136615833782\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136616387998\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136617004138\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.1366187275\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136621254981\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136621297607\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136622260916\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13662368487\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136625808227\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136626382547\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.136628460561\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136631832025\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136633766053\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.1366350027\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.136635710397\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136637415052\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136638984798\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136639522431\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136642030047\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136642919306\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136645513603\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136647496449\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136647982443\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136651154161\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136651945149\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136656262479\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136657749389\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136658437241\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136658599458\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136660377671\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136663721906\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136666285949\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136666946058\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136669036324\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.136670212591\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136670573683\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136674657245\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136675007477\n",
"Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136675875385\n",
"Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136676220233\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136678822076\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136683339071\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136684033566\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136685166023\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136686870865\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136687197892\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136688045867\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136688795468\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136689501375\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136690410128\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136690549866\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136691037974\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136691266295\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136691746837\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.136692204539\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136692344202\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136694465593\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136695834191\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136696079491\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.136696492865\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136697522144\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13669915538\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136704287528\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136704544126\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13670486142\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136704962991\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136705378471\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.136705447975\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136706543531\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136707142279\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136707554331\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136708588551\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136709125318\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136709926608\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136710440169\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor 0.136710526919\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136710845192\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.136710950373\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136713278786\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136715734679\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136717323983\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136719614825\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136722188984\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136727522055\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136728266677\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136728867345\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.136730020054\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor 0.136730886776\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136731492162\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136733855531\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136736516847\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136738698525\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13674021536\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136744144021\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136744571456\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136744718892\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136744944094\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136745090469\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136745249254\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.13674531187\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136745588787\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13674589426\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136746034852\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.136748540574\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor 0.13675035776\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136751319261\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136751415334\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136751770533\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.136752517266\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136753406914\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136753562642\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136755996574\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.136756504293\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136757630313\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136758781424\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136760060093\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13676080129\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136762797077\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.136762923648\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13676303113\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136770044128\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.136770162197\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.1367702355\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136775561003\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136776422866\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136777165159\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136779946674\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136780306901\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136786041441\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136789162178\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136789333604\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136790058313\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136791611081\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136791861222\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136792826828\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136792852291\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136793067009\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor 0.136793131226\n",
"Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136793934565\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.136796006239\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136796173069\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136796953167\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136797526587\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136797975475\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13679815238\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136799936749\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136805597582\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136808387911\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136811466653\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136819297872\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136819359371\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136819454217\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136821659406\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+XGBRegressor 0.136827044935\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136828223959\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136829772453\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136829944011\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13683341902\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136836387411\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136839268639\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136839924402\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136841964952\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13684373377\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136846177687\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136847718678\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136849777472\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136852154148\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136854869478\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136856129797\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136856460368\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.136857272391\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136858769315\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136859089754\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.136859353743\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136862570473\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136863919416\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136865031461\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136873291292\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.136874251734\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136877833786\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.13687856255\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136878875372\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136879921072\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136880010147\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.136880799781\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136880809812\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136881510254\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13688333861\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136884802324\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.136889737008\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13689150565\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13689210477\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136893015266\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136893396205\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136893480777\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136894734672\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136896336922\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136898458504\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.136899095366\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136899832302\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136900311849\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136900332655\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136901013107\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.136902166283\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136903659012\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136904168341\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13690515577\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136905579311\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136906638542\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136907901281\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136909421321\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136911570096\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13691671024\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.136918126132\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136918494372\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136921400193\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136921541809\n",
"Lasso+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136922695023\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.136925629059\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136926077095\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136930726181\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136932511328\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136933872965\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136934156507\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136934592686\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136936223341\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136941845779\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136942916115\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136942957934\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136943246345\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136946573476\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.136947994067\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136951710778\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136955166492\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.136955284464\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136956902128\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136957167203\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.136960167201\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.136960422522\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136960738026\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136966526192\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.13696999473\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136970187472\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136977611356\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136978753813\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136979588784\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136981727944\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136982258239\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136982666101\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136982763167\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136983308239\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136988474575\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.136988624415\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136997405942\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136997856851\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136998671012\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136999673606\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137002844331\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137007669349\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.13700794944\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.137008669547\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137009368654\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137013159812\n",
"Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137013717917\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.137014315808\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137016846032\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137018520185\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137018862474\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137020044174\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137020295532\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.137022229941\n",
"Lasso+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137022353455\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137025022657\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor 0.137025512353\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137027500438\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137029257287\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137035814406\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137037401225\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137038102227\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137038294225\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137038837869\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.13703916674\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.137041370728\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137041714382\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137043636172\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137043817347\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1370441546\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137045856245\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137046630857\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137047562729\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137047618956\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137047821462\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137047986407\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137049860141\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137050403379\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137052363409\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137052575759\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137052701305\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137055007402\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137055703985\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137056297672\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137058122145\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137068557262\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13706998415\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137071190615\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137072247639\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137075766257\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137077607809\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.1370798918\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137080316595\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.137083387855\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137086604697\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137086617745\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137088425848\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137090249432\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137093963006\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137096418087\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137096942753\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137096986618\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13709732999\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.137097869546\n",
"Lasso+LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137099963056\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137100768333\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.137102374275\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137105493646\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13710593876\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137109750887\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137111247915\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13711159861\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.137113135828\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137114418098\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.13711510597\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137119159753\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137119361362\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.137120882429\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137121663167\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137122403032\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137124226641\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137124421594\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.137126151655\n",
"Lasso+LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137131470622\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137132270319\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13713680858\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13713764866\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137137797193\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137138161722\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137138173874\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137138525082\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.1371418164\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137141881494\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137146685782\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137148062403\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.137156531507\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137157525611\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137159480695\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.137163655589\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137164758341\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137164832503\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137165030579\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137165245891\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137166831248\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137166834215\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137168223983\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.137168573297\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137168812124\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137172059368\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137172455413\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137173533238\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137174192677\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137174615224\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137175009738\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137175466189\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137177124503\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137177829716\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13718101154\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137183929887\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137185917562\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137189575912\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137190048467\n",
"LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137191440212\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137191799804\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.137193039395\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137195441908\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137195834696\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137199394727\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor 0.137200634423\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137201848018\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137202497415\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137205175157\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137206288833\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137209712985\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137210117493\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137210379841\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.1372117298\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13721207033\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137214079647\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137215267937\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137215527917\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137216322918\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137216448901\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137216922753\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137217230741\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.137218653401\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137219207542\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137220261703\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137223404152\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137223848887\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137232153865\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137234745515\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137234804091\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137235932896\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137237868635\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137238276821\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137239828682\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137241515827\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137243449854\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.137244113697\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137244410329\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137245260439\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137245903395\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137245917387\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137247389487\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.137249335574\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13725084399\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137251708792\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.13725174454\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137251918233\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137253266741\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137255847452\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137259516377\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137260175527\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137262619673\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13726297043\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13726361793\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137266407925\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137268125331\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137270857459\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137271274405\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137275297347\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137277073396\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.137277499833\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137277534664\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.13727793585\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137279368146\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137280033007\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137282926444\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137284655289\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137285147825\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137285250349\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137285666383\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137288465482\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137289498118\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.137290301213\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.137290768548\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137295181286\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137295236868\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137296316375\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137299507594\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137300534462\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137301261029\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.137304501375\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137304526432\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137305836744\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137310786387\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137311897518\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137312875553\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137313101698\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137314971038\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137315092057\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.137316050187\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137318096241\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.137322950787\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137323680631\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137324064716\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137327953896\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13732802033\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137331478655\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137332462159\n",
"Lasso+LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137335154385\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13733649141\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137336953801\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137342845602\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137343206464\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137343528814\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13734376977\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137343793575\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137344107462\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137349217502\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137349480542\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137350041184\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.137352331994\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137352425049\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137352603682\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137353551219\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137354495668\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137355842227\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137355990899\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137357051132\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137361125646\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137361572895\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137362237783\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137364172759\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137364191531\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137368820243\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13736914644\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1373702387\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137371023925\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137371334446\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137372332336\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137377541349\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137380058933\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137383707211\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137386957794\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137387445977\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.137389272327\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137390590373\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13739132119\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13739339551\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137393454504\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137393867311\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.137395300987\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137397100888\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137400223758\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137401277735\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137405105439\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13740560764\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137405920312\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor 0.1374098966\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137413469264\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137415108059\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137418024304\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.137418040096\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+XGBRegressor 0.137422634063\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137423491557\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137425773729\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137433431471\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137433505778\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137434873813\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137436589688\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13743739814\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137439160914\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137439965501\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137440247713\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137450794875\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.137451095312\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.137452140496\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137453725209\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137454354833\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137460485378\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137461921984\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137462803297\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.137463388782\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137466630457\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137467299069\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137468351357\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1374692952\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137470783697\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137471667587\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13747315921\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137475287658\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137476749143\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.1374770375\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137477448874\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137480026367\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137482574797\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137482837343\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137484074663\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137484739932\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137492163011\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137496424017\n",
"Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137496727195\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.137497748683\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137499200925\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13750103848\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137502092177\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137503633272\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.137505432433\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137507610448\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137508718053\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137508848209\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137509151419\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137510249521\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137511534158\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137511625799\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137511715342\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1375138699\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137514503617\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137514826047\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137516485727\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137517138242\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.13751794464\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137518530616\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13751856662\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137519231325\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137519500627\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137520101971\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137520843431\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137524371439\n",
"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137524434421\n",
"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137526045078\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137528920607\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.137532684522\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137532865443\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137537100875\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137537436338\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13753785854\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137538213125\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137538716621\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137539993362\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137542020082\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137543405373\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137544147718\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137545264897\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137545704317\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137548714797\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137552210545\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137554808716\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137556908307\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137558466848\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137559197703\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137561559145\n",
"Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137561715705\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137561859635\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137564384342\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137565768068\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137566317526\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137569000894\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137569513559\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137571153332\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137573126744\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137575505071\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137577132572\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137578016562\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13757861217\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.137580752417\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13758205601\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor 0.137589020519\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137589476171\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137592064713\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.137594476062\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137595587979\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137595673337\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.137598674112\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137599889692\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137600989194\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.137601189562\n",
"TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137603654761\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137603764883\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.137603813953\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137604241627\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137606235536\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137607699617\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137611069691\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137613395023\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137614242975\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137615813498\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137616566105\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137618255638\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.13761914532\n",
"Lasso+LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137619503483\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.137622142758\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137622903553\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.1376231421\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137623667771\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137624136868\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137626580275\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.137627036323\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.1376430029\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137644054587\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.137645040978\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137647914318\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137647964206\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137648245411\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137649027985\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137650309074\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137650709087\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.137652630206\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137652834577\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137653075843\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137653240106\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137653858391\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137654442362\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137656209294\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137659360736\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137659762701\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.137665564522\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.137671662206\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137672192935\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137672889764\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137674879424\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137675090731\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.137677699157\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137677914415\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137678574012\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137682038222\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137684847876\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13768840306\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137688865613\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.1376894362\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137692035158\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.137692555866\n",
"Lasso+LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137692882405\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137693190831\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137693525854\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137697913016\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+RandomForestRegressor 0.137698966587\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137700228935\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.137700561571\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137701433231\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137701821975\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+XGBRegressor 0.137703679066\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137704688725\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137704927823\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1377053433\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137706842422\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137708219541\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137710914116\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137711981741\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137716037126\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1377160871\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137718436567\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137722456841\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137722781409\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137727657653\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137728968024\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137730460063\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.137730836919\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137731267653\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13773270425\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137733722119\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13773372832\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.137736343581\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137738970463\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137739742689\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137740636964\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137742515473\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137743257403\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137743281326\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137748515758\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137750762624\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.137752713886\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13775397345\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137756594358\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137758043842\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13775854268\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137758701887\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.137759637042\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137763465005\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137765275885\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137766289168\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.137768350354\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137769021037\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137769663614\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.137770381346\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137771416409\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137771845342\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.137772243192\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137773485472\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13777498063\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137775641579\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137776473748\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor 0.137777683385\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13777787532\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor 0.137779017858\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137779467754\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137779470284\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.137779559795\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13778165304\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137782640473\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137784506412\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137785664465\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137785993099\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137789590632\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137789879982\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137790788702\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.137791540266\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137791717017\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137793315169\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137795373461\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137797206344\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137798114638\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137799982414\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor 0.137800019525\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137800258357\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137801075202\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137802496798\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137802596228\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137802998437\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137804247158\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.137804992926\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137805119994\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137811988747\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13781251043\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.137813297912\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137814594463\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137816390475\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137820218135\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137820948465\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137821956769\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137829148633\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.137829221079\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.137829983317\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137830383795\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137831226291\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137831454687\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137831913955\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137832538263\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137834070615\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137835062459\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137835604366\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137836926587\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137837055296\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137837771685\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+XGBRegressor 0.137843512912\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137845338412\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137845819993\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137846709169\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137846821744\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137847278635\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137848191028\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137848227265\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.137848804513\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137849583334\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137849767812\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137852356281\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137853017791\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137863330542\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137864338411\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137865694205\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137865772789\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13786630109\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137866770952\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137867203451\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13786816322\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13787156439\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+RandomForestRegressor 0.137871881824\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137872493429\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13787612073\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137876599504\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.137877057812\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137878640669\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor 0.137879588442\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.137881036124\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137884674387\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137886360015\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137886431204\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137887205456\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137887244011\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137888398101\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.137889616218\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137890634349\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137894066018\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137894811012\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137899607387\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.13790256491\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137903576459\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137904109871\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137904158293\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.13790687006\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137907202764\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137913127592\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137913209931\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.13791415165\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137914340641\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137914425246\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137914932382\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.137915361269\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.137917074771\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137917265199\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.137917352031\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137920485897\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137921859321\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137922807361\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137923450578\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.137923552309\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137925898398\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137927650349\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137927784288\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137929209926\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137930759075\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137931499538\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.137931844078\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137934862797\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137935852897\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137936082827\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137936293247\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.137936973374\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137937560367\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137938317359\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137938608207\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13794077476\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137942130322\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137942251629\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137946308162\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.13794690943\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.137947927629\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.137949548118\n",
"Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137959879639\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137961116802\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137961412402\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137961895703\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137964165241\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137966107972\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137967443469\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137970772367\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137971475282\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137973088243\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137974892108\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.137975894424\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137976166036\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137976633243\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137976958957\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137977593745\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137978707483\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137981168402\n",
"LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137984133368\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137984137096\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137984973397\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137985773359\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.137986891499\n",
"LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137987519174\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137987657151\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137991556449\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137992398758\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137993509197\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137993896798\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137997541316\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.137998666098\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137999229626\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13799962689\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.137999629527\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137999810157\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13800642703\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13800708175\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138015133971\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.138018429232\n",
"Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138021593785\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138021712971\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138022710013\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.138023485944\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138023772456\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13802560002\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138026265501\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138029857739\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.138031345744\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13803443834\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138039864116\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138040596801\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.138040979575\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138044678214\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138046049837\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138048893705\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138049355985\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138050105616\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor 0.138051705949\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138053132992\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13805437636\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138057024408\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138061950703\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138063359157\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138064171898\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138065982017\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138066149736\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138066510457\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.138068746331\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.138070196996\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138072102078\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138074755309\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138076478524\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138080448823\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138081315345\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138083392179\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138084939962\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13809046152\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138090868332\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138092868074\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138096493023\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138096606877\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138107262169\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138111062868\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.13811347323\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138113793253\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138116637675\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138123055294\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138124413741\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138124777622\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.138128880671\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138128940075\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138133666663\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138133814572\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138133872392\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13813539631\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.13813705906\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.138137973142\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138138149145\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138139615415\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138141588706\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138142129889\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138143644205\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138144016929\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138145720426\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138145816357\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138149070534\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138152929888\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138154460619\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138154610672\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138155815111\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13815646722\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138159457728\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.138160232001\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138162145337\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138166344659\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.138171387232\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13817299528\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138173058665\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.138174929978\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13817730545\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138177854692\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.138178524424\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.138178947017\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138179262265\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.13817980327\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138180279814\n",
"Lasso+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138180558593\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138183531591\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.13818444803\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138186606753\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13818866784\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138190200766\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.1381926585\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138194713495\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.1381972093\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138198565568\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138198704911\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138200388665\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.138201223369\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.138201327823\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138204992416\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138205057219\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138205858834\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138208092931\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138208755219\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138212541173\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138214153813\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138218719131\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138219578947\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138222387356\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+XGBRegressor 0.138223526416\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138230462416\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138232239057\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138236132164\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor 0.138236396126\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138236921959\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138237189079\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138243574976\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.138250772205\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138251142451\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138251170276\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.138251644376\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.13825719331\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138257333218\n",
"ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138257387617\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.138261368365\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.1382647054\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.13826565809\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.138266617204\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.138266880621\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138267505833\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138268029077\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138268900042\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138269676382\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138272200733\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138274483072\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138276053096\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138279744424\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138281278357\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.138282333099\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138287171346\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138288805879\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.13828930343\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.13828967028\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13828971175\n",
"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138291611814\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138293016339\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138293108859\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138293271929\n",
"Lasso+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138293351792\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138295316185\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138295400979\n",
"Lasso+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138298432329\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138299228752\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138300986622\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138302465723\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.13830258577\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138302846622\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138304074392\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.138304339281\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138306194481\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138307501641\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138311976905\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138312294526\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138312440014\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138314996457\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13831680756\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.138319193562\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.1383197991\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.138320368553\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138322043136\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.138326066562\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138327381894\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.138327952854\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.138328611221\n",
"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138329837947\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138330964061\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138332376851\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138335818248\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor 0.13833998527\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138341293591\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138342369084\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138343892206\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.13834437369\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138349377286\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138354852154\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.13835628307\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138356466788\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.138356905337\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.138360455053\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138363507191\n",
"Lasso+Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13836431343\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138366919094\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138368986404\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138369465575\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.138369824541\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138370189587\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138373339579\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.138373796567\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138374058231\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.138374171827\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13837464919\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138376476144\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138382818433\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138382942452\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138383286014\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138384618038\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138388407867\n",
"LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13838855838\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138389098417\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138389467468\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138392812825\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138393399336\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138394265897\n",
"Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138396013478\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138396415922\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.138396514342\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.138398958206\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138401125877\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138403733559\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138405245426\n",
"LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138415847822\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138418198234\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.138419497263\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138420984916\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138421285962\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138431028975\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138431840983\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138432604322\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138433181731\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138435865788\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.138436252644\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.138436505495\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138440083458\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138440234449\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138440300537\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138442434996\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.138443288882\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138446563914\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.138446985957\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138447246803\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.138447937096\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.138449041334\n",
"RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138449043275\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138449049673\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138450983285\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138451791034\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138453372553\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138453997661\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138454796152\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138455329704\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138455770873\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138456307455\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138461599614\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.13846301145\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138463567812\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138465757302\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13846695752\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13846705724\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13846798399\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138469401834\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138469897858\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138472986803\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138473272871\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138475406664\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138475670031\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.138475670217\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138482052733\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138482145449\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13848459366\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138487178746\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138487445042\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.138489957511\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138490947231\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138493494792\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.138494691439\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138495663263\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13849618742\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138496683647\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138497442128\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.138498477966\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138499889035\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13850392815\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138503939244\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138504054087\n",
"LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138505111899\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138505720823\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138506842576\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138511240497\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138511855079\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.138512859533\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138514508974\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138516119469\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138518689037\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138519413212\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13852065798\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138521125803\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138521309449\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138522264109\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138524452646\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138525955107\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138527004791\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138527052648\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138527844229\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138528898662\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138528926815\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138529955926\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.138531693458\n",
"LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138531885906\n",
"LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138536461159\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138539086482\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138539492746\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.138540733176\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138540863049\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138542542059\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138542979511\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138543043073\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138548059249\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138548461066\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138549206617\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.138550327516\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138553357162\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138554129792\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138556626033\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138556830224\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13856009757\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138563233322\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13856459825\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138565680204\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138565949327\n",
"Lasso+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138566794307\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138567578125\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138567679472\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138568520499\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138569156724\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138571692548\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138572065538\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13857346715\n",
"Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138574728991\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138575449115\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13857872699\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138580132824\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.138581110333\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.138582574656\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138584546603\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13858878798\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.13859229782\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138594727193\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138600019453\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.13860277819\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13860280348\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138602883428\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138602902294\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138606662814\n",
"Lasso+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1386067174\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138606866802\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138612506592\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138612930184\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138613971262\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138615204168\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138617117262\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138617470877\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13861897048\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.13862154727\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138624787862\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13862796334\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138633169389\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138634621718\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138640515573\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138640634282\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138644785387\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138645790673\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138646069358\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138646114644\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138646162679\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138646404275\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138646806343\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138647779632\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138647803992\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13864835269\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138650230321\n",
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},
{
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"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.138891424008\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13889376923\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.138894496878\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138896382894\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138897630632\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138899256478\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13890139078\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.138902105188\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138902908022\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138902930247\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138903640465\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138903796116\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138904049903\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138904134138\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.138909188117\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138909317781\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138909634762\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138910960862\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138911899411\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138913145175\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138915049874\n",
"LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138916547073\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138920956803\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138921138667\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138922459601\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138923038007\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138924835587\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138925741722\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138927614952\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138930868562\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138932148824\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138932612363\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138932613488\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138936118855\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138937125316\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138938947482\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13893898694\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138939208828\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138942753603\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138944212928\n",
"Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138944888886\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138946891212\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138948917118\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138949510988\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138949519202\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138950426996\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13895264419\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.138954254677\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138955267964\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138959501273\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138960680063\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138960682597\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138962783452\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138964899486\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138964958084\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.138966225447\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138968874431\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138969913456\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138971202834\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138975287436\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.138975708718\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138976216885\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138978061286\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.138978684969\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138982865855\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.138983686347\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138983842884\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138987010683\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.138987217756\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138987515619\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.1389879129\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138989519553\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.138992752993\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.138996274334\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138996325875\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138996727404\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138997677432\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139000230357\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139000940506\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139003407219\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139006269317\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139008406918\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139011299915\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139017259148\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.139017835183\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139018644719\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139018850903\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139018989355\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139018997293\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.13901932189\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139022456351\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor 0.139023457945\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139025280784\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139028625072\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139029166223\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139030398277\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139033279912\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139034346105\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139035296243\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139036302224\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139036633072\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139037284662\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139041145226\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139043725137\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139047089318\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139047471125\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139047574874\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139047893521\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139049271235\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139049805629\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13905129884\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.139052407856\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139054842831\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139059876521\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139064030852\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139064743725\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139065011905\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139068594007\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139068684181\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139068957687\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139069168722\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13907170944\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139072613686\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139072874281\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139074404259\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139074712639\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139075935439\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139077340616\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139079279852\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.139080370592\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139080951793\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139085596124\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139089477157\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139090098581\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139090894923\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139091923481\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139092442385\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139092964672\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139095289718\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139095487904\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.139099820116\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139101560564\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139104588758\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139105139568\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139107663585\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139107806272\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.139109959631\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139113036986\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139115116819\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139115412059\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.139118048738\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.139119613161\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.139119884147\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139120854909\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139120902493\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139123262611\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139123860258\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139124761987\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139128655839\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139129088846\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139129240184\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13912991446\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139131401893\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.13914003461\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139140420976\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139140560427\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13914277687\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139142853476\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139145216621\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.139146214082\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139156040133\n",
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"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139156939567\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139157691216\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139158162123\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139162408449\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139164305201\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139168272307\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.139168728743\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139168928885\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139171662948\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.13917170712\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139173858515\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.139177561329\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139178819866\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.1391803019\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139181457591\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139182324266\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139185149524\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139185862727\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.139187000756\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139189589069\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139190443024\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13919208227\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.139193488045\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139196575798\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.139200663331\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139204636655\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139204730784\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139204885671\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139209151597\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139210143081\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139213244571\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor 0.139214530215\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.139214650459\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13921551755\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139216429193\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139220436311\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139221047798\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139222632626\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139223155486\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139226492536\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139228147291\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139229660024\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1392302833\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139230727405\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.139231274724\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139231862237\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139236145472\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139240277839\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.139240754848\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13924338932\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13924544628\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.139246428592\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.139246486973\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.139248526473\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139248710744\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139250175536\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139252267034\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139256290304\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139259207041\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139267242343\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139268483061\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139269973509\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139272711846\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139275029488\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139275867528\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13927624212\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139279710002\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139283072335\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139286185765\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139287522941\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.139288739321\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139289494899\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139290778802\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.139290882831\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139292023305\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139292088491\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139292427892\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139292504687\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139292911386\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139297284733\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.139307529164\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139311207586\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139313395927\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139315551192\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+RandomForestRegressor 0.139316572867\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139318894685\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139323765215\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139325766394\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139325899779\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139332377302\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139333598081\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139334762775\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139340627933\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139344856534\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139345555308\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139346268191\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139347908179\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139348164849\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139348609027\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139348633977\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139349730973\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139353914295\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.1393560453\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139360180471\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139362033699\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139362205909\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1393647444\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139367162067\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139371277028\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139373513885\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139375249984\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13938048032\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139380530175\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139381490065\n",
"Lasso+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139387265635\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139390693881\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139391283274\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.139392849764\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139393420391\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139393444614\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139397449925\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139397816437\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.139400588504\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139402681163\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139407359316\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139410497077\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139411187013\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139411573039\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139411757398\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139413273152\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139413455803\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139415070177\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139416418012\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139416941992\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139416951253\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139416969548\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139418785402\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139422111987\n",
"Lasso+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139422546275\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13942263659\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.139423653403\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139426524306\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139427241103\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139429878613\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139431446755\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139432520007\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139432562341\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.139432854776\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.1394342588\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139435443586\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139437705191\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139440339502\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139446087139\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139449666698\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.13945135683\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13945202387\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139453394364\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139454944767\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139455058129\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13945547644\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139455488167\n",
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"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139457887553\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.139459599946\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139461104735\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139468464812\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139469284794\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139470496631\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139470668752\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139470769739\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139471457064\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.139473056302\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139478755063\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139479306192\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor 0.139479579792\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139482302119\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139482635553\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139485732482\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139486420082\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.139487480001\n",
"Lasso+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139487718533\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139487910844\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139488977056\n",
"Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139490175585\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139490891278\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139497532904\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13949830488\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.13949962382\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139500059556\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139501355889\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139501545748\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139502067994\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139504415689\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139505857017\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.139507112241\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.139507919812\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139512566433\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139513837999\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139516271914\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139516486686\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139520885544\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.139522639577\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.139529279465\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.13953274878\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13953785675\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139541649935\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139545029436\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139545354303\n",
"LinearRegression+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139546291168\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139546632412\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139547035251\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139549423564\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.13955080281\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13955236292\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139555056382\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13955563817\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139556178124\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.139558612942\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.139558689429\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139559868537\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139560134203\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139563265447\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139563312409\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139567406614\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139571741724\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139573075949\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139573377855\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139574622367\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13957589089\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139576777637\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139580406419\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139582093407\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13958514922\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139585236951\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139587461482\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139587724685\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139591440358\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139595604901\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139596307928\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139597323685\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.13960074878\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139601113693\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139604103276\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139604811224\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139604988236\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.139605338865\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139607906765\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139608801651\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139609631444\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139611424095\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.139612424586\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139612443381\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139613396918\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.13961372251\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139615727519\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139615791308\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139616378309\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139616834789\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139620461541\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139622259244\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139623784648\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139628477196\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139628794233\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139629836442\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.139631208532\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139636984548\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139639402224\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139640106478\n",
"Lasso+LinearRegression+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139640240376\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139640658605\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13964268444\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139644856694\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.139645605649\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139646457504\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor 0.139646613869\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13964709129\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139648080368\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139649287135\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13965237534\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.139654893358\n",
"ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139655237483\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139656941834\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139658363249\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.139660906888\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139661482951\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139662841751\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139663692185\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139665660074\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139667982681\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.139669266782\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.13967140806\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139674635695\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139676719098\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139678372541\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139680465189\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139681382142\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139682728211\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13968836712\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139691429248\n",
"LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139694220628\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139694455973\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139694657656\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139695761984\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139697190081\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.139698503223\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139698518021\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.139698837847\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139699464435\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139699765136\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139703244865\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.139705172927\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139708470691\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139709830006\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139710050317\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139711628939\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139712192558\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139715052362\n",
"Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139716004468\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139717683664\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139718087451\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.139720552163\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.13972233286\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139725664266\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.139725752125\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139726396777\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139726568954\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139727685762\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139727852931\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139728596181\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139730110391\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139733099659\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139735217244\n",
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"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139738068868\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139739358923\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139740908046\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139741271349\n",
"Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13974288181\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139742916093\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139742927981\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139744109186\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.139744633578\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139746974848\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139747227386\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139749052301\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139749483607\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139750290925\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139753314638\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139760656827\n",
"LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139763099893\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13976522537\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.13976589255\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139768400748\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139768911104\n",
"Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139769396951\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139770921004\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139771454673\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139774656577\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139776452193\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13978042183\n",
"Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13978199617\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139784790371\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.13978862818\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor 0.13978892384\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139792715842\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139793417398\n",
"Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139793629837\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139798681464\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139800291711\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139806226289\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13980961344\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.13981050755\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139811656627\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139812616826\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139813325949\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13981609027\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13981910184\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139819342227\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139819865558\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139821412642\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139822003187\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139823981843\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.139824555644\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139825119561\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139825538029\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.139826247989\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139826251396\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.139827247588\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139829634364\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139830185262\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139831600974\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.139835803777\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139841637495\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.139851091905\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139854148217\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.139856812273\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.139858204238\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139859115956\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139865348358\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139867042922\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139869689105\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139870406004\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.139870816615\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139874703027\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.139874856663\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139875259333\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139876099898\n",
"TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139877281864\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139882038041\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139886138261\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139890152542\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139892042266\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139893544117\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.139893894059\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13989412533\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139895935562\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.139899045711\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.139901745601\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139911705763\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139912471814\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139912692743\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.13991440284\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.139915223091\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139916360908\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139917107259\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139919424032\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139920732313\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139923295277\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139924042131\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139924113795\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139928853309\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.139928987016\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139929162772\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139929531779\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13993344512\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139934204233\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13993516147\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139936512392\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139939423591\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139939817578\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139940287502\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139942279205\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.139942346679\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139943193849\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139944127667\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139946094504\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139950473173\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139953611822\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139954211651\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139954286345\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139955088884\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139962506967\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139963209711\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.139963966929\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139966625681\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139966653958\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139967705386\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139968251013\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139970427695\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139972121507\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.139974972812\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.139975753158\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139976019403\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.139976499672\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.139977869901\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.139978251354\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139983680331\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139988071829\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.139988326916\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139989566545\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139989706804\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139990044183\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139990313942\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139991253045\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139993220423\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139993474651\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139993697045\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139993711065\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139993782004\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139994955304\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139995801979\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.139996905818\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.139997083048\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.13999773067\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.140001563729\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.140002319959\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140004128915\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140005244876\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.140008223195\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14001018026\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140013037844\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140013615853\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.14001386152\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.140014151141\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140014910712\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140015112785\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140017943271\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140020300958\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140020310081\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14002058493\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140024325466\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14002544672\n",
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"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140027814251\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140030573404\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140031560283\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140032079698\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140032166607\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140032658438\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.140035432418\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140036476802\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140037440206\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140037779295\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140039448384\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor 0.140040768005\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140042197501\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.14004288267\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.140043258587\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140044644839\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140049144823\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140050137684\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140050284937\n",
"ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140050458898\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140060715671\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140061814905\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140064853451\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.14007139102\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140075102589\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140075496506\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140079323051\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140079355051\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140079848835\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.140081558395\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140082104129\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140082347094\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140083606054\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140083651105\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140084311473\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140085012042\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140085636201\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140088294833\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140089849907\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140090794788\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140094573862\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.140095872089\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140101689887\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.140102355843\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140103619963\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.140103953194\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140104255246\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140108444498\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1401097683\n",
"Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140111794536\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140115134559\n",
"Lasso+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140116963632\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140119493576\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140120251736\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14012029775\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140122402739\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140124873013\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140125366267\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.140126689819\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140127374104\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.140128647015\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140131604092\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.140131636224\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140132000071\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140132989192\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140136707021\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.140137208725\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140146910142\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.140155017584\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140157701285\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140158339458\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140159312559\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140162741659\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140164103124\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140167640064\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140167893911\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140168583145\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140169911733\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140170545537\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140171009656\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140172961606\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140175247306\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140178694102\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140179676411\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140182651766\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.14018337675\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.140185497563\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140186281709\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140186661317\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140190975346\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140191677548\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140192165224\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140192473683\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140192966566\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140196892434\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140200963281\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140202757007\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140204315377\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140206912236\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14020982222\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140213982604\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.14022013472\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140229537423\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140232127977\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.140234088008\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140236233981\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140236242659\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140237044869\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140238124351\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.140239200562\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140239969844\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140242831205\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140243073674\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140247104292\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140247474747\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14024823728\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140250035098\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140252128271\n",
"Lasso+LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140256593662\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140257686558\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.140263200914\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140264255245\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140274961037\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140277842509\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.140279388424\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.140279508734\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140279818652\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14028182543\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.140284433156\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140285890753\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.14028632962\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140287028594\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140295592677\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140295721813\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140299106214\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140300545322\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140301916053\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.140303014161\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.140305552541\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140306476167\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140306561777\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140307668184\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140308894946\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140311448714\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140313239613\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140319919637\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140326242573\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140329578274\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140331329196\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140334007039\n",
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"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140341289382\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140342678737\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140343921859\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140344168904\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14034761553\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.140349641883\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.140350465851\n",
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"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140354993559\n",
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"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140367175077\n",
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"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.140373858852\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140374028142\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.140374125736\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.14038375378\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.140386056864\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140386369522\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140386515415\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140387464689\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14038886751\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140388979218\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.14038902329\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140392666724\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.140393862077\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140396924723\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140401407534\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140404799827\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140406004498\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140410387254\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140411894082\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.140412262888\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.140416422723\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140417523016\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140421391005\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140422498905\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140427817537\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140428522494\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.14043101723\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.140431741968\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140432230775\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14043353268\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.140440466712\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140445806432\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140446120584\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.140447828382\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.140449295257\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140449941584\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140450469531\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140451900496\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.140455347704\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140464031066\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.140465032797\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140467482768\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.140469560272\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.140477751848\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140483781851\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140483881374\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.14048609958\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140486162963\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140486828757\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140487242479\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140487591039\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140489276626\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140491073043\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140492385111\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140494177303\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140495933914\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140502474419\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140505573738\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140506423246\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140508204262\n",
"RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140509030113\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140517242114\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.1405190416\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140519853937\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140524408298\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140530429891\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.140530456358\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140530988266\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140531100898\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140532576963\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.140533513411\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140534363358\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.140535522797\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140536777205\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140537893934\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.140538773176\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140539659946\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140540994676\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor 0.140542097958\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140545461702\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140545877836\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140546655208\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140548990602\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140549704888\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14055028176\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140551215907\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140553241313\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140553701361\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140554244145\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140555265177\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.14055574743\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140558720276\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14056374055\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140566322985\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140566951786\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140568131563\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140568718902\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140569125259\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140571194321\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140572965955\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140573556989\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140578056515\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140578746128\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140582774564\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140586759285\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140586892675\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140587426073\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.140590009842\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140591133403\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140591568761\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140593783583\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140594627355\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140599918186\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140600053541\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140606109019\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140606384319\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140607977783\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140610705521\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.140610840477\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.140612872239\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140615965693\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.140616242738\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140619831575\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140621642527\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140621809101\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140629382977\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140629586031\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140630141678\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.14064179095\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140650495035\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140650623363\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140651654479\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140652040599\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140653296805\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14065431789\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140656239249\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140657186884\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140658675086\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140659303589\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14066597369\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140667205576\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140670043162\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140670318193\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140676042225\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140677876277\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140679363696\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140687001861\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.14068758748\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140693254695\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14069517494\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140700236774\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140703469868\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.140703472167\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140704570533\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140704708237\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140705470224\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140705916758\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.14071174205\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140711747199\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.140715332765\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140716715127\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140718283633\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.140719378587\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.140719382518\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140723766811\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140726503142\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140726532337\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140727181772\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140730694479\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140733098883\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140733663457\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140734645487\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140737256045\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140737370897\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140737657775\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140741789578\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140742062805\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140742729068\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140746072263\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140746129104\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR 0.140748122732\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140750757285\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140751807671\n",
"Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140752818977\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140753134179\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140754730443\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140755301406\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140758227528\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14076030076\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140761366929\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140762572896\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.140764866639\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140767219613\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140768034438\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14076991294\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140771255598\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140772328012\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140773460728\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.140773573802\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140777241934\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140777262909\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140778583783\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140778808417\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.140779982182\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140780413866\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140781364805\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140783295923\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140784894219\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140786355146\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140791765523\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.140792036119\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.140802769528\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140803703368\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140805959363\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140806521005\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140806639309\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140809365822\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140811826811\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.140815351318\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140815746462\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140817654843\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140820079634\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.14082143297\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140825291948\n",
"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140830559218\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140830942027\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140831309771\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140833096068\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140833102936\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.1408402216\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.14084070056\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140844396793\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140844985746\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14084598275\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140847972908\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140853238314\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140857494757\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140857502982\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.14085961094\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140860239711\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140860796703\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140862857056\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140864807382\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140872020286\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140873316698\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.140873790649\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140877140009\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140878262098\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140878559052\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140878863317\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140885310548\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.140887699039\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140888693758\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140888729381\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140891517605\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140893219688\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140899073229\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140899493263\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140902248288\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140904644492\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.140910291751\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140910462904\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.14091055919\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140911838459\n",
"RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140914879717\n",
"Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140915054552\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140919231401\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140919929871\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.140921769861\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140922112901\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.14092623147\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140932989462\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.140934145673\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140935037338\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.140936639755\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140937023833\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140938424355\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140938432695\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140939171403\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140939807326\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140942177921\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140942624339\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140946428397\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140946772652\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140947409279\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140951011875\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140953961566\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140966597369\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140967041023\n",
"Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140969842426\n",
"Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140971607081\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.140972032248\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140972073222\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140974608896\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140976514269\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140976867035\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140980005339\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140980185765\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor 0.140988157248\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140992321137\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.140996579205\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140996999\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140999258537\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141000006374\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141000067409\n",
"ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141003067318\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141005873279\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141009608951\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141010952454\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141011784968\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141020087218\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14102315125\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141028411117\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141032759073\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141034341104\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141034397817\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.141034793826\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141034899771\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.141035374608\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141037671381\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141041613086\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.141042585826\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141042596921\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.141043259787\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141043876599\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor 0.141049007098\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141052276515\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141053951141\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.141055629129\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141055908643\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.1410563955\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141057703369\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141059175386\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.141059374713\n",
"TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141060494591\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.141064811176\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141072073086\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141072607111\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.141075182189\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.141078069376\n",
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},
{
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"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141541973476\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.141546526794\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141547193346\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141554823795\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141555240771\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.141555427128\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141560343874\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.14156206222\n",
"RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141563170809\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141581576702\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141582947819\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141583233468\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.141586001628\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141587025875\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141591363838\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14159169236\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141591696615\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.141592652239\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141594611883\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141599340966\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141604575225\n",
"Lasso+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141606582274\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141608439382\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141610200106\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.14161336947\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141614713275\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141614972308\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141616354169\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141619061572\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141620269388\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14162101593\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14162289795\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141628153695\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.141633527346\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141634840855\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141639860689\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141645341517\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141650367404\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141653781959\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141656221864\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.14166005774\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141665946636\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141666066114\n",
"ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141667644543\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141670690691\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141684118776\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141690291205\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141696689494\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141699115274\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.141700680567\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.141702613149\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.141706397831\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.141708111332\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141714460256\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141715695444\n",
"LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141716834595\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141725961645\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141727857112\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141728897011\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141736992629\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141746350578\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141748671644\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.14175208472\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.141752719366\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.14175289517\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141755067608\n",
"RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141757520281\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141761892567\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.141764129536\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141764225659\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14176658923\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141770752209\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141772049835\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141775844346\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141781049064\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141781891799\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141785653292\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.141785662058\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141786461212\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141786921653\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141787748666\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141796549088\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.141797871655\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141800372712\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.141801773746\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141802229815\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.141804746085\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141808104727\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141809714355\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.141811072569\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141812900189\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141818919661\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.141821776853\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141821953341\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141824311899\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141825774232\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141826530451\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141829685249\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141830027662\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141830451638\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141832480523\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141833045409\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141833124587\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141834021401\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141834496845\n",
"Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141836297791\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141839526683\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141840516177\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141842193597\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141842614051\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141844825266\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14184534643\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141845409143\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141845826756\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141845912438\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141852483802\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.14185252337\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141858577754\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14186213284\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141869112543\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14187082921\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14187153811\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141874822679\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141878588036\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141878848711\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.141879316004\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141885727544\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141886173308\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141888681555\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141892050481\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.14189326755\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14189470443\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141896087422\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141896493826\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.141897594902\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141897610282\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.14189845658\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141898841717\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141900119638\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141900143137\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141900828683\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141901797832\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.141902778759\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.141908508426\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.141910724143\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141912600145\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.1419181025\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141918318852\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141922799655\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141923820115\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141926396653\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141926708041\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141930026001\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141935204969\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14194108941\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.141942433734\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141943010796\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1419451464\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141945810466\n",
"ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141946744146\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.14194676412\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.141947412695\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141947680438\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141951539745\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.14195291783\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141953513185\n",
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"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1419584426\n",
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"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.141961710723\n",
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"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141976960375\n",
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"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141982546041\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141991766979\n",
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"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141995627625\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141997307968\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141998207858\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR 0.141998699859\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142001501873\n",
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"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14200285663\n",
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"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142003892549\n",
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"Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142005960893\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142006299868\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142009180348\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142009962405\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142012780112\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142012780294\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.142017997088\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142019833405\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142020786016\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142023815158\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.142026176443\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142028719763\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.142030591926\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142030976593\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142032271884\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142035546809\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.142037111606\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.142037987879\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142038965038\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142040953388\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor 0.142040966665\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142041650748\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142041880864\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142044648442\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142046008463\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142052805865\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142056057538\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142059452363\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142060446487\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.142062318488\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142062450632\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.142063307884\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142065624807\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.142067243185\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142074325458\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142074400689\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142078294052\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.14207963742\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14208196446\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142084842353\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.142085516564\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142086078638\n",
"TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14208724779\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.142089256129\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142092157215\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.142095647554\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142095936557\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142097863739\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142101939828\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142102167963\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142104201173\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142105264248\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142115213976\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142120347001\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142123578298\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142126226854\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142128274297\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.142129737518\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142130855472\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142134351551\n",
"Lasso+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142135395148\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142139957089\n",
"Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14214133789\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142141753062\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142144113669\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.142145204659\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142150423474\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142152103934\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142156632527\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14215727232\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142160388261\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142166450344\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.14217103641\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142173512988\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142176712119\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142183988673\n",
"Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.14219236079\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142193583906\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142195257369\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142195324117\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142195660856\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.142197784105\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142199008266\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142201043507\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142202902963\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142203407526\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142206354041\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142206947036\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142216919002\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142218661169\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142222008537\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142224473499\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142224662476\n",
"Lasso+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142225768849\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142226242127\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142229734019\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142230418024\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142230467649\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142231645557\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142232400114\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.1422330197\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142235123116\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142236804917\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142237652323\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142238823586\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142239156801\n",
"Lasso+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142242784895\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142248611864\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142250324083\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.142251986195\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142252759802\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142254827164\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142254855027\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.14225526851\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142259939134\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142262457821\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142264203675\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.142267200711\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.14227057077\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142275240591\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142280521923\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.14228107585\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142283107888\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142283179935\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142287083952\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142287397603\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142288420174\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142296277836\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142298285889\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142300538168\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142300744241\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142301211566\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142311863044\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142312472577\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142314760593\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142315392645\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142317151725\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142320685633\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142320962621\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14232164846\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142324470596\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.142327856164\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor 0.142333006715\n",
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"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142342506825\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142345723179\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142348096787\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142350043443\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142351025664\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.142358576398\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142361904245\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142362687877\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142364088824\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142364766209\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.142368355169\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142374026327\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142374992598\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1423768354\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR 0.142386018493\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142391000579\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142392125981\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142398419087\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142410864949\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14241928877\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142419621506\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142423317307\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14242407743\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14242580048\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142430647518\n",
"SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142431297903\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142432339566\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142434641029\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14243480616\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142435009316\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142448381222\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142449140763\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142450584764\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142451470597\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142454995539\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142459895\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142461411972\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.142466520634\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142468098348\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142470849221\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142472908529\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.142485054585\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142487173446\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142487664233\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142487787629\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.142495134034\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.142495356632\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.142506483075\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.142507144449\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.142507914739\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR 0.142513750495\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142515696305\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14251988587\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14252178908\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142523733837\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142526591039\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142526839393\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142528936481\n",
"Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142529099165\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142529988912\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142531117936\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142537716929\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142537964043\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142540879333\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142543847967\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR 0.142549688124\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.142557904677\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142560079631\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.142561745146\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142564415899\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142565061126\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.142567108722\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14256962754\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.142570613853\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142573569562\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142575039659\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142579589485\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142579648943\n",
"Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142588439646\n",
"Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142590513387\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142597540827\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.142597563644\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142598478873\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142609369946\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142624780089\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142629076207\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142636966167\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR 0.142639544282\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.142641454192\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142645662872\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142669082109\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14267697572\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14267915962\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14268522739\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142686202103\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142687792152\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142696234274\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142696311681\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.1427087679\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142709765613\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142709894117\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142710593578\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142711230812\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142713547334\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142720293072\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.142724821029\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.142725978565\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142726925583\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142732156812\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142735413259\n",
"ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142737653952\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142746375176\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142752806157\n",
"ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14275769679\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.142764321599\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142767277317\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142769044571\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142777440159\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142777872428\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142782837559\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR 0.14278357342\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.14278758528\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.142789680854\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14279358499\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.14279367078\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142797628968\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142798339985\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.142802431506\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142811756454\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.142812980241\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142818240404\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142819944812\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.142824641067\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.142825695848\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142826119236\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142830085412\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.142838420831\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.142840217833\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142840237417\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142842560881\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.142842891747\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14284515145\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142846160973\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14285362596\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142867909434\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142870690422\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142875296486\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142880397152\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142881457737\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142882650049\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142883054097\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142888403792\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.142889467771\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.142891694928\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14289181695\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142901598845\n",
"LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.142913415207\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142916897119\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142917520766\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.142917576463\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142927494657\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142928463862\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142928470572\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142930588725\n",
"TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142936909392\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142950282167\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.142954049237\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142955858059\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142956703514\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.142958408863\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.142962315849\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142963308695\n",
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"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142971325812\n",
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"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142993612673\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.143001247299\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143005719222\n",
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"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143019939361\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor 0.143020215721\n",
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"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143027055534\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor 0.143028893734\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143029011355\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143032801884\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.143034654949\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.143038761079\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.143040210159\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.14304033932\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143043562847\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143049983891\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143055177881\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143063065232\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.14306381475\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.143072036936\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143083092813\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143083695006\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.143092131935\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143093301837\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.143096035156\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143102796037\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.143110547245\n",
"Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143119002394\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.143120780792\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143127372904\n",
"Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143127815993\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143127848269\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143135958619\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143145280625\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143145567827\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143147047817\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.143147087962\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.143147866303\n",
"Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143162786955\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143169218075\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.143172657091\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143182749277\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143184859423\n",
"Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.143185666135\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143190557898\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.143195439464\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143197211398\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.143198431339\n",
"Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143203729474\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.143219270786\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143220778652\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143222420576\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.14322781816\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.14323255419\n",
"TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143237458025\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143252353523\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143253248142\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143260260223\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143265022444\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143269284108\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143269391203\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.143274953372\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.143277779822\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143284530907\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143285156592\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143288961966\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143293650123\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143296237901\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.143299900256\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143305220694\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143308985646\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.143310794515\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143314538113\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143315578504\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143318904233\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143341343488\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143342146415\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.143342297804\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143345206387\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143350708831\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143352343925\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.143361584936\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143363814975\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143372040141\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143372825296\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143376960114\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.143383893415\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143384480742\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143385796948\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.143387354332\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143387574176\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.14339032817\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143393040276\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143396396543\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143401215209\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.14340290921\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.143405987018\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.143408518829\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143408866419\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.143413119255\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143414176503\n",
"SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143415229889\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143423077529\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143425157401\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143427058364\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143428321508\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143428330842\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.143429381313\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.143436756609\n",
"RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143437538133\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14343759895\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143448734452\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143449046907\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143454018683\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.143454369864\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143459199575\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143463974229\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143464770169\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143464881521\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.1434747174\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.143475477669\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14347675167\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143476887294\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143478031414\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143485148578\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143490904242\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143491539757\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143498400572\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143499425664\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143500118321\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143505555149\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143507307669\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.143509328658\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143526813657\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.143533452602\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.143535061853\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143537917445\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143537955456\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143539625565\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.143540709814\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143542144487\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143542510987\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143542525894\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143546823067\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143547304035\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143555176106\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.14355893906\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.143570187893\n",
"ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143582271315\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143591598931\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.143592506602\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.143594892846\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.143595605231\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.143596511015\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143604277012\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143613046064\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.143616739825\n",
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"ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.144593547392\n",
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"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144604660204\n",
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"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.144635158944\n",
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"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.14469458385\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.144702292127\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.144702921271\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.144722449866\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.144724750071\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14472843839\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.144736980813\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.14473878036\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.144740062321\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144747813555\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144758497266\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.144766153382\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.144771633658\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144774089679\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144787044388\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.144790471238\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144797359676\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144798758745\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144800988055\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.144803657654\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144808039693\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.144808274667\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.144809827736\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144824118862\n",
"ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.144830288736\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144846393872\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.144847150886\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144849102703\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144865048264\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144868479351\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.144878076696\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144886118691\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.144892506255\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.144923969651\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144940755638\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144945905061\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.144950775834\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144955710426\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144974951664\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.144976238828\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144997737921\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.145006323171\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14501909843\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145034431548\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145038338362\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145039071208\n",
"ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.14504185662\n",
"ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145051568488\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14505163694\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145051860774\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145057406652\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145059164772\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145065166898\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145066603519\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145076181106\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145080295656\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145085104276\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145085246386\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145104237056\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145119553352\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145127636525\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145127903951\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145131578489\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145136610183\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145146877481\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145158753149\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145160566909\n",
"SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145166693821\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145171092909\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145176669471\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.145188684774\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.145194846938\n",
"Lasso+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145196216563\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.145197416039\n",
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"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145211208181\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.145218451323\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145224172\n",
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"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.145229254168\n",
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"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.145235006164\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.145240759742\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14527613225\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145281977315\n",
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"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14529271781\n",
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"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145325123457\n",
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"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145338077421\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145341589741\n",
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"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.145363816424\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.145398809104\n",
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"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145493226796\n",
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"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.145584095892\n",
"TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.145588484546\n",
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"Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145711945042\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145714210439\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145745926435\n",
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"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.145799997547\n",
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"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.145847340426\n",
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"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.145949344788\n",
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"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146056158521\n",
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"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146064965049\n",
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"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.146204033435\n",
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"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146260456712\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.146260516131\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146280668161\n",
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"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146321241827\n",
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"Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146338111981\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146355915206\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.146374470827\n",
"ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.146385000791\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146411829919\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14644145511\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146448469534\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.146463698087\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.146467494606\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.146534456601\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.146535712657\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146539627604\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.146540221636\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.146541331976\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.146565994377\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146579801009\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146580396035\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146588905792\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14659306593\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.146601003668\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146608154607\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.146629235799\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.146631039982\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146637407115\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.146641765211\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146642554621\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.146650039554\n",
"Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146667235654\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146710910966\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146723458684\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146727017834\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.146789194603\n",
"TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146794784192\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146806561187\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.146829507565\n",
"ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146836020554\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146880108723\n",
"ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.146885839413\n",
"ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146904624633\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146906748368\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14691064642\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14692589705\n",
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"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.146947275767\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.147020452727\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.147050401045\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.147062241453\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147066529776\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147074512892\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.147124707912\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.147134745543\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147160618071\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.147164768121\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.147167170231\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.147208631449\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.147213431657\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147215145513\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.147243233865\n",
"RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14725195594\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.147277949435\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.147287338513\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.147299848696\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.147316541187\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.147360017987\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.147362682988\n",
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"ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147488240874\n",
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"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147511256675\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147523834256\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.147535591012\n",
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"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147576848886\n",
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"ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14758461149\n",
"ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147596159371\n",
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"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.148524863526\n",
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"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.149520815355\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.149600213066\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.149663063722\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14966440585\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.149680794161\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.149835772074\n",
"HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.149875565416\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.149916478288\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.149940777743\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.149968364596\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.150010490286\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.150032232587\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.150051698621\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.150184279949\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.150283807926\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.150308715905\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.150356044468\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.150485882073\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.150512429981\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.150975006165\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.15125798913\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.151285208742\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152520069559\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.152665670126\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.152849206769\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.153061999167\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.153531634258\n",
"\n",
"Model Amount : 8\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128744815807\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128752844112\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128906279856\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129012688809\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129030025433\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129065967214\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129070022746\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129127655655\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129289488451\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129335626953\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129385937178\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129408999999\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129444913362\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129455540047\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129464784598\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129466981592\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129503890741\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129572651559\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12958721018\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129601540233\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129610826069\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.129652735274\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129664575057\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129706916416\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129710062983\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129742419134\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129751938653\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129757844879\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129765179628\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.129776554988\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129779518933\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129779865027\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129800220247\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129810764697\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129814836739\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.129857299878\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129872191124\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.129872819172\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129902544903\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129906738305\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129908414711\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129920088555\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129927535283\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129937651041\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12994255188\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129946194778\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129954446268\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130001262806\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130006829583\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130023804431\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13005614965\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130064437838\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130064557005\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130071810545\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1300978713\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130124864208\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130131513021\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130132340865\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130138799883\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130154314515\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130161157285\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130172531147\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130237365297\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130242195693\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130242265342\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130245215684\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130277307249\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130280849732\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130320337085\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130340626646\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13034396828\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130346363442\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130353449654\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130381649211\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130394397433\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130396746029\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130408375219\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130409659214\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130420421417\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130429906029\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130439586271\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130441730006\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13044486616\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130450236707\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13045504054\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130482258456\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130483809174\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130492061515\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130492394266\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13050926457\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130558615784\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130569435286\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130590792013\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13059527468\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130621255211\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130632409387\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13064621644\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130647258118\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130654666968\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130662413909\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130669133655\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130673023228\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130679339993\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130685469919\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130687956107\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130688684348\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130701920599\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130709114827\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130712338055\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13072010232\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130743745807\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130749925645\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130762232664\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130771944256\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130772512341\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130799485597\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130799761479\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130856282572\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130903987394\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130909089554\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.130925940919\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130938263616\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130943543593\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.130950861004\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130950971443\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130960713851\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130968619445\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130972162369\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.130977039053\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130983300618\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130986007877\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.130989630385\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130991972834\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131001824923\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13100334636\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131005496248\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13100880099\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131017371081\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131023875931\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131024615114\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131026456476\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131056017452\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131063381514\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131068178727\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131069658255\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131070501314\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131075140799\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131085929803\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131104531974\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131108013493\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131110805885\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13111748702\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131121485236\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131125975476\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131134525673\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131135985154\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131138869359\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131142855381\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131143666207\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131143733004\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131144904684\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131147881269\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131151280466\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131156931909\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131171565647\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131172862443\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131188101951\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131192813046\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131200485743\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131202589198\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131205348545\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131209485985\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13121553161\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131229745312\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13123007353\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131243111911\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131263226872\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131275038718\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131281991157\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13129771083\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131297758552\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13130049225\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131307477382\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131324191534\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131331228765\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131335145267\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131335661666\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131341782239\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131352065337\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131353031051\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131361465667\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131364413333\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131364517125\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131364585679\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131366480668\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131371643002\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131373176521\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131378646099\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13138952118\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131390908083\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131394095341\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131395078547\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.13140547591\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131407065678\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131407097545\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131416697274\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131421756306\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131422924159\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13142806258\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131440182247\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131442076447\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131452732884\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13145797925\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131463085719\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131470348582\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131471361183\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131474886133\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131482417357\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131502037749\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131502295384\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131511092279\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131521953872\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131530493461\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131532401235\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131535237821\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131539069022\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13154501931\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131547702737\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131553378561\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131557030616\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131562024977\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131574135985\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131574894844\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131579250109\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131580801649\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131586856347\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131594709407\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131598686471\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131606881373\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131607549804\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131615514098\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131616992932\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131620625649\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131621727541\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131621996127\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131627796816\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131629856511\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13163125785\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131634296294\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131640741713\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131642672286\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131653704092\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131653885519\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.1316680696\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131672366377\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13167592099\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131677069283\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13167929733\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13168478574\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131684798202\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13168496533\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13169125149\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131696559754\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131698638409\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131704086579\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131712705315\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131713766153\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131728848825\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131733341531\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.131739365912\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131752067814\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131759317409\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.131761733673\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131762097202\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131762148669\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131762232862\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131764058024\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131773346027\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131775236314\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131777685759\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131779416717\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131783353067\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13178855474\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131789865531\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131792292644\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131795799428\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131798001663\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131805549118\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131806705955\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.131815850575\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131817135301\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131817340826\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131835159967\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131835564448\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131856091826\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131856735416\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131861188809\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131861580515\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131865897774\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131874818864\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.131876461168\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131877039952\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131883265068\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131883309009\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131888350132\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131889351637\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131892241345\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131892763916\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131892911053\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131897102662\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131904259929\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.131904583117\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131924023012\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131924396251\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131924666195\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131926846378\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131930919752\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131938791675\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131944553598\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131952647123\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131954325929\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131961544357\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13196612768\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131968035943\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131973959765\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.131977217031\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.131979871956\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131985951327\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131994837291\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131999491464\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132003217884\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132006437849\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132008097758\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132012711597\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13201685643\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132020662437\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132023583698\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132030770938\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132041644991\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132045364739\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132048480516\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132049597833\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132055037922\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132056160468\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132056366203\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132062514625\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132064451576\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13207141783\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132079007069\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.13208037347\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132080764345\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132081658688\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132086270196\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132087309795\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132092270959\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13209265699\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132096145176\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132112956501\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132113953178\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132120914057\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132121462776\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132122592295\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132124089797\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1321319641\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132133601672\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132138923583\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132147997287\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132157020876\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132162061353\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132166355338\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132170385342\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132176452571\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132188962779\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132194265938\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132197925463\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132200351415\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132207318538\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132217951515\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132220221618\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132222602999\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132225644717\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132226246649\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132231198782\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13223127244\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132233392442\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132236340707\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132236817111\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132242259145\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132243327061\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132244020522\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132246437039\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132257986797\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132258214863\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132261730147\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132263085305\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132267739939\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13227344722\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132273646601\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132278864743\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132281544293\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132289701667\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132295840335\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132313682506\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132316435253\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132318451355\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132320535092\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132321386354\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132322714689\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132334349132\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132335883277\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132345254502\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13234554514\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132346118679\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132346370414\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132348597667\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132348628964\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132353754217\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132360595587\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132363681084\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132370176001\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132371349062\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.1323717185\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132374182524\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132374327192\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13237667738\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132379279871\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132381519611\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132389287853\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132389447543\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132391965805\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13239314848\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132394100506\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132395945153\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132399474418\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132403293908\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132405700662\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132406040529\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132406134873\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132409029659\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132409067198\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132409954037\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132410719397\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132432475991\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132440649357\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132447441138\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132449049594\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132450842658\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132451192384\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13245802724\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132458741377\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132459401296\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132459746552\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132460105843\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132460407548\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132461168353\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132461732079\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132464075398\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132467771186\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132470875852\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132472871443\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132473499285\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13247439316\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132476124109\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132478985156\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132482592269\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132483588683\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132484412681\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13248922873\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13249040665\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132491383484\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13249466419\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132496424478\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132499069243\n",
"Lasso+LinearRegression+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132500742808\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132503089612\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132503644402\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132505494029\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132507123543\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132509159611\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132509355819\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132516824123\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132523408122\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132526250411\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132527038697\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132528784243\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132530663653\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132531950835\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132533503067\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132533673606\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132541459705\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13254181938\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132543256203\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132547933963\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13254859669\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132552970715\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132554984717\n",
"Lasso+LinearRegression+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132555664805\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+XGBRegressor 0.132556091352\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132557294012\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132558442324\n",
"Lasso+LinearRegression+Ridge+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132564926964\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132568454066\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13257153747\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132573619214\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132576790931\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132584656793\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132585183814\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132590234702\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132593074021\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132600288918\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132606657037\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132611277262\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132611916945\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132614355934\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132617727947\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132622321577\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132629948304\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132636897177\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132641511694\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132644946651\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132650634641\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132652931952\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132655698301\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132657832084\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132661196621\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132664726249\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132665035684\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132667711179\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132667747016\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13266862518\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132672315891\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1326768645\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132679922852\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132685965098\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13269624875\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132701023119\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132702542449\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132704862956\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132708062644\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132710675638\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132714010978\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132718552951\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132719451942\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13272227803\n",
"Lasso+LinearRegression+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132724806131\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132725618939\n",
"Lasso+LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132729742358\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132735087298\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132735448739\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132736435837\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132740259322\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132746992853\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132748702628\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132755947818\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132755948552\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132759967575\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132761011139\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132761456453\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132761598184\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132761677028\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132762010812\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132763923668\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132767897825\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132767990954\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13277461452\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132781896948\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132781930536\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132787034724\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132788221014\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132788664972\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132789456121\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132791595467\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132791910837\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132792676964\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132799262135\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132801960266\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132803068263\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132804576689\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13280529265\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132816983022\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132819440914\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132822766722\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132823794135\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132825749416\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.132827918133\n",
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"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132846577574\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132861798636\n",
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"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132866136756\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132869461602\n",
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"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132888217589\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132893852725\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132894313644\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132899029644\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132900537527\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.132905027282\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132907459308\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132910404581\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132914992849\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132917423813\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132917738029\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132918680565\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132919191097\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132921994508\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.132925089922\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132925442417\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132928282138\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132939330217\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132941800801\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+XGBRegressor 0.132942941529\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13294739069\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132948221256\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132950030245\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132950505087\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132950746966\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132952820578\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13295403247\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132954854092\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13295499485\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.132957044183\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132957653661\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.132962258113\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132966722739\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132966727933\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132972437268\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132973336713\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132973540278\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132974663647\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132981604658\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132984338417\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.132984715653\n",
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"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132988234506\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132989229329\n",
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"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132999973381\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133012611405\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133012652564\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133015078813\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13301663854\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133023131209\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133274747094\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133275059018\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133279732177\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13328172388\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133283243504\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133287482389\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133287745081\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133287942958\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133288178556\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133289042368\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133289215049\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13328924373\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133292264413\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.133292884728\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133302480739\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.133302606773\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133304020211\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133305072756\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133308856686\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133309096047\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133311384741\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133314092483\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133314135904\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133314660236\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133315217154\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133320820224\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133321456146\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133323967006\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13332443885\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133328212181\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133330973172\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133332952245\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13333471423\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133335193349\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133339939527\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13334344859\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133347717044\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.133350620961\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133350783586\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133353915299\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133356576359\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133357275239\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133359481639\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133365996276\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.13336821405\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133371677489\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133374141747\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.133377939597\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13337931692\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133380366042\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133382559029\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133386205843\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133391582429\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133391872362\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133392121246\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133393640225\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133396798256\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133398929589\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133402480058\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133404389199\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.133408638351\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133414142346\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133418637663\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.133420002504\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133421187147\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133422454798\n",
"Lasso+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13342313286\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133424431601\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13342473285\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133424867938\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133425977543\n",
"Lasso+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133427258778\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133429545334\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133430123673\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133437398555\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133437744756\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.133441693424\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133445157339\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133453122202\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133453417545\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133454766808\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133458584006\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133459817555\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133462055003\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133463709087\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133464203106\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13346552559\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133468817776\n",
"LinearRegression+Ridge+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133468855347\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133470864341\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133471636788\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133471652273\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133477628975\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.133478632885\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.133479348698\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133480164246\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133482883807\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133484828992\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133485206254\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133485779097\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133486171864\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133487054571\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.133487224006\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133490302324\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133491671022\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13349412076\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.133495040226\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.133510711992\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133510790233\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133515059384\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13351787908\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133519305236\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133519576857\n",
"Lasso+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133519968652\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133526462844\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133530824814\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13353086246\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133533074126\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133533962203\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133534145228\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133536540567\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133538488713\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133539275414\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133540455317\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133540918081\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133542469147\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133542724172\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133543674529\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133543712476\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133547035688\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133556951092\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133560937305\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133562400253\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133564100967\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133570346392\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133571596927\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13357323099\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133573476858\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133575677168\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133575800101\n",
"Lasso+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133577505516\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.133579229155\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133579441862\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133589591555\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.133590605716\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133592554899\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.133595021859\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.13359542021\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133596258207\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13359690639\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133598454382\n",
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},
{
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"text": [
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133936276034\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133940704049\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133941444652\n",
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"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13395035738\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133954267435\n",
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"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133963238743\n",
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"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133967651785\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133967828753\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13397240224\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133973933522\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133976168104\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.133977083463\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133977889055\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+XGBRegressor 0.133978866114\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133981549103\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133982622963\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133983246625\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133984206161\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133984732241\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133985590723\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133987767682\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133987881279\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133992862987\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133994053312\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133994504902\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133997747357\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134001354514\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134008188041\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134009149898\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134011247549\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134012789128\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134014849955\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134014859052\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134015040265\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134017290299\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134017644518\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134018596706\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134021064357\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.134023252027\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134024850596\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134031467755\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134032744469\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134034217699\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13403503775\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134035810053\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134036127372\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134038371015\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134038526148\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134040984439\n",
"Lasso+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134040998384\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134041919958\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.134042308639\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134042517238\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134043420876\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134045955554\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13404807866\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134048214787\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134048682414\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134049430364\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.134050080463\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134051555818\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134053162814\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134053982027\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134054226826\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134055184507\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.134059982327\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134062040914\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134063805496\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134067561269\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134073672193\n",
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"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134230028664\n",
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"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134239477455\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134239737191\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134239871573\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134240593524\n",
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"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134248126644\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13425008118\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134250882833\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13425127602\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134253364499\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134258630723\n",
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"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134259398879\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134259641905\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134260784349\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134262204109\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134264613838\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134265220882\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134265330678\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134267777741\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134268215299\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134269119093\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.134275351707\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134276108833\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.134278740644\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134280393589\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134289568588\n",
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"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134301381519\n",
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"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134305773249\n",
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"Lasso+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134419964455\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134420999013\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134422159906\n",
"LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134423143209\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13442665467\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134427968203\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134429189967\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134432743348\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13443295272\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134433620858\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134433752081\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134434525685\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13443580651\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134437360906\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13443872515\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.134438926455\n",
"Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134441295283\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134444293253\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.134448152092\n",
"Lasso+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13444876434\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134449549609\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134451288795\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134451480187\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.13445203373\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13446038445\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134460949076\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134461398444\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134462046158\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.1344626185\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134463401622\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134466495841\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134467541237\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134470969922\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134476357633\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+XGBRegressor 0.134478302411\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134480716419\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134481953194\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134482031248\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.134482617211\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134484899272\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134486014443\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134486418674\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134487186755\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134490331015\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.134492070763\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134495274727\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134497143113\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134498285769\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.134499410571\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134501672736\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134504911712\n",
"Lasso+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134505541042\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134505566942\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134506482801\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13450804887\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134508948489\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.134511889299\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134512607579\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13451490022\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134516809583\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134518014468\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134518435008\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134518667694\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134519739045\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13452079176\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134521330096\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134522094699\n",
"Lasso+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13452515813\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134525884986\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134527088892\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134527236732\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134529880461\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134530361726\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134530955725\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134531704907\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134532889672\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134535156699\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134535190458\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134535708918\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134536811022\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134537285964\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134540724812\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134541927848\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134542504537\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134542537365\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134543703921\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134546368109\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13454688872\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.134547464554\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134547812948\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134548778381\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.134550138642\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134551043304\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134551689939\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134554670486\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134557058412\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.134557174835\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134559434745\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134559751201\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13456228303\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13456656289\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134568163237\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134570297906\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134570910706\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134573856819\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13457447118\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134574677088\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134575379614\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134576450488\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134578010077\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134579383514\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134579537455\n",
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"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134586553459\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.134587082618\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134587269908\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134590266749\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13459127099\n",
"Lasso+LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134592071877\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.134592184215\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.134594069757\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134596384849\n",
"Lasso+LinearRegression+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13459976792\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.134600744491\n",
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"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134605283905\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13460747054\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134607984911\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134610960735\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13461251398\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13461399997\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134615508831\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13461600976\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134617945754\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134620316315\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13462120641\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.134621689967\n",
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"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.134621954719\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134622316495\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134622460736\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134624896495\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134625092382\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134625592765\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134626353711\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.134629299651\n",
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"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134634251442\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134634863565\n",
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"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.134637817867\n",
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"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134638928327\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134639451556\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134643966327\n",
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"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134648642877\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134649087148\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134657293933\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134658925579\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13465996731\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134660383757\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134663468025\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134664234892\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134664905518\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134666445054\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134672542209\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.13467274898\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134677088597\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13467796065\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.134680198526\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134681177929\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134683782859\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134683932987\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134691194824\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134695670553\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134695764615\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134697463743\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13469902766\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134702201592\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134703041774\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134704378827\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134704469039\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134704536812\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134705053549\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134705641387\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134706427906\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13470796486\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134708064806\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134710138837\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134712700758\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134713399234\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134716546082\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134720508175\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134721842867\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.134724262001\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134724454217\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134727772865\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.134728986444\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134729849314\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134729899606\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.134731544893\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134732856084\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134735842348\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134740592927\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134741458196\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134743138091\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.134744413304\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134744947445\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134745015299\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13474553556\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13474595006\n",
"LinearRegression+Ridge+ElasticNet+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134746084416\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134748793837\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134756367135\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134756392716\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134760421514\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134760654312\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134764591675\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134764771312\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134767403281\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134767437712\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.1347682389\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134772650755\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134775863818\n",
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"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134778112011\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134779354262\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13478320896\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13478349145\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.134785556791\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134786995128\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.134789781258\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13479219336\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134794941431\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134797005459\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134799075483\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134802129439\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor 0.134803766619\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13481301516\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134813643751\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134815759091\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13481994899\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134820810088\n",
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"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134827545099\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134830212846\n",
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"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13483153581\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134833606719\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134834037674\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13483625719\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.134838090809\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134838898511\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134842612495\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134844556411\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134849381378\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13485032886\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134850599871\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134851836744\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.134853968159\n",
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"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13486257112\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134862808792\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134863570992\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.134866786762\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13486695113\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.134867807563\n",
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"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134884857215\n",
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"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134885838315\n",
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"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134888891094\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134889144709\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134889739626\n",
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"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134891772275\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134900011042\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134900319677\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134900881442\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134901068194\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134901908048\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134901931759\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.134902015492\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13490432916\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134906680478\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134908653354\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13491068554\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.134911847648\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134913660865\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134914236806\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134915347676\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134916959981\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134917110005\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134920429767\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134920887772\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134921149927\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.134921437126\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134921770835\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134924350576\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.134925072334\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.134926107891\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134927401553\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134928230831\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.134929078118\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134930557482\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134931235583\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+XGBRegressor 0.134931744249\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134932374099\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134932467752\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.134933502334\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134936682366\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134936799723\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134937971279\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.134938739843\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.1349392157\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134939509949\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134940277597\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.134942294245\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134944299032\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134945449194\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13494913504\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.134949643493\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.134950260469\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134951252008\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13495398132\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134955248461\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.1349565994\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134957232082\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134957296855\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134958177113\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134959453106\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134962382254\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.134963356173\n",
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"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13496989646\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134971606559\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134973300507\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135001108935\n",
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"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135038728599\n",
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"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135041370798\n",
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"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135056837267\n",
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"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135147098266\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135147167153\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135147485912\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135149859272\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135150059161\n",
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"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135164084791\n",
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"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135165586651\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135168650241\n",
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"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135171128365\n",
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"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135171681335\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135173506589\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135176005725\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+XGBRegressor 0.135199622947\n",
"TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13519986591\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135205540758\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135345488465\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135346991749\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135347609328\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.1353479443\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135348637601\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135351205532\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13535426183\n",
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"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135360768125\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135361150366\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13536196426\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135363922407\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135365475297\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor 0.135366083599\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135366378644\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135370258123\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135370289538\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135373265627\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135376711064\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135377330896\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13537840182\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135379496611\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135380833333\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135385865891\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135387852731\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135388790666\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135394689038\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13539699451\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13539766833\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135397678212\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13540156496\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135403261599\n",
"Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135404599254\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135404719748\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135405793413\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135408319403\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13540955529\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135409909428\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135409970696\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135410541138\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.135413168715\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135415307945\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135415542982\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135416041387\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135416646795\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135418455414\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13541899679\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135420074258\n",
"Lasso+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135420183114\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13542019367\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135420810697\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135421310034\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135421549622\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13542258488\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135424020054\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135424392402\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135424742034\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135425157786\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135426995738\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135429260853\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13542970931\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135430287256\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135434068484\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.135435031597\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135435238849\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.135435971517\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135436814811\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135437163154\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13543946139\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135440760493\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135443101376\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13544409505\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135445904533\n",
"LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135446063982\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135451092828\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135451700896\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135454471466\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.135456413599\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13545851431\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135459156512\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135462529401\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1354645154\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135464981365\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135469422717\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135470813171\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135475587588\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.135476061641\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13547907188\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135479531034\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135479915617\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135480657332\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135488399875\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135488623912\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135490051407\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135496631775\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135496893377\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135500466035\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135500622602\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13550094694\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135501487187\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135502817489\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.1355038027\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135507016339\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135507142541\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135507617529\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135508050395\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135509432322\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135510873086\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135511394853\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135512772795\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135513298233\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135513415335\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135514835246\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135515783243\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135516923939\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.13551839427\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135518906614\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135521369751\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135525215396\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13552732576\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135527515208\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135527581764\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13552810113\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135530486363\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135535841439\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135535948339\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13553685119\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135537268809\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135547056168\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.135547552866\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135547560551\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.135549532967\n",
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"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135550804344\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135553708719\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135557490821\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135563522331\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135564272844\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135566392196\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.135566659221\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135566691213\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135568786764\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13557089827\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135572716517\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135577866687\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135600343833\n",
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"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135616454245\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135617852242\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135623378587\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135623468953\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135623693514\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135628227589\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135628366164\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135635919466\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135636375538\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135637021469\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.135638505699\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135641340544\n",
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"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135641787818\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135644433694\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135644439365\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135645469619\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135648030152\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135648452607\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135648701326\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135648953956\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135650016892\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135650695118\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135651985381\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135652236305\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.135653749861\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135658863724\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135659406492\n",
"LinearRegression+Ridge+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135664675586\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135665052568\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.135665532027\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135669165674\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135670246167\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135672166606\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135677020686\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135678151924\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135682885872\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135684200226\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135684720682\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135687094958\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135687528046\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135688101921\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13569276465\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135697183108\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135697617106\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135697779827\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135700743486\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13570106988\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135701730479\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135702231229\n",
"Lasso+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135704586922\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.135704928393\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135710222865\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135710577374\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+XGBRegressor 0.135710768353\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135711810383\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135712266044\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13571315776\n",
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"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135718174849\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135719283982\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135721400833\n",
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"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135724396336\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135727613895\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.135731979865\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135737215035\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.135737975056\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135741311296\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135742816258\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135745905625\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135746020114\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135748051252\n",
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"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135751628198\n",
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"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135767920856\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.1357763668\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135789037418\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.136019614469\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136020928631\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136020947375\n",
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"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136027378439\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136027581626\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136027679673\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13602935876\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.136029835811\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136031208115\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136032241789\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136033420527\n",
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"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136036770196\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136037744883\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.136040565706\n",
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"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13604151171\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.136042089985\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136042452934\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136044672624\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136045761816\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136046843979\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13604728051\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136050605608\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.136052979988\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136054546794\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136055373953\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136057831773\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136058394276\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.136058633324\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136059900811\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136060673425\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136060703846\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136063588225\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136064106559\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136064479953\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136065391206\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136067875048\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136069362579\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136070085526\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136071330253\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136071710847\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13607191341\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136072095592\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136073055872\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136074250817\n",
"Lasso+Ridge+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136074876995\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136076535066\n",
"LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136079171575\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136079271177\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13607994584\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136081206524\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136082634567\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136085849897\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136086668747\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136088808813\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136089445573\n",
"Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136089446652\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.13609104248\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor+XGBRegressor 0.136091121398\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136091295014\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136091513289\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136091789066\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136091852692\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136092190721\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136093126255\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136096924665\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136099381508\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136101110847\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136101351301\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136101662811\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136101907453\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136102071565\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13610242698\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.136102625862\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor 0.136103110132\n",
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"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136107677167\n",
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"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136117794395\n",
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"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136122752511\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136123682488\n",
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"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136124131674\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.136125704359\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136127090767\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136127149848\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136136844629\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.136138552881\n",
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"Lasso+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136143960739\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136144338225\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136149332091\n",
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"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136150093596\n",
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"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136152919733\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13615331202\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136155103257\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.136155240398\n",
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"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136161191083\n",
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"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136162494727\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136165563792\n",
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"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136171300671\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136171473185\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136172911117\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.136175853108\n",
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"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136417456285\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.136435995216\n",
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"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136436745246\n",
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"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136440601481\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136441169425\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136441825006\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136446897404\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136554688143\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136557935771\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136558114336\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136560424098\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136560948871\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136561703921\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136562440829\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136562799826\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136564644661\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136566825738\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136567275194\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136567699907\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136568747841\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136569407408\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136570040713\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136570718796\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136572820225\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136573577868\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13657376908\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136575791939\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136576284767\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136576858141\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136579273558\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136582892069\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136583305861\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136585514916\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136586028996\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136587083489\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136587182798\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136587437934\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136587878033\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136589260948\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136590369344\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136590832792\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136591828935\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136593459343\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136593570322\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136595884161\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136601193096\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136603242195\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136603359847\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136603767432\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136605551772\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136606318336\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13660903445\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136609418952\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13660970665\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.136609812499\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136609816595\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136610780241\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.136614439318\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13661646144\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13661702303\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13661774036\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136617750289\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.136619450523\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136620701181\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136623753924\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136624672963\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136626059731\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136626924122\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136627139606\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.136627904426\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136628422773\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136628485032\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13663000225\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.136632487415\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136632528103\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136633206343\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.136633804263\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136635101037\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136636779119\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136636947837\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136637080853\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136637118646\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136639582874\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136640299383\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136640665914\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136641273547\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136645448059\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.136646779492\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.136647696218\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.136649513222\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136651515628\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136652530138\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136653119622\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136653397718\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136654178051\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.136656812426\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.136657087657\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136657145453\n",
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"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136662759128\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136662802738\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136662862532\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor 0.136662865638\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136663566643\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136664028883\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136664513958\n",
"Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136665806716\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136666818959\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136668387452\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136669259098\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.136669921257\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136670680248\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136670948159\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136671308059\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136671428653\n",
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"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136675312568\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13667710042\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136678268789\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136678359937\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136678610064\n",
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"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136679620278\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136680018754\n",
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"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136683610739\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136696438926\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136697167574\n",
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"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136711879755\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136714890645\n",
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"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136745905554\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136749334586\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136749476102\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136749737005\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136750495041\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136751433532\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136752059539\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136752762258\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136754213644\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136754398963\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136754709095\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136754761759\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136758222427\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.136758862868\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136760158844\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136760334653\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136761285155\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136763279414\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136763811005\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.13676630194\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136766709105\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136766908998\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136767008832\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136768922147\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.136769800355\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136772837032\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136773502257\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136773965666\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136775026886\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136776682525\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136777604432\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136780157753\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13678085587\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136781570101\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136782521569\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13678546885\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136786171393\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136787179899\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136788417627\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.136791151275\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.136791765931\n",
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"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136797026271\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136797805302\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136798425939\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.136798749158\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136799243634\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136802632638\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136804992574\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136806743489\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136810166027\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.136815897084\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136819610151\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136819706515\n",
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"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136826559798\n",
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"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136922819025\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136923051255\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136933217585\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136935876853\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13693749421\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136938356915\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136938667778\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136939359445\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136940276822\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.136941467683\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136941550387\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136941641719\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.136941682406\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136942272696\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136946230414\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13694657903\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136947179436\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136947667671\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136950670198\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136951417005\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136951420176\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13695213897\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136954002604\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136957494968\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.13695794741\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136958798203\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.136961215808\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136961740091\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.136961937394\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136962473202\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136965155174\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136965550228\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136967892987\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136971605506\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136971738542\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136972583716\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136973503283\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136974347453\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136974535798\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136977626578\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136979280693\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136982206319\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136984209251\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136986876129\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.136987604218\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136993069934\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136994905039\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136998155452\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137000792684\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137001732601\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137001750484\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137002541251\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137002922274\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.137005300365\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137007080105\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137008079321\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137008302238\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137008666136\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.137008785881\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137010755385\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137011951778\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13701270334\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137014879525\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1370149167\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137019307763\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137020881326\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137021024627\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137023541038\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137025361002\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137027561316\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.137030091736\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137030432227\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137030711476\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137031872277\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137032270266\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137032521281\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137033169036\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137033612446\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137033730394\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137034482715\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137035277864\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137036601033\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137038093773\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137038119956\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137038122541\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137038813953\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor 0.137039106812\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13704091257\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137043097334\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137043438326\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13704360401\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137044666303\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137045711655\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137046772864\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137048256451\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137051087608\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137054796044\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137058757428\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137058783686\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137060704624\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.1370615614\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137061595825\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137062848608\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.137065046766\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137065439484\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137065723395\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137067127651\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137068572588\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137069749533\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137072720697\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137073882804\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137075165425\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137075597484\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137076799167\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137079043923\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.137079303948\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137081262483\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137083515735\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137085815913\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137087709243\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13708848629\n",
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"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.137089980831\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137090623529\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137094413224\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137095998513\n",
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"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137099425249\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137100497763\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137102802871\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137108896339\n",
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"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137122001581\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137124876475\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137125769821\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137128061778\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137131875606\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137185342462\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137188871524\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137189591571\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.137190536912\n",
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"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137192911315\n",
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"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137194417304\n",
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"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137199747082\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137200380148\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137201079536\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13720141565\n",
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"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137204259798\n",
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"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137205088336\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137205147794\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137206379287\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137206428772\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137207287693\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137208129267\n",
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"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137221597778\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137222029868\n",
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"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137223406239\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137223640833\n",
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"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.137332583851\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137334877959\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137335519655\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137338139885\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137338262705\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137339629324\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137339980109\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137340021708\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137340903879\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137341282564\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137341598025\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137342285296\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137345258295\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+XGBRegressor 0.137347218683\n",
"Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137349416198\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137352100071\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137354827728\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137357665984\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137360787577\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137362617311\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137362969249\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137363643755\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+XGBRegressor 0.13736494527\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137367922338\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137368190932\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137368222132\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.137369642347\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137371338094\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137372241944\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137373785829\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137374998496\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137375284844\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137378332825\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137378949727\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137380519489\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137381296088\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.137381660833\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137385479646\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137386433398\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137387059903\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137389280384\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137390580303\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137391114952\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.13739300945\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137395552007\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137398476959\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13739915757\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137399249028\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137400295374\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137403212927\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137403392612\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137404571027\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13740627357\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137407970183\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.137416322873\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137416865718\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137417212032\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137417801618\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137419811409\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137419837288\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137420401994\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137420547093\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137421544313\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137425229764\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137428038413\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137428915006\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13742938514\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13743026245\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137430674046\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13743212155\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13743489423\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137437614531\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137440920178\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137441145366\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.137441339296\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137443495738\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137443836586\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137444172667\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137444213302\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137449246687\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.137452237474\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.13745252937\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137454427121\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137455453082\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137457860867\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13745816854\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137458297962\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13745960255\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137459622116\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137461357446\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137461403713\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137462210573\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137463752916\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137466597323\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137466899745\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.137469516328\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137471401303\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.137471680358\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137473218067\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137473711949\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13747440923\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137474504605\n",
"LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137474698142\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137476116049\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.137476830348\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137477639187\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137481199163\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor 0.137481864554\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137485124812\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137485631871\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137486636377\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137486746251\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137487679508\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137488347031\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137488532077\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137490467244\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137490916126\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137491390062\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137491450932\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137492382766\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137493087939\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137493691439\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.137494292266\n",
"LinearRegression+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137495390323\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137498855184\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137505115892\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137506308216\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137507672918\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137510135236\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.137510606471\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137510976364\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137512100645\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137512695384\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1375134566\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.137514476051\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137514715754\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137515621903\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.13752104864\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137521468052\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137522900144\n",
"Lasso+LinearRegression+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.1375243309\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137524597016\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137525575265\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137527377662\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137527975633\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13753470062\n",
"LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137536236995\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137537843814\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137538436501\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13754053624\n",
"Lasso+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137541234853\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13754133768\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13775959923\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.137760274963\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137760359541\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137761509576\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137763442454\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137765765456\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137766571998\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137766942197\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137770172522\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137772119339\n",
"Lasso+Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137773553523\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137773991139\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137774343363\n",
"Lasso+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137775357285\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137775961782\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137776981755\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137779250325\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137779287521\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.137780752096\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137780874651\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137783946503\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137785672571\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137786840754\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137790923181\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137792132697\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137796870556\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137797672341\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137799622502\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137802718004\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137808327522\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137814465422\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137815453174\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137815579356\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137817844108\n",
"Lasso+LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137821037473\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137821125174\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.13782264253\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137827206955\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137827789239\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137828265725\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137830073285\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137830091612\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137830939352\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137833039188\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137833546558\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.137836153102\n",
"Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137839341264\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137839817568\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137840233701\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.137841321009\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137841474538\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137842690867\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137843669514\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137844117341\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137844413214\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137845668943\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137847975473\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137849567653\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.137850567368\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137851146573\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.137852384488\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13785414332\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137854609859\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137854615952\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137858146127\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137859410983\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137860723732\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.1378635267\n",
"Lasso+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137868710981\n",
"Lasso+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137872181382\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137874796317\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137875554008\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137876935057\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.137876962338\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137877468401\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137878852945\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137880416333\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137880833089\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137881585431\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137882195088\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137884115758\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137885224605\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+XGBRegressor 0.137886737621\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137887866795\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137888648779\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137888952017\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137889011047\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137889364309\n",
"Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137892577768\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137892603724\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137893680828\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137897291855\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137898558503\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.13790764006\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.137907913814\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137910977275\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137911235316\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.137911304349\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137911853334\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137913941164\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137914416344\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.137919073696\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137919409106\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137923963786\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137925808029\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137926302404\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137926450777\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137928852921\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137929158691\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137929301456\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137929867291\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.137930038929\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137938572269\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137938901113\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137939614188\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137939844878\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137946167129\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137947977921\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.137948862665\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137949072977\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137949219995\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137949521757\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137950508881\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137951041695\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137955844927\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137957607745\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.13795773316\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137958036063\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137960357926\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137960497411\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137960949328\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137962459088\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137964455084\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137967879454\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137968048554\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137968358947\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137969373405\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.137975263202\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137975653051\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137977353993\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137978299126\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137978598448\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.137978664376\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.137978879685\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137978921287\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13797953824\n",
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"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.137983419439\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor 0.137984033364\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137990478946\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13799174082\n",
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"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137992323473\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137994239463\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137995535362\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137996647287\n",
"LinearRegression+ElasticNet+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137998051413\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13800075959\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138001091159\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138001183267\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138004024044\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138004497577\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138004906193\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138005418667\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.138006963538\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13800755392\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138008439208\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138009843629\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138010597018\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138011141156\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138011184361\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138011503629\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.138011880056\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.1380124406\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138014456593\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138014512962\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138014740796\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138015546106\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13801770275\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138018836006\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138022281838\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138024876144\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138025385007\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138028968182\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138030118884\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138031828382\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138032847318\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138033641899\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138034143498\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138035053328\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138042373802\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138044062441\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.13804826157\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138050702889\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138050789375\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138052095511\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.138053339264\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138053733767\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138053794615\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138054616219\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138056583933\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138057844576\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138060984391\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138062031479\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138063060717\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13806515057\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.1380677789\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138067952695\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138068105157\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138069035205\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138069603152\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138070702005\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138071908051\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138073676513\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138075932621\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138084088458\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138084676022\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138085466914\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138086021282\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138091102995\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138093147615\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138093349664\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13809350534\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138094499591\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138094735094\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138095012566\n",
"LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138096233446\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13809676499\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138098238214\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138099857274\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138101921562\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138102655963\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138103515882\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138103721463\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138104162576\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138105492752\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138108309053\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138112155309\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138113543882\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138116338704\n",
"LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138117648341\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138118149559\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13811819661\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138123305251\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138126604629\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138127415502\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138128676111\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.138129054046\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138132623822\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138135638718\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.138135638819\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138138052332\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138142476593\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138144393683\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138146291398\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138146438157\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138146925622\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138148571355\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138149235678\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.138150617456\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138156072989\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138157673103\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138159583189\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138159820526\n",
"LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138161969704\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138162335585\n",
"Lasso+LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138165686705\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138167148781\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138167888138\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138172582985\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138172739329\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138174776167\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138175350288\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138176836292\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138177233633\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138178871182\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.138179546249\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13818348025\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138186153731\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.138187862286\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138189255645\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138194259493\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138194989171\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138195037854\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13819599707\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138196153409\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138196674143\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138197322513\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138201452307\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138203322641\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138204259151\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138204494365\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138204495136\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138205483573\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138206616944\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138208227343\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138208849768\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.13821054483\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138211079618\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138212667399\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.138216454638\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.138216727611\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138217193406\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138217386695\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138219076561\n",
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"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.138228455054\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13823077576\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138233137873\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138236792886\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138237131929\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138237881125\n",
"ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138239632006\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138240505709\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138241765724\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138243066938\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138248843979\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor 0.138250521483\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138250958467\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13825166228\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138251742165\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138252570905\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138258149461\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.138258980491\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13825954287\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13826598867\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138266347025\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13826751819\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13826965093\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138269870142\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138269912425\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138270400035\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138270631277\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138270966975\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138272430578\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138273376909\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138275489728\n",
"Lasso+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138275753662\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138278191775\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138278441967\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.138282269447\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13828759121\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138288283135\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138288312915\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138289625675\n",
"Lasso+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138289642573\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138291527107\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138296335821\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138300328302\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138300509536\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138300845446\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138301712049\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138302124876\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138303620578\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138307010045\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138310730434\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138310837446\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138311645154\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138317018276\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.138317439262\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138317981634\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13831842856\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138319513917\n",
"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138321197606\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138322094073\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.138322255917\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.138323582975\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138324854012\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138326090613\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.138326705186\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138327807511\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138328704373\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138330795632\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138332031342\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138332723135\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138333223722\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138334579733\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138337278477\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138341989463\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138343202091\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138345775748\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138348991859\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138349714518\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138349997049\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138349997413\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138350315578\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138350615447\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138353262176\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138353947089\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138354849739\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138354873964\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.138357223051\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.1383575297\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.138359253042\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138360698349\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138368552239\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138369825648\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138372555192\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138376249222\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138378151044\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138382116907\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138383915158\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138385722782\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1383859721\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.138386286426\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138387317984\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138389605491\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138391536657\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138391769761\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13839795882\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.138398548754\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138401060471\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138403470887\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138406788452\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138408661753\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138410228005\n",
"LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138411513666\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138412042018\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138415177883\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138418553211\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138423257118\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138425079794\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138425233884\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138426644579\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.138427655427\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138428227786\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138430402469\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138430842116\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138431176685\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138433154615\n",
"LinearRegression+Ridge+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138435395375\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138436024367\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138440007306\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138446079599\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138446537152\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138446747842\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138447100408\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138447339078\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138448364635\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138450102093\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138450501245\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138453676299\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor 0.138456800995\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138458091138\n",
"Lasso+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138461082524\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138462339853\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138463938546\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138464554606\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138466299766\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138466465385\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13846827106\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138468772837\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138469325421\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13847073495\n",
"Lasso+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138474765698\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13847532246\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138477771089\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138478746598\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138478795951\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138480925135\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.138484605773\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.138490346098\n",
"Lasso+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138491381487\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.138491771398\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138493108434\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138493960758\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.138495920034\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138496769385\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138498055178\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138498147498\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138503507364\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138505141508\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13850581189\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138506851066\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138506900411\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138507626689\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138508387518\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138508834642\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138511306883\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1385132359\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138513631327\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138515216977\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138517646022\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138517898423\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138518874223\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138519393356\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138520609444\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138520899776\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138523633452\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138524185546\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138525322699\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138525807896\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13852613219\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138528387999\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138534071908\n",
"Lasso+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138534075598\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138535091761\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13853523146\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138537850278\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138537886987\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138538368385\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138539288758\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138540990954\n",
"LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138542231453\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13854275005\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13854385783\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138543865037\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138545366893\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138546134648\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+RandomForestRegressor 0.138546212626\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138546980887\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138550002849\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138550242947\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138550819863\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138551660852\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138554119417\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.138555132953\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13855781775\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138561549917\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138562472394\n",
"Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138562563522\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138568609325\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138569148206\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138569659921\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138572315805\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138572336098\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.138573059411\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138573688228\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138575451212\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138576051922\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138576472596\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138577017097\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138577821646\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138578306946\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.138579919977\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.138580050401\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138580661161\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.138580669652\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138580699067\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138585468911\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138585507361\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138586909609\n",
"Lasso+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13858704336\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138588536188\n",
"LinearRegression+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13858886578\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138588909481\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.138590991\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138593225798\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138594075561\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138594298097\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138599588525\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138600650771\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138601065302\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13860118616\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138604655958\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138605104226\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138605839769\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138606399823\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13860659443\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138607031494\n",
"LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138607043251\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138607195545\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138607433999\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138607581022\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138607756144\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13860821128\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138608704104\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138610554348\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138611202881\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138611535533\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.138611769041\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138612022721\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138612416234\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13861259024\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138613772104\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138614519393\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138616201935\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138616604358\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138616804402\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138617356136\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138619772134\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138620133613\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138621463403\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138623253575\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13862474961\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.138626479161\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138627285133\n",
"Lasso+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138627953922\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138627984399\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138628300189\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.138628644618\n",
"LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138630715408\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138634840452\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.138634929993\n",
"Lasso+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138634937654\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138637360614\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138637472878\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138640769589\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138641015442\n",
"Lasso+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138644298945\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.138647150566\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138648318084\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138649397526\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138653412465\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138653730271\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138656643766\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.138658445228\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138659506104\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.138660614174\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.138664922561\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138665819767\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138666485994\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138666580568\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138667536171\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138668772063\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138671264608\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138671280831\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138674478138\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138675059992\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138675553085\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138675555629\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.13867619385\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138676233188\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138677067519\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138677177447\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138679890626\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13868405245\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138686727688\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138687188292\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138688621869\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138693108855\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138694925023\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138697245664\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138698446182\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138698756323\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138698773508\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138700338351\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138700504587\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138704445972\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13870604143\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.13870641322\n",
"LinearRegression+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138709818342\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138711065686\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.138711459945\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138712781207\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138713545209\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138713594463\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13871427247\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138715524805\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138715854306\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.138716842855\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138719112205\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138720139875\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138721808124\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138723205546\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138723841912\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138723936054\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138728408064\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138730072562\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138731921826\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138731974842\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138732635216\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.1387328572\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138733920045\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138736598487\n",
"Lasso+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138738295213\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138741040538\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138741201127\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138742180403\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138742896693\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13874399222\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138744102923\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138744211605\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138745209982\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138749050122\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138749570744\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.138750358245\n",
"Lasso+LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138750609545\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138751589996\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138752629257\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138753603264\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138753689905\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138754979988\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138756271995\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138756384186\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138757741048\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.138758677101\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138762981649\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138764607831\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138765005604\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138769757492\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138770804007\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+RandomForestRegressor 0.138771555911\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138773394907\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138773982067\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138774671304\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138778257398\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138778617414\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.138780270422\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.13878086253\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138784147199\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138785393264\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138786106444\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138787691361\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138789107303\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138789786964\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138791746126\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138791784175\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.138792887108\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.138794006768\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.138794932532\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138795011161\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138795083934\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138796230035\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138801568791\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138801724819\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138802047177\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138806568741\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138807757906\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138809673691\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138811885999\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138814324473\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.138814625804\n",
"LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138815555269\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138818085417\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138818394909\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138819615954\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138821222614\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138821736143\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1388227287\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1388228319\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.138823440874\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138824645535\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138826375851\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13882727337\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138827521484\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.138829561481\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138832827188\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138833511424\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.13883514021\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.13883528351\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138835811667\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138836083903\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138837010844\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138838114033\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138840077936\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138840887988\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138842784175\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138844111636\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13884415121\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.138846010761\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138848488565\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138848626322\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138848678098\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138849358609\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138850255676\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138850999813\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138853920232\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138854699841\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138855235872\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138857111262\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138861083889\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138862560664\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138863491544\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138865498207\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138868045148\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138871106202\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138871234431\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138871264356\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138872518101\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138875073304\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138875448816\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138877265026\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138877530909\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138877758346\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138880359115\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138882396313\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138884135498\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138885885783\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138887248773\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138888046489\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138889739062\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138890051806\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138891943176\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138894208761\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.138899383073\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138904512554\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138907727069\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13891456729\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13891476576\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138918512753\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13892157948\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138924786325\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138925917276\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138930477396\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138932935649\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138935100497\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13893757474\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138939242717\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138940385641\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138941580157\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.13894221071\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138945212128\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138946626153\n",
"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138949516719\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138950881005\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138952376749\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138952982312\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138954292334\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.138956767608\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138957807255\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138959456964\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138959682287\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13896048927\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.138965838983\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138967864274\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138968659087\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138970936952\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.138975996059\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.138981639643\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138982723264\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138984216266\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138984877117\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138986147778\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138987914192\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.138988487964\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138989573711\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138990017527\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.138990697962\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138991426727\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138993088631\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138993142797\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138995342643\n",
"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138995555485\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138995849724\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138997393528\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.13899874655\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138999656943\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139000942488\n",
"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139001616993\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139001883446\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.139003635104\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139006293843\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139010003409\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139010748114\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.139011016821\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139011720989\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139013473794\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.139014017668\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor 0.139017843055\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.1390192455\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139021269685\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139024742971\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139026807149\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139028432088\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor 0.139029636112\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139033786994\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139036842215\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139036961118\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139038075752\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139039580581\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139043599856\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139043883273\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139044171435\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139045405129\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139048486017\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139050575464\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139050935046\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139054267982\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139057652458\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139058208477\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139058855197\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.13905926063\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139059819607\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139060082752\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139062124641\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139064389624\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139065264447\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139065332536\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139065617976\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139070888061\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139077826768\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139078228248\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139079488819\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139082122797\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139082582278\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139083437047\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.139083549864\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139088732688\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139093171526\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139093299768\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.139094986371\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139095605889\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.139099926551\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139101986627\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139102472421\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.139104554025\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139105595783\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.139108781703\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139109018394\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13911048707\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13911050364\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.139111796254\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139114767592\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139115111384\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139116956441\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139117179616\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139117422348\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13911801656\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.1391185655\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139123220033\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139123516177\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139123945714\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139125331924\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139125457112\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.139125550446\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.139126877527\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139127909968\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139128161025\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139128353769\n",
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"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139607465441\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139607650577\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139607672494\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139607979351\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139611781977\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139614436737\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139617703258\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139618568435\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139620825022\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139620995813\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139621671034\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139624553759\n",
"TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139626503042\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139626542148\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139626696398\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139628040425\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139628840394\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139629546437\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139631673877\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139634939578\n",
"Lasso+LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139635630958\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139635867596\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139637859943\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139638639626\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139639761803\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139642977305\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139642988366\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139647609361\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139649756074\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139650043046\n",
"LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139650217496\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13965228212\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139652339887\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139657738698\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139658879925\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139658980735\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139660333808\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139665010803\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139667246525\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139667556383\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139670339049\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139674496915\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139675795918\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139676701128\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139677556185\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139678034211\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13967926926\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139685502662\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139690398501\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139692062792\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139694377517\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139694825329\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139694936345\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139696578635\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139698643954\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139700336652\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139705327769\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139705940378\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139707729969\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139708375107\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139711746213\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139713564175\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139713785701\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139714754171\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139721317952\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139723908425\n",
"Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139728160487\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139728363778\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139728531845\n",
"Lasso+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139730659862\n",
"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139732660926\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139733907751\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.139736926219\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139737953604\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139739874361\n",
"Ridge+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139741462588\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139742238468\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139743462655\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139745673719\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139749568219\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139750016533\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139754747849\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13975577082\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.139756133313\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139757581881\n",
"Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139764929711\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139767990534\n",
"LinearRegression+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1397707609\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139771729539\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139773233212\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139773403636\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139775162626\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139775812106\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139776914702\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139778464486\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139786867924\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139789995861\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139794289132\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139794408739\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139794462004\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139795664134\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13979693981\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139800037608\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139802557056\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139802812527\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139804679788\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139805791749\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13980780491\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139810001203\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139816822488\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139817805202\n",
"Lasso+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139819276567\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139820463697\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.139821704611\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139822565976\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139840257139\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor 0.139841281023\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.139841438005\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139842325601\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.139845947136\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139846118038\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139848176495\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139848612909\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139849259912\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139850472498\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139850766135\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139855768811\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139857428422\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139861635177\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139864967623\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139865872384\n",
"ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139866854562\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139872537653\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13987352404\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139873767413\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139873863369\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139874743463\n",
"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139881646852\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139886222933\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139887286257\n",
"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139888312864\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139888480726\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139890130152\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.139893770742\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139894190316\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139894794167\n",
"LinearRegression+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139895031393\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.139895980602\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139897444829\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139900196226\n",
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"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139903268225\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139903510345\n",
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"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139905366055\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139908784882\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139911031328\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139911053267\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139913641841\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139915388634\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.139919722316\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139920251138\n",
"LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139921476033\n",
"TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139921828696\n",
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"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139929964086\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.13993036589\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.139934966027\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139936638315\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139937657925\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139939424906\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139945342839\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139960541701\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.139960798811\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139963319413\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139964779718\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139965052289\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139965664863\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.1399659086\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.139971898679\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139972785014\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139974894198\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.139979348038\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139982520626\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13998505877\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.139992356482\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139993020087\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139993478648\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139997453404\n",
"ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139997481925\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139999576302\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140006142837\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140009438884\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14001822896\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor 0.140020931727\n",
"Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140021831784\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140024532751\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140027452007\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140029419765\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140034765109\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140037878164\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140038691139\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140040656457\n",
"Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140041042632\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140044843212\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140045447033\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140045459364\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140048580227\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140049230365\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140051548318\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140052255788\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140052717767\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140062802672\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140067877543\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140068387589\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140072989505\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140073651047\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140074275879\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140077591883\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140084508555\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140084572432\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.140088141916\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140090785661\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140092093393\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140093470576\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140096032177\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140097438282\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140106804874\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140119677928\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140125073403\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140125231907\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140129553635\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140130890567\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.140132703139\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140134424917\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140135884851\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140136559892\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140140347936\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140140982527\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140141319918\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140145118631\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140145368556\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.140145826169\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140148898463\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140151754167\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140153602287\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140153664185\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140154867019\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140156917309\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140160670015\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140162249153\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140163568671\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140166258343\n",
"Lasso+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140168731853\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.14017519917\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140175833149\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140178745509\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140180265126\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140182152217\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140187392238\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140187529768\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140187947319\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140191065331\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140196944582\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140198555153\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140198949281\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140202491349\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140206470677\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140210758628\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140212105945\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140213155754\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140214072307\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140217286927\n",
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"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140228208673\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140229575637\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor 0.140229705849\n",
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"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140232929802\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140235071067\n",
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"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140246802172\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140256219253\n",
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"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1402603645\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140260458126\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140260548267\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140260986055\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140298571309\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140337357163\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140344058392\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140346151324\n",
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"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140349163833\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140349727353\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.140361933951\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14036569586\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140366372127\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.14036658562\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140370859066\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140375504878\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140377181324\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140377442557\n",
"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140378763042\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140379202926\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.140379718972\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140380091652\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140382386867\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140383488085\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140383742339\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140386321709\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14039661623\n",
"LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140396765307\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140401054265\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140402010305\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140402113773\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140409007189\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140412934521\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.140413410874\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140416579188\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.140416634172\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140423720749\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14042411098\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140425823408\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140426591111\n",
"Lasso+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140428387705\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.14042851898\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140430624194\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140431022953\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140431229267\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140435128631\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140438623292\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140438834208\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140438914354\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.140439214597\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140439436492\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14044275743\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.140446580155\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140449024818\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140455688186\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.140458731581\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140460636363\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140467652293\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140472841807\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14047614428\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140477172236\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.140477174426\n",
"Lasso+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140483226894\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140487558267\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.140488078234\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.140488555789\n",
"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140489257457\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14049015589\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140490582896\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140493014008\n",
"Lasso+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.14049344553\n",
"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140495745938\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1404986294\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140499418501\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140499424246\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140504423006\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.140507343828\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14050752577\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14051044333\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140511025997\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140511229924\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140517824332\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140518631631\n",
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"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140519735257\n",
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"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140528554791\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140530555681\n",
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"TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140543710745\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.140543900539\n",
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"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140553306056\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140555699065\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140559192104\n",
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"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140562286145\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.140565461979\n",
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"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140566041921\n",
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"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14057873592\n",
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"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14058127421\n",
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"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140610747068\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140615980858\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.140617692757\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140618121161\n",
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"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140752683277\n",
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"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141046910097\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.141054077461\n",
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"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141062521755\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.1410690087\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141080696384\n",
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"LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141084829914\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141085565809\n",
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"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141101162451\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141105447925\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141107371184\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141111766206\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141112620969\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141118581237\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141121217357\n",
"Lasso+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141122652379\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141126202714\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141128167481\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141131260726\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141134210535\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141136006215\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141136278185\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141136476069\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141137426456\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141138721624\n",
"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141139180591\n",
"Lasso+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141143108534\n",
"Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141147456558\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141147653357\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141149527289\n",
"ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141150232702\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141152666156\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141153723213\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.141156181212\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141160847002\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.14116660172\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14116706684\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14116949663\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141170488786\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141174112577\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141174248664\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141174997015\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141177462219\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141182333209\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14118770517\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141190421927\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141191189536\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141191671766\n",
"LinearRegression+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141200554532\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14120485548\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14120665748\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141210146477\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141212368969\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141213747988\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141223720645\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141225605044\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141227128276\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141232488312\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141233091699\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141233743\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141235002479\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141235977319\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141238103375\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.14124776281\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141248428039\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14125170383\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14125803599\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141264458857\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.1412658259\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141267257263\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141271952045\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141272044725\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141275518691\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141276165386\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141280632103\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141289016461\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor 0.141290688377\n",
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"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.141296380827\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR 0.141296933705\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141301760993\n",
"LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141304711647\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141306144015\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141312054173\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14131703291\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor 0.141317152372\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141320178129\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141320183297\n",
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"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141324089141\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141324297891\n",
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"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141328189004\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141330046583\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.141333797329\n",
"LinearRegression+Ridge+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141334526299\n",
"TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141335002563\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141337459454\n",
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"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141342556794\n",
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"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141350377478\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141350447008\n",
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"LinearRegression+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141352066886\n",
"Lasso+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141352857118\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141355080404\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141367005112\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141369846682\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141372202366\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141375764482\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141381521411\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141384935218\n",
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"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14138722448\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141387740422\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.141404329392\n",
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"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141412072902\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141415574145\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141420362598\n",
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"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141423827166\n",
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"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141425058231\n",
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"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141495313005\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141502403156\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141508215613\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141510838079\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141511700306\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.141518127864\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.141519048426\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141520541593\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141521737337\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141522044554\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.141523610028\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141524767722\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141531564017\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141533075134\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141539919155\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141540461498\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141542557771\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141545921483\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141550545343\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141554583612\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141554659355\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141555685732\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141556215509\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141560412343\n",
"Lasso+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141560753755\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141562612245\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141563478049\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141569484241\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141572590514\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.141575373538\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141582120714\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141582397964\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.141585904151\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.141590974074\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141591757794\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141594630963\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141595740515\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141595911791\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14159648715\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.141600090132\n",
"Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141604133436\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141609883551\n",
"LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141610095728\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141610189862\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141621767743\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141621944932\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141642190485\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141647688749\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141650955772\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.14165451441\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141655079085\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141659810806\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141661018253\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141666359168\n",
"LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141666816724\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141668752531\n",
"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.14167107599\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141675543299\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141687690417\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141689255368\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141693709176\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141697229529\n",
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"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141703692007\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.14171085201\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141718107406\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141721976817\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141724596074\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.141727114011\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141728889729\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141729367714\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14173601744\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.14173823299\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141738375664\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.14173938684\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.141750232945\n",
"LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141754115759\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1417612519\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141769403216\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141770406374\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14177416857\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor 0.141774894817\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.14178775527\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141788878481\n",
"RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141792916569\n",
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"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.141827241166\n",
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"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141836403982\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141840314374\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141843728811\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141850285286\n",
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"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141861389865\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.14186270307\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141867680061\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141874853178\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141874929807\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141875628371\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.141877629507\n",
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"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141921374779\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.141928762351\n",
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"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141933194969\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.141935619978\n",
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"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.1419416037\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141945222742\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141974577165\n",
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"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.141986100756\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142824284727\n",
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"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142836874863\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.142851152634\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.142873336255\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142880938047\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142882520196\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142888181353\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142891320021\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.142895432652\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.142896450817\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142910969317\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142919378489\n",
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"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.142925405768\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.142932600067\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142935709689\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142956843031\n",
"Lasso+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.142964021452\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.142995928801\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142997417834\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143018317034\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.143020531575\n",
"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.143023035555\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143029203746\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.143043883242\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143045909279\n",
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"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.143052925411\n",
"RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143080443465\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143096470842\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143102391408\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143112303793\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143115279248\n",
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"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143137898806\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143141175753\n",
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"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143142803633\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143145687208\n",
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"Lasso+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143148633586\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143149172454\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143168869435\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143175262947\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.143238545263\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.143246566476\n",
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"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143270878945\n",
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"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143277640453\n",
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"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.143287676033\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.143296304645\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.143302312842\n",
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"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143367409912\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.143389198282\n",
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"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.143872022419\n",
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"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143906626972\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.143916034997\n",
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"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143923811526\n",
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"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14395123546\n",
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"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.143995060978\n",
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"Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144017898069\n",
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"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144062525359\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144067331957\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144080235907\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.144084529928\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144089359135\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.144096654549\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144109290393\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14411152263\n",
"ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144117792912\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144133697055\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144147214419\n",
"Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144150998231\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144159168183\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144170789995\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144197912553\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144206326551\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144225890896\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.144228337678\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.144229414849\n",
"Lasso+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144232873981\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.144264005901\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144276481515\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144285626886\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144308738561\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.144313208396\n",
"Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144314231225\n",
"ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144314803543\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144345880934\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144346472772\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144347432128\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144347743644\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.144350321596\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.144355150228\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144355654657\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14436298069\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144364149166\n",
"LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144367805921\n",
"ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144375882763\n",
"ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.144386347168\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144411705968\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.144413960855\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.144417378433\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144417496455\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.144426938427\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144429759111\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144433728162\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.144445866279\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144454675681\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144473512218\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144495663159\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.144522762218\n",
"TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.144524514486\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144557806224\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.144585054768\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.144585300155\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144602873484\n",
"HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.144630136287\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.144643075122\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144660926715\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.144697028312\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144726267216\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144726693948\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.144734748662\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.144764367296\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144814710411\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144834770409\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144843036954\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.144855094187\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.144865793044\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144868066922\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.144882493731\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.144895758443\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.144937539825\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.144948193802\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144977987535\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.144980647897\n",
"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.144983712488\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.144984146613\n",
"Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145008050904\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145085115842\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145113554733\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145117846624\n",
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"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.145248008842\n",
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"RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145258390834\n",
"Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.145273272561\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145323254845\n",
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"ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145415444388\n",
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"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.14550926391\n",
"TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.145558225792\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.145579547053\n",
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"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.146027185705\n",
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"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.1460761026\n",
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"RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146180171314\n",
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"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146224328437\n",
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"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.146628691123\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.146662308926\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.146718628588\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146744628055\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146924511997\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147040486539\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147055060832\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.147059697299\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147063552153\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.1471417954\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147367022614\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147367663291\n",
"HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.147456226863\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147481625282\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14756747949\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.147656665835\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.147906488044\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.148196480443\n",
"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14822776302\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.148247437305\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.148323296307\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.148337472982\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.148346476929\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.148359572409\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.148497809631\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.148518773833\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.149022684929\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.149034439511\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.15101196731\n",
"\n",
"Model Amount : 9\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12914274306\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129186347736\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129225687127\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129281588765\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129293233401\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129326127481\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129363315144\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129533807562\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129637052724\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129657811728\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129713677098\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129735157779\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12976709436\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129798320102\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129834540695\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129834690114\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129849842538\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129866720996\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129911682811\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129933061344\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129942083208\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.129963211887\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130053935915\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130076173011\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130090149231\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130091011145\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130133341199\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130149979213\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130156454207\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130162820693\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130180156015\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130245922127\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130254180292\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130254540184\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130258780737\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130264385896\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130278257273\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130307520137\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130329911028\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130336357138\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130344460068\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130377128415\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130395222442\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130407603787\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130411604188\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130441159986\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130443948537\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130460425456\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130462534127\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130486398029\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130507594543\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130531962935\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130537159813\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130539129948\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130541152479\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13054338767\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130544799752\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130549362937\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130571641031\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130577834258\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130578143288\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130582271645\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130595407653\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130602016506\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13060645689\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130621883859\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130631048509\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130636079254\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130656956132\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1306721852\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130674955645\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130677590989\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130682930506\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130708914347\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1307394609\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130758681868\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130780726343\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130802465373\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130802791038\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130802973365\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130831613812\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130841940421\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130867627176\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130869951554\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130898823636\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130912063194\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130912396175\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130919940706\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130922511275\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130948637116\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130957414002\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130963759009\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130977237834\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13101352145\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13102222081\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131055496809\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131055694986\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131078197677\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131089230622\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131090945637\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13109198\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131098010387\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131110983682\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131117003712\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131117239351\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131128701978\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13112880505\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131142136612\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13114564923\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131157450892\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131160575929\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131167371211\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131180936921\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131182058644\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131184566119\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131204106543\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131216354267\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131220153089\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131220234332\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131220878634\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131233097161\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131235747369\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131249904149\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131263320193\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131267942363\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131275664833\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131277614036\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131286540767\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131286914441\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131295273598\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13131189466\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131326030676\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131336427753\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131338433229\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131352870176\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131356067909\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131380505696\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131381848313\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131395643965\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131401572311\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131401967017\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13140439821\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131406754569\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131410412037\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131411037124\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131433844177\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131435584188\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131443808441\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131462588108\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1314695876\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131469853768\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131485480889\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131490283689\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131513650883\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131515736892\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131519365444\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131526722468\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131529350695\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131530450372\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131544732716\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131548269395\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131548743019\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131549230715\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131551699757\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13155278596\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131557501062\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131576813308\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131585838133\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13159027906\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131597098107\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13159723328\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131600651025\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131602699239\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131619123266\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131628991908\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131631415864\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131631480712\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131635612829\n",
"Lasso+LinearRegression+Ridge+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131637961218\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13163811133\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131639127668\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131642375489\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131653750101\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131669107333\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131669610861\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131671441341\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13167355983\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131675949116\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131681867012\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131693488464\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131705797098\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131723674341\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131726041021\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131735831762\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131765633697\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131773087846\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131784174777\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131976250312\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131983978657\n",
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"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132119041947\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132126645658\n",
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"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132139674803\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132143706078\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132143840418\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132146485554\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132147053864\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132150466979\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13215359735\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132162264795\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132164833749\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132167214655\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13217068754\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132172015825\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13217370218\n",
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"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132176336204\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132181169293\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132192671485\n",
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"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132201584096\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.132202982052\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13220541189\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132219382833\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132267751509\n",
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"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132280889251\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132283782982\n",
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"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132303627289\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13258444837\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132587390966\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132593788799\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132593943423\n",
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"Lasso+LinearRegression+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132596443446\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13259865734\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132600530276\n",
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"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132602968736\n",
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"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132603742917\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132606821309\n",
"Lasso+LinearRegression+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132607164651\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132614096753\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13261459414\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132614963608\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132616727743\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+RandomForestRegressor+XGBRegressor 0.132622171072\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132623961584\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132625549156\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132627687362\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132630102351\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132631676199\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132632327817\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132634483705\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132635136281\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132641139114\n",
"Lasso+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132655428563\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132655897492\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132656839538\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132656855993\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132661056046\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132670564359\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132671526646\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132673975554\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132675115495\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132676140165\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132686775166\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132688310262\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132695701983\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132699649882\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132703223984\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.132704098195\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132704416551\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132704932485\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132709416053\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132711454066\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132713696433\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132717277408\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132720811365\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132727429705\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132734611487\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.132734960549\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132735071999\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132745282076\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132748574392\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13275472665\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132758486947\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.132765945664\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132774565868\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132776616939\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132779155412\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132780455021\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.132784081136\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132784203208\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132786125661\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132788657932\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132789682124\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132792627998\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133002167183\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1330052081\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133006941203\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133010549218\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13301389141\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133019083924\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133019251402\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133020501296\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133028219667\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.133030103562\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133030978724\n",
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"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133032571169\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133033683012\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133035187674\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13303601691\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133038640775\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133039186544\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133040648608\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133041687152\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13304564167\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13304680996\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133047604423\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133047995446\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133048797844\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133055738854\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133056043741\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133062094279\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133063128226\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13306328232\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133064584346\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133068738778\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133076507654\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133076918024\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133083301591\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133086247854\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133088359667\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133090634929\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133096423215\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133098343212\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133100524056\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133109547917\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133110765808\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133113271835\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133113426328\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor 0.133116743692\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133118765514\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133125296326\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133126963294\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133127958398\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133128494094\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133128564625\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133128734196\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13312964276\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13313012155\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133130497806\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13313184928\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133135830663\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133136356967\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133136505591\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133137047436\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133141894896\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133142170235\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.133143678772\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133145076338\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13314700003\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133147979167\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133150170446\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13315371945\n",
"Lasso+LinearRegression+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133155597246\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133156507518\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133157842859\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13316037118\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133165340409\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13318273643\n",
"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133185622098\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133186651815\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133189748519\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133194138789\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133196754808\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133199074856\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133201822115\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133212082799\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133215621911\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.133216953909\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133220746213\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133221576837\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.133224388338\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133224939614\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133231633574\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133231897832\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133233094565\n",
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"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133236953183\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13323982088\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133245416483\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13324551824\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133249550788\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133250728173\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133256629272\n",
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"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133270070646\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133271659675\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133274456325\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133284336199\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133287947026\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13329332827\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.133299004847\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.133301784017\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.133302608672\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133307315217\n",
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"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13333162646\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133334933652\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133336853085\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133337288569\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133337397955\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133342452294\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133346200149\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133350277303\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133360307726\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13336094878\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133361560069\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.133363963232\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133367872094\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133369711442\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133369931836\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133375955971\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133377533558\n",
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"Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133381880733\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133383763116\n",
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"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133388709006\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133390104076\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133392877174\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133393974923\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133395536599\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133395666703\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133395892424\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.13339691879\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13339764306\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133398305167\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133399079561\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133399450488\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133401666852\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.133405257407\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133406240378\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133406685803\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133409234586\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133410765209\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133411420551\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133412279433\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133413583476\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133418763197\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133419244691\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133419929455\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.133420337596\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.13342666753\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.133428980115\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133430747145\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133432769592\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133434012954\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133439107336\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.133439467442\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.133443401754\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13344780973\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133447930896\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133453535338\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133455947133\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133459244223\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133460666162\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133465951893\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13346902151\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13347279977\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133477528889\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+XGBRegressor 0.133477841182\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.133480138602\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133482916113\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133483804355\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133486479747\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133490541251\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133490627555\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133491371325\n",
"Lasso+LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133494927431\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133496729858\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133497722221\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133499862778\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133501509948\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133502179814\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133509360705\n",
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"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133688765834\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.13368900833\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133692198576\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133695940276\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.133696768336\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133698171025\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133710336752\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133716213956\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133721796869\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133724776286\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133740678553\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+RandomForestRegressor+XGBRegressor 0.133741173573\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133747136059\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133751345161\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.133754831568\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+XGBRegressor 0.133755199264\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133759795645\n",
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"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134441358929\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134443741319\n",
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"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134461675518\n",
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"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13446901973\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.134469701302\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134470216632\n",
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"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134473340881\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.134476591456\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134483011827\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134487976595\n",
"LinearRegression+Ridge+ElasticNet+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134490276963\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134493454983\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134495475172\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134496042624\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134502990305\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134504715607\n",
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"Lasso+LinearRegression+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134510831567\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134510934653\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13451167967\n",
"Lasso+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134512540457\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134513307569\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13451512304\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134518048347\n",
"Lasso+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134519718348\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134521169302\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134526182535\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134526688356\n",
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"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134532751987\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134533406595\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134537471454\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.134537591654\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134540112906\n",
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"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134544383428\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+XGBRegressor 0.134917804458\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.134920237175\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.134920805874\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134921048651\n",
"Lasso+LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134921162446\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.134924826495\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.134926500878\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.134927916023\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor 0.134938022571\n",
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"Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135496995312\n",
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"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135499725412\n",
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"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135500536417\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135500808022\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13550492606\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135506152894\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135507129661\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135508299446\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135510405836\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1355121618\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135512853139\n",
"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135517044915\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.13551810874\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135520753792\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.135520964173\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135522566216\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135524479545\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135525903143\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135526490252\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135526537378\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.135529926432\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13553038325\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135532705063\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135534742193\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135535923717\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135538954559\n",
"Lasso+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135539500896\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13554036791\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135541181294\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135543345667\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135543992218\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135544509484\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.135545463256\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135545594886\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135547421714\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135548836762\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135550669796\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.135551146813\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor 0.135553148545\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135555158014\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13555550197\n",
"Lasso+LinearRegression+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135555588404\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135556373071\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135557024238\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135557999325\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135558644439\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135558670203\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135559843812\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.135561904494\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.135561939842\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135562009812\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135562984935\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13556437202\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135565943603\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135566355971\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135566585631\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.1355670158\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135567058317\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135570321187\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135570625871\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135572969649\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135574287452\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135694977034\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.135705859349\n",
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"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135711557618\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135711697713\n",
"Lasso+LinearRegression+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135715672023\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13571755617\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135717589694\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135721378731\n",
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"Lasso+LinearRegression+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135725423467\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.135727959228\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.135730429514\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135737115659\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135740012641\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135741373268\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13574145807\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135742673139\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135743052079\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.135743338756\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.1357445829\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.135746185298\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135749767003\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135749941216\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.135750429602\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135755898455\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor 0.135756736937\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135765835187\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136538317345\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136542011064\n",
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"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136662956861\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13666502934\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136666540863\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136666826626\n",
"Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136669247224\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136669778747\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136671134036\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136673535404\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136674039741\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136674689856\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136675567079\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136676804331\n",
"LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136676948653\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor 0.136677448621\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136682194836\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136682833986\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136682942644\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136683619516\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136685531816\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13668835097\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13668915662\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136690188744\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136690401225\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136691326591\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136692004585\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136694786837\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13669633866\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136698344937\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136702734761\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136703853705\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136705113696\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136708204953\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136708795796\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136709169969\n",
"LinearRegression+Ridge+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136710512542\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136711259984\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13671138991\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136711542434\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136711748148\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136712705086\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136714422527\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136715427045\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136715717697\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136716045894\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136718959256\n",
"LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136721499734\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136724902724\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136726426464\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136727303995\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136727539839\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+RandomForestRegressor 0.136729172101\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136732647489\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136733072804\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136733866703\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136734517721\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136735094858\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+XGBRegressor 0.136735456349\n",
"Lasso+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136736420806\n",
"Lasso+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136737672003\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136738770777\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136739911513\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136742238336\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136745517818\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136747809675\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136748009126\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136749215737\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136750172517\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136760061702\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136761004473\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136764388114\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136765753323\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136769155709\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136772943113\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136773921399\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136774266445\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136774475853\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.1367746108\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136775870034\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13677794938\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136778486928\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136778598131\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor 0.136779323664\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136780383285\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136780703131\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136780942284\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136787916916\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136790383136\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136790956662\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.136792303951\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136793213629\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136793454862\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136796003375\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136796298418\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13679652931\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13679664338\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136798407035\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136801213872\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136802285259\n",
"Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136804560262\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.136807014882\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136807856912\n",
"LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136809272921\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.136810114939\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136810156268\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136810221525\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136810527368\n",
"Lasso+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13681072727\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.136814407753\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136815609117\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136815712869\n",
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"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.13682277638\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136823841833\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13682582488\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136826980966\n",
"Lasso+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136827227311\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136827881098\n",
"Lasso+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136829950035\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136830531111\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136831202283\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136835483808\n",
"Lasso+LinearRegression+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136835736938\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136837113968\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136838284956\n",
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"Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136843544003\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.1368453221\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1368457983\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136846591943\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136847024237\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136863843361\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136869617118\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136874070943\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136874478535\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13687635668\n",
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"Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136879847905\n",
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"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136880575177\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136884010392\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136884123553\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136885884665\n",
"Lasso+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136886253842\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136887158505\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136890420722\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136896167858\n",
"Lasso+LinearRegression+Ridge+ElasticNet+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136897130102\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136897828116\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136899100684\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136899754411\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136900413699\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136902892925\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13690613178\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136907293052\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136907692733\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+RandomForestRegressor 0.136909940436\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.136910260352\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136910832159\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136910956179\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor 0.136911335403\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136912800386\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136912805244\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136915991222\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136916128929\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136921762551\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.136922403314\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.136922699456\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136925332654\n",
"LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136927183012\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136927418377\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136927732888\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136929850682\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.136933274392\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136935674776\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136935771992\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136940799006\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.136942607878\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136943763759\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136944134711\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136945430908\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136948923005\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136951145676\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13695178977\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136957660053\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.136958333638\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136958759297\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136958847359\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136961463813\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13696152319\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136962274954\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136963425444\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136963488668\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136963934361\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136964598956\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136966240418\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136966677015\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136970041728\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.136971059633\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.136972126336\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136977788667\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.136978029532\n",
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"LinearRegression+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136983665163\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136983837745\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136984479258\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.136985785404\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.136985996126\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136987242736\n",
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"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136993824682\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136994159237\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.136995902555\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.136997338327\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136998455421\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.136998933905\n",
"LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13700078862\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137001348152\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13702757815\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137030068686\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137033092466\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137040565525\n",
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"LinearRegression+Ridge+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137043119723\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137044565703\n",
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"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.137052829623\n",
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"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137060412234\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137060433846\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor 0.137063888289\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137067770116\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137067931738\n",
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"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137084418797\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137085033809\n",
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"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13711334734\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137115113295\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137117615584\n",
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"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137123245069\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137125634243\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.137126125641\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137127261247\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137128284236\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137129291207\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137130714602\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.137132144339\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137133853476\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.13713433162\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137135880457\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137142941341\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137142969041\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137143918542\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137144633331\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137144773896\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1371453107\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137145880959\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137148364153\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137150417906\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137151107655\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137152138841\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.137152424404\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137152649499\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137153296152\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137154024717\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137154086383\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137154489691\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.137156941568\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137158257934\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137161666149\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137165759665\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137166633046\n",
"Lasso+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13716758357\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137169364376\n",
"Lasso+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13717064389\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137174657781\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137177552881\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137182008276\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137182169456\n",
"LinearRegression+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137183809675\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137188685412\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137189408998\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.1371895998\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137189895602\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.1371901509\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137190190058\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137193388252\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137195296778\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor 0.137195962771\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1372003675\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137201010481\n",
"Lasso+Ridge+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137201150934\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137208655661\n",
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"Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137214380041\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137215605989\n",
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"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137234491188\n",
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"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137235845604\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137236071971\n",
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"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137534574663\n",
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"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137535947124\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137557597488\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137558149162\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137561017484\n",
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"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137567579322\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137568449886\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137570317837\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137573621524\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+XGBRegressor 0.137575803904\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13757831563\n",
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"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137592961075\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.137771779935\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137777971522\n",
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"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137786848674\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.137786887759\n",
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"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137790067557\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137792587213\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137792690556\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137792834673\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.137794257126\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137794587737\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137800700385\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137801910653\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.137803346073\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.137805173136\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor 0.137805316405\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13780616773\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137808114246\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.137811489251\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.137815513812\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137815608888\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137820108936\n",
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"TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137821787345\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.137822900446\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137828020589\n",
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"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+XGBRegressor 0.13783237186\n",
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"LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137834701886\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.137834953973\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137836538798\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137838863865\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137839841778\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137841142772\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137846017164\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137846803652\n",
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"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137852070523\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.137853506147\n",
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"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137858286593\n",
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"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137864917343\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137868515594\n",
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"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137890520896\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.137894838292\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.137895922467\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138034069044\n",
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"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13804515456\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138046201988\n",
"ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138046808081\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138047243455\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138050690778\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138051191935\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13805149298\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138056692542\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138059239076\n",
"Lasso+LinearRegression+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138059795544\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138063703132\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138064263309\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138065349886\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138066142111\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138069109287\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138073939781\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138078717615\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138078905544\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138079727951\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138082671649\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138083349475\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138087441357\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138087759597\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138091776821\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138092362777\n",
"Lasso+LinearRegression+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13809400397\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138095255616\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+RandomForestRegressor 0.138095393024\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138095637652\n",
"Lasso+LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138096354704\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138096629704\n",
"LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138099903805\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138100131754\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.138105873358\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138109932339\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138110177554\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13811389991\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138117601584\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138117772508\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138121010894\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138121172474\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138122668459\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13813153713\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138138873576\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138142775708\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.13814466437\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138145716304\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor 0.138146707511\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138155505333\n",
"LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138156747733\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.1381578207\n",
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"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13816223681\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138164518336\n",
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"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138219523264\n",
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"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138525550565\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138526553024\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138527308735\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138528004672\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138530086282\n",
"LinearRegression+Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138530349935\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138531355486\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138531480297\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138532747399\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138533704534\n",
"Ridge+ElasticNet+TheilSenRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138540118731\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138541862109\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138544421252\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138547094294\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138551767273\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138551996298\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138552460045\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138552797113\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138554247847\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138554580623\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138557232323\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138558623269\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138563150517\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138566884397\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138566968269\n",
"Lasso+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138569241407\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13857038356\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138573691765\n",
"Lasso+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138578044366\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138579196302\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138580605437\n",
"LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138583011877\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138583222896\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13858814633\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138589918053\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138595249186\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138601205922\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138602365228\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138605249021\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138605705974\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138606091151\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138608894913\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138613621521\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138614835663\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138615840733\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138616226437\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138616834871\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13862088249\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138621418903\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138622048229\n",
"Lasso+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138623102532\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138625928153\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138625972572\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138626101829\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13862669596\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138628444368\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138629203844\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138630779305\n",
"Lasso+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13863356112\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138636076758\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138639077451\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138639990509\n",
"Lasso+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.138642422936\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138644049021\n",
"Ridge+ElasticNet+RANSACRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138645844871\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.138648002614\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138648720626\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.13864953365\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13865018698\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138658114642\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+XGBRegressor 0.138663255038\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138663772752\n",
"Lasso+LinearRegression+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138664774995\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138667305604\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138670997503\n",
"Lasso+LinearRegression+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138671982004\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.13867584562\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138677711626\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138681813537\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.13868288168\n",
"Lasso+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138683118295\n",
"Lasso+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138687127677\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13868762381\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138687793436\n",
"Lasso+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13869088946\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138691615191\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13869395545\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138695517982\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138700959612\n",
"LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.138701516774\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138701620962\n",
"Lasso+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13870371548\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138703853061\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13870413358\n",
"Lasso+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138705080599\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138707894373\n",
"LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138708905757\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138709702237\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138709866665\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138710326566\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138711586984\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138715601516\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138720817477\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138727450785\n",
"Lasso+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138727570373\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138730541979\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138730890606\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138731768433\n",
"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138732666512\n",
"LinearRegression+ElasticNet+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138732924148\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138733106425\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.138742462124\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138743488974\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.138745660119\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138745983445\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138749493401\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138750427383\n",
"Lasso+Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138751166077\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.138751464369\n",
"Lasso+Ridge+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138752911765\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.138754458641\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13875568817\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138756406519\n",
"Lasso+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138760427078\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.138761093527\n",
"LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.138763267886\n",
"LinearRegression+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138765873113\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.138767175893\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138767354624\n",
"LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138768233058\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138769054529\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138769989778\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor+XGBRegressor 0.138770178875\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+RandomForestRegressor 0.138778003716\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor 0.138778255949\n",
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"LinearRegression+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138780358359\n",
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"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138900296653\n",
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"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.139442475044\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139443497041\n",
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"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139453199021\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139491395631\n",
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"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+RandomForestRegressor 0.139497453572\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139500215892\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139503244785\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139503308769\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139506789256\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139507805246\n",
"Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139509204161\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13951013101\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.1395130738\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139521432533\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13952427329\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor 0.139525740237\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139528624\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+XGBRegressor 0.139542182912\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139549344973\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139550446342\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139555978181\n",
"Lasso+LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139557294333\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139558812138\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.13955937776\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139559457273\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139560211966\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139568412805\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.139571351192\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13957232057\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139574210039\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139575195593\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139576339383\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.139578720728\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139583266532\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139585444828\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139586925114\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139590021984\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139592512729\n",
"Lasso+LinearRegression+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139592752644\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139601746928\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139606932625\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13960961998\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139611650892\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139614170627\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139618095628\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139618557332\n",
"Lasso+Ridge+ElasticNet+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139619499841\n",
"Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139619966858\n",
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"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139624406056\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139637675869\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139638619554\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139639399281\n",
"Ridge+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139642866387\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.139643030671\n",
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"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139673622735\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139687837895\n",
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"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139695031386\n",
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"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139703190195\n",
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"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.139716320726\n",
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"Lasso+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139724050315\n",
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"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139731461844\n",
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"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139737501268\n",
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"Lasso+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139815842778\n",
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"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139852018592\n",
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"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139856044341\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139873838354\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139883143992\n",
"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.13988361014\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139884087203\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139884460795\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139890972551\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139891712917\n",
"Lasso+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139894776746\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139896259455\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139907447829\n",
"Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139908816989\n",
"Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139912812643\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139915386581\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139919470998\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139920117866\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139925236985\n",
"Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139925864506\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139931133452\n",
"LinearRegression+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139932030695\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139943987375\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139950199917\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor 0.139953972382\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139954075792\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139954970331\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139959044463\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139964371488\n",
"Ridge+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139966381799\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor 0.139967446058\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139967485745\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.139969731564\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.139971873097\n",
"Ridge+ElasticNet+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139977965055\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139978661631\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139979734743\n",
"Lasso+LinearRegression+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139980230649\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.139980427827\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.139981343569\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.139988143329\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.139991093539\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.139992163499\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.139996844\n",
"ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.139999266813\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140001615291\n",
"ElasticNet+TheilSenRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140003558316\n",
"Lasso+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.14001148983\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140018312914\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140018510904\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140027602686\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140035489764\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140035679961\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140035810573\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140039450931\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140040081138\n",
"LinearRegression+Ridge+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140048693926\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140055243902\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.1400590543\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.14006382281\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140065006231\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140068077832\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14006878792\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140070924237\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140071311734\n",
"LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14007283161\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140073554446\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140074232143\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140075229822\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140075639615\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140080357519\n",
"Lasso+LinearRegression+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140085369065\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140086968679\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140088347582\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140101802511\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140104924968\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.14010542111\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140110738535\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140118812861\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140123392663\n",
"Lasso+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140129361421\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140129771145\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140129944407\n",
"Ridge+ElasticNet+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140134586322\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140136233541\n",
"LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140137117415\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140138295313\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.140147839994\n",
"Lasso+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14015831432\n",
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"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.140175041094\n",
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"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor 0.140180281423\n",
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"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.140210647589\n",
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"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140215342755\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor 0.14022473482\n",
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"Lasso+LinearRegression+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.140227007269\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.140239827528\n",
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"Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140242889048\n",
"TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140254408075\n",
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"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140261344066\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140265010592\n",
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"LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140265984677\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor 0.14027790132\n",
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"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140281310912\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140289105533\n",
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"ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145052438767\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145134296097\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.145277297891\n",
"ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145358023191\n",
"HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.145812471188\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.145892615381\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146105715468\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.146601566644\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.147339077127\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.147348156777\n",
"\n",
"Model Amount : 10\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12941602922\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129636396187\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12968893155\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129733299025\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129762839682\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129908427333\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129930274214\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129964881775\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130017878967\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130048957057\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130061589984\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130125370073\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130158601124\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130236063602\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130239006929\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130250452093\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130348722389\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130363461439\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130399111081\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130422246831\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130427542075\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130450226935\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130459245472\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130473591855\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130515473112\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130526938041\n",
"Lasso+LinearRegression+Ridge+ElasticNet+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130531751001\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130644048535\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13067211319\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.130688476219\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130695314853\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130698696267\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130701204602\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13073023134\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130732215257\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130748399893\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13090143425\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130923692203\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130943031414\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130947895252\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13101914189\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131025941337\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131039453264\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131081312527\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131085901496\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131106798425\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131117690295\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131131218633\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131136623552\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131140499515\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131147092714\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131155473241\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131176569723\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131193628586\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131198673901\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131221066329\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13122223795\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131240923916\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131252057646\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131255065783\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131297623553\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131303002602\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131304889478\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131306421157\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131318652955\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131326093607\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131334290839\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131334567979\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131337809411\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131381990565\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13139322652\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131406628532\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131419724447\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131427466774\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131433277745\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131450210124\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131463803472\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131490688601\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131491247947\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131510200395\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131514420424\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131529191933\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131529998611\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131535145771\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131535328105\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131547990134\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131555564364\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131556932255\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131564198664\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131567322739\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131568451873\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131577365411\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131581953316\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+RandomForestRegressor+XGBRegressor 0.131588950174\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131594180689\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131599677604\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131606818353\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131612248269\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131621615908\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131652291901\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131653641135\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131670767227\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131680354472\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131684412988\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131692712561\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131700942328\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131703373814\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13170353092\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131710918412\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131711005914\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131711617825\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.131715671996\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131717633326\n",
"Lasso+LinearRegression+Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131719695764\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.131730366852\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131734465539\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131759192708\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131765343599\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131767323891\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131775099759\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131784184752\n",
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"Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140688530667\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140697161527\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140698387594\n",
"Lasso+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140714334297\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140718003596\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140766073146\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.140769909115\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140774862683\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140797121221\n",
"Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140801968056\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.140825351493\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.140832505892\n",
"LinearRegression+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140845979611\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140919868771\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140923300829\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.1409431292\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140956577519\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140958680176\n",
"Lasso+LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140962816852\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140963605294\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.1410296871\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor 0.14104892265\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141058365499\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141078947931\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.141090520177\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141090602903\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141096892619\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141107298066\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141122410654\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.141132917504\n",
"Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141156690387\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.141246143275\n",
"TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.141268633123\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141343269657\n",
"Ridge+ElasticNet+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141367869827\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141385440408\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141412024588\n",
"Ridge+ElasticNet+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141500171718\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141532121292\n",
"Ridge+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141551081263\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141555942855\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141601344859\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.14170075468\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141797019221\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141802673334\n",
"TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141825827774\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141837898441\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.141884863828\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.141937826173\n",
"ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141939554866\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142071917774\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142166514103\n",
"Lasso+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142184955629\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142260138196\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.142318971343\n",
"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.142340245042\n",
"RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.142360429366\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142596653002\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142917096844\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.142933259989\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143151082491\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143159001962\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143326661023\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.14333004452\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.143731322416\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.143765515793\n",
"ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.144639523239\n",
"\n",
"Model Amount : 11\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129702489557\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12997551769\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130261452324\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130319957598\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13034623437\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13038053712\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130382456792\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.130520292558\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130641846915\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130836255235\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130878963279\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130911837447\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130950280719\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131026691381\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131190912617\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131212704596\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131267703273\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13127012557\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131296129959\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13131760649\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131348566852\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131366253529\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13137803007\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13141354121\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13141404271\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131463530207\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131475963589\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.131477982091\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131494910615\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131502844731\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131518965144\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131522029798\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131546770846\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.131584433196\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131624805203\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131666871389\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131671608978\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13171221914\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131739795696\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131782103811\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131783396778\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13179337079\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131878694104\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131889020891\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131890007313\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131903307245\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131907113296\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131926264806\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131927857193\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131928525822\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131933275736\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131936411953\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131941902453\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131949823712\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131954098583\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131956134695\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131968598753\n",
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"LinearRegression+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.140323228414\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140389268423\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140389435362\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.140390020276\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140659370836\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.140674524811\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140689199267\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.140693143513\n",
"Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141104401843\n",
"ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141246056732\n",
"ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.141610632687\n",
"\n",
"Model Amount : 12\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130193380744\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131097396162\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131117703804\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131177549783\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131424803552\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131540410956\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131585612615\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131598510493\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131598541981\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131736853875\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.131938608406\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131961414091\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131974306489\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131984140653\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132061858403\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132065778049\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132067288223\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132100975446\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132106779255\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132107909132\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132144393395\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132163363295\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132167634683\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132174771911\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13220778573\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132220858057\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132232266224\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132350177828\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132451986951\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132471355058\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132510924971\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132527536235\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13258015912\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132581294731\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132603616367\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132618546887\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132632020752\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132655031618\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132666644381\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132705422301\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13273777178\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132748894695\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132808157616\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.132810002069\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.132812130389\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132819239338\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132820033225\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132851173917\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132858185304\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.132875440513\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.13295139556\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13296527286\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132973269327\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133004496286\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133009758395\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.133025385346\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133054726582\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13306889723\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133077825755\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.133092116804\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133138397634\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133144355366\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133149494295\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133155136899\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133159421273\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133186088758\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133230558807\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133253839465\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133254281601\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133265654178\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133268056842\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133273507574\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+XGBRegressor 0.133273768757\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133282159609\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13328704247\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133289784661\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133292299939\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133298309627\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133303423971\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133306887636\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133327757403\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133342875066\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133408998283\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133438220804\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133448569328\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133465338078\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133475630315\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133496408964\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133503806289\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13356102549\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133563098905\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133569771775\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133572257427\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133579386887\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133584706118\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133614476758\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13361784778\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133621208426\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133628525268\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133636684317\n",
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"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137725446929\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.137759426105\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137809036417\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137812312562\n",
"Lasso+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137818205418\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137822558705\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137848103475\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137908397167\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137934621573\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.137968304991\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.137996068654\n",
"LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13799818074\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138012658736\n",
"Lasso+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138022106171\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138050109352\n",
"LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138056995574\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138105167717\n",
"Lasso+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138214029518\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.138234034747\n",
"LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138342452168\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.138509472229\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.138527787703\n",
"Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13871382797\n",
"Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138971504659\n",
"ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.138974702111\n",
"\n",
"Model Amount : 13\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131609901609\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131670609041\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131784225899\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132532657022\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132549621322\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132577342995\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132629708936\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132663584365\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132727464185\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132770889254\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.132930442099\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132952803958\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133122022177\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133158182673\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133181989475\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133182971007\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.133267168383\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133396873194\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133400170127\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13343388145\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133472304501\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133535218278\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133547289262\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133599123424\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133692093618\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133757778896\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133760518781\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133778388575\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133834711485\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13383769953\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133916555191\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133925592066\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+XGBRegressor 0.133981314954\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133996724035\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134007572231\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134029913996\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134030847028\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134064092843\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13408567868\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134104889173\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134123013944\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134180327986\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134207873797\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134268540006\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134277531339\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.134302625192\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.134339786386\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134363976655\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134440532945\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134495221371\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134497289028\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134569368771\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134598241817\n",
"Lasso+LinearRegression+Ridge+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13476875638\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134785045278\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13481650035\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.13483166423\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134839715335\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134848686299\n",
"Lasso+LinearRegression+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134848971186\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134855399278\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134884028204\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134885410562\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134887781986\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134922975665\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135002247468\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+RandomForestRegressor+XGBRegressor 0.135061987974\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13509486715\n",
"Lasso+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135149110024\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135196483059\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135233692653\n",
"LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135255513957\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135257215623\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135279046154\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135311373291\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135325034018\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135337163676\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13533944101\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.135390496481\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135408213767\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+XGBRegressor 0.135490057998\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor 0.13553414473\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135542236337\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13556195123\n",
"Lasso+LinearRegression+Ridge+ElasticNet+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135711723019\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135831474658\n",
"Lasso+LinearRegression+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135975066796\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.13598996842\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136013733726\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136062039657\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136089990252\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13610516966\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.13612478312\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13630949713\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13640080747\n",
"Lasso+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136427992131\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136463815399\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136489587773\n",
"Lasso+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13649925935\n",
"LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136551708233\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.136574163577\n",
"LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.136598586988\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.136601332209\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor 0.136715094522\n",
"Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.137081400282\n",
"\n",
"Model Amount : 14\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132828091395\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132850505043\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133168768482\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13375435832\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.133930537605\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134068840053\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+XGBRegressor 0.134138819305\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.13418412404\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134739257293\n",
"Lasso+LinearRegression+Ridge+ElasticNet+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134821372402\n",
"Lasso+LinearRegression+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134836383376\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+RandomForestRegressor+XGBRegressor 0.135187496405\n",
"Lasso+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135218981737\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor 0.135221327055\n",
"LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.135315522158\n",
"\n",
"Model Amount : 15\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134028947124\n",
"\n"
]
}
],
"source": [
"final_results = []\n",
"for comb_length in range(1,len(regs)+1):\n",
" print('Model Amount :',comb_length)\n",
" results = []\n",
" for comb in itertools.combinations(preds,comb_length):\n",
" pred_sum = 0\n",
" model_name = []\n",
" for reg_name,pred in comb:\n",
" pred_sum += pred\n",
" model_name.append(reg_name)\n",
" pred_sum /= comb_length\n",
" model_name = '+'.join(model_name)\n",
" score = np.sqrt(mean_squared_error(np.log(y_test),np.log(pred_sum)))\n",
" results.append([model_name,score])\n",
" results = sorted(results,key=lambda x:x[1])\n",
" for model_name,score in results:\n",
" print(model_name,score)\n",
" print()\n",
" final_results.append(results[0])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lasso+GradientBoostingRegressor+XGBRegressor 0.124979582373\n",
"Lasso+LinearRegression+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.126491410988\n",
"Lasso+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.126842572869\n",
"Lasso+XGBRegressor 0.127194837142\n",
"Lasso+LinearRegression+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127371402793\n",
"Lasso+LinearRegression+Ridge+SVR+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.127926929743\n",
"Lasso+LinearRegression+Ridge+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.128744815807\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12914274306\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.12941602922\n",
"Lasso+LinearRegression+Ridge+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.129702489557\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.130193380744\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.131609901609\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.132828091395\n",
"GradientBoostingRegressor 0.133400775498\n",
"Lasso+LinearRegression+Ridge+ElasticNet+TheilSenRegressor+RANSACRegressor+HuberRegressor+SVR+DecisionTreeRegressor+ExtraTreeRegressor+AdaBoostRegressor+ExtraTreesRegressor+GradientBoostingRegressor+RandomForestRegressor+XGBRegressor 0.134028947124\n"
]
}
],
"source": [
"final_results = sorted(final_results,key=lambda x:x[1])\n",
"for model_name,score in final_results:\n",
" print(model_name,score)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"最终输出"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(0, 'Lasso'),\n",
" (1, 'LinearRegression'),\n",
" (2, 'Ridge'),\n",
" (3, 'ElasticNet'),\n",
" (4, 'TheilSenRegressor'),\n",
" (5, 'RANSACRegressor'),\n",
" (6, 'HuberRegressor'),\n",
" (7, 'SVR'),\n",
" (8, 'DecisionTreeRegressor'),\n",
" (9, 'ExtraTreeRegressor'),\n",
" (10, 'AdaBoostRegressor'),\n",
" (11, 'ExtraTreesRegressor'),\n",
" (12, 'GradientBoostingRegressor'),\n",
" (13, 'RandomForestRegressor'),\n",
" (14, 'XGBRegressor')]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[b for b in zip(itertools.count(),[a[0] for a in regs])]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pred = np.mean(list(map(lambda x:regs[x][1].predict(test),[0,12,14])),axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sub = pd.DataFrame({'Id':test['Id'],'SalePrice':pred})\n",
"sub.to_csv('submission_Universe_fillNaN.csv',index=None)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: docs/Kaggle/competitions/getting-started/titanic/README.md
================================================
# **泰坦尼克号**
## 比赛说明
* [**泰坦尼克号**](https://www.kaggle.com/c/titanic) 的沉没是历史上最臭名昭着的沉船之一。1912年4月15日，在首航期间，泰坦尼克号撞上一座冰山后沉没，2224名乘客和机组人员中有1502人遇难。这一耸人听闻的悲剧震撼了国际社会，并导致了更好的船舶安全条例。
* 沉船导致生命损失的原因之一是乘客和船员没有足够的救生艇。虽然幸存下来的运气有一些因素，但有些人比其他人更有可能生存，比如妇女，儿童和上层阶级。
* 在这个挑战中，我们要求你完成对什么样的人可能生存的分析。特别是，我们要求你运用机器学习的工具来预测哪些乘客幸存下来的悲剧。
## 参赛成员
* 开源组织: [ApacheCN ~ apachecn.org](http://www.apachecn.org/)
* **菜鸡互啄(团队)**: [@那伊抹微笑](https://github.com/wangyangting), [@风风](https://github.com/fengfengtzp), [@李铭哲](https://github.com/limingzhe), [@刘海飞](https://github.com/WindZQ), [@DL - 小王子](https://github.com/VPrincekin), [@成飘飘](https://github.com/chengpiaopiao)
* **瑶瑶亲卫队(团队)**: [@瑶妹](https://github.com/chenyyx) 张俊皓 kngines [@谈笑风生](https://github.com/zhu1040028623) Yukine ~守护 程威 屋檐听雨 Gladiator 吃着狗粮的电酱PRPR
## 比赛分析
* 分类问题：预测的是`生`与`死`的问题
* 常用算法： `K紧邻(knn)`、`逻辑回归(LogisticRegression)`、`随机森林(RandomForest)`、`支持向量机(SVM)`、`xgboost`、`GBDT`
```
步骤:
一. 数据分析
1. 下载并加载数据
2. 总体预览:了解每列数据的含义,数据的格式等
3. 数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备
二. 特征工程
1.根据业务,常识,以及第二步的数据分析构造特征工程.
2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)
三. 模型选择
1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.
2.比较各个模型的分数,然后取效果较好的模型作为基础模型.
四. 模型融合
1. Bagging: 同一模型的投票选举
2. Boosting: 同一模型的再学习
3. Voting: 不同模型的投票选举
4. Stacking: 分层预测 – K-1份数据预测1份模型拼接，得到 预测结果*算法数（作为特征） => 从而预测最终结果
5. Blending: 分层预测 – 将数据分成2部分（A部分训练B部分得到预测结果），得到 预测结果*算法数（作为特征） => 从而预测最终结果
五. 参数优化
1.可以通过添加或者修改特征,提高模型的上限.
2.通过修改模型的参数,是模型逼近上限
```
## 一. 数据分析
### 数据下载和观察
* 数据集下载地址：
> 特征说明
| 特征 | 描述 | 值|
| - | - | - |
| PassengerId | 乘客ID | |
| Survived | 生存 | 0 = No, 1 = Yes |
| Pclass | 票类别-社会地位 | 1 = 1st, 2 = 2nd, 3 = 3rd |
| Name | 姓名 | |
| Sex | 性别 | |
| Age | 年龄 | |
| SibSp | 兄弟姐妹/配偶 | |
| Parch | 父母/孩子的数量 | |
| Ticket | 票号 | |
| Fare | 乘客票价 | |
| Cabin | 客舱号码 | |
| Embarked | 登船港口 | C=Cherbourg, Q=Queenstown, S=Southampton |
### 特征详情
```python
# 导入相关数据包
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
```
```python
root_path = '/opt/data/datasets/getting-started/titanic/input'
train = pd.read_csv('%s/%s' % (root_path, 'train.csv'))
test = pd.read_csv('%s/%s' % (root_path, 'test.csv'))
```
```python
train.head(5)
```
```py
>>> # 返回每列列名,该列非nan值个数,以及该列类型
>>> train.info()
>>> # test.info()
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId 891 non-null int64
Survived 891 non-null int64
Pclass 891 non-null int64
Name 891 non-null object
Sex 891 non-null object
Age 714 non-null float64
SibSp 891 non-null int64
Parch 891 non-null int64
Ticket 891 non-null object
Fare 891 non-null float64
Cabin 204 non-null object
Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB
```
```py
>>> # 返回数值型变量的统计量
>>> # train.describe(percentiles=[0.00, 0.25, 0.5, 0.75, 1.00])
>>> print(titanic.describe())
PassengerId Survived Pclass Age SibSp Parch Fare
count 891.000000 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000
mean 446.000000 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208
std 257.353842 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429
min 1.000000 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000
25% 223.500000 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400
50% 446.000000 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200
75% 668.500000 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000
max 891.000000 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200
```
## 二. 特征工程
### 特征处理
目的:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备
```python
# 存活人数
train['Survived'].value_counts()
0 549
1 342
Name: Survived, dtype: int64
# 对缺失值处理（Age 中位数不错）
titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())
titanic["Fare"] = titanic["Fare"].fillna(titanic["Fare"].median())
# 对文本特征进行处理（性别， 登船港口）
print(titanic["Sex"].unique())
titanic.loc[titanic["Sex"]=="male", "Sex"] = 0
titanic.loc[titanic["Sex"]=="female", "Sex"] = 1
# 组合特征(特征组合相关性变差了)
# titanic["FamilySize"] = titanic["SibSp"] + titanic["Parch"]
# S的概率最大，当然我们也可以按照概率随机算，都可以
print(titanic["Embarked"].unique())
"""
titanic[["Embarked"]].groupby("Embarked").agg({"Embarked": "count"})
Embarked
Embarked
C 168
Q 77
S 644
"""
titanic["Embarked"] = titanic["Embarked"].fillna('S')
titanic.loc[titanic["Embarked"] == "S", "Embarked"] = 0
titanic.loc[titanic["Embarked"] == "C", "Embarked"] = 1
titanic.loc[titanic["Embarked"] == "Q", "Embarked"] = 2
def get_title(name):
# 名字的尊称
title_search = re.search(' ([A-Za-z]+)\.', name)
if title_search:
return title_search.group(1)
return ""
titles = titanic["Name"].apply(get_title)
# print(pandas.value_counts(titles))
# 对尊称建立mapping字典
# 在数据的Name项中包含了对该乘客的称呼，如Mr、Miss等，这些信息包含了乘客的年龄、性别、也有可能包含社会地位，如Dr、Lady、Major、Master等称呼。这一项不方便用图表展示，但是在特征工程中，我们会将其提取出来,然后放到模型中。
# 剩余因素还有船票价格、船舱号和船票号，这三个因素都可能会影响乘客在船中的位置从而影响逃生顺序，但是因为这三个因素与生存之间看不出明显规律，所以在后期模型融合时，将这些因素交给模型来决定其重要性。
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Dr": 5, "Rev": 6, "Major": 7, "Col": 7, "Mlle": 8, "Mme": 8, "Don": 9, "Dona": 9, "Lady": 10, "Countess": 10, "Jonkheer": 10, "Sir": 9, "Capt": 7, "Ms": 2}
for k, v in title_mapping.items():
titles[titles == k] = v
# print(pd.value_counts(titles))
# 添加一个新特征表示拥护尊称
titanic["Title"] = [int(i) for i in titles.values.tolist()]
# 添加一个新特征表示名字长度
titanic["NameLength"] = titanic["Name"].apply(lambda x: len(x))
# 相关性太差，删除
# titanic.drop(['PassengerId'], axis=1,inplace=True)
titanic.drop(['Cabin'], axis=1,inplace=True)
titanic.drop(['SibSp'], axis=1,inplace=True)
# titanic.drop(['Parch'],axis=1,inplace=True)
titanic.drop(['Ticket'], axis=1,inplace=True)
titanic.drop(['Name'], axis=1,inplace=True)
```
### 特征相关性
> 1)数值型数据协方差 corr()函数
来个总览,快速了解个数据的相关性
```python
# 相关性协方差表,corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关.
train_corr = train.corr()
train_corr
```
| 相关性 | Survived | Pclass | Sex | Age | Parch | Fare | Embarked | Title | NameLength |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| Survived | 1.000000 | -0.338481 | 0.543351 | -0.064910 | 0.081629 | 0.257307 | 0.106811 | 0.354072 | 0.332350 |
| Pclass | -0.338481 | 1.000000 | -0.131900 | -0.339898 | 0.018443 | -0.549500 | 0.045702 | -0.211552 | -0.220001 |
| Sex | 0.543351 | -0.131900 | 1.000000 | -0.081163 | 0.245489 | 0.182333 | 0.116569 | 0.419760 | 0.448759 |
| Age | -0.064910 | -0.339898 | -0.081163 | 1.000000 | -0.172482 | 0.096688 | -0.009165 | -0.037174 | 0.039702 |
| Parch | 0.081629 | 0.018443 | 0.245489 | -0.172482 | 1.000000 | 0.216225 | -0.078665 | 0.235164 | 0.252282 |
| Fare | 0.257307 | -0.549500 | 0.182333 | 0.096688 | 0.216225 | 1.000000 | 0.062142 | 0.122872 | 0.155832 |
| Embarked | 0.106811 | 0.045702 | 0.116569 | -0.009165 | -0.078665 | 0.062142 | 1.000000 | 0.055788 | -0.107749 |
| Title | 0.354072 | -0.211552 | 0.419760 | -0.037174 | 0.235164 | 0.122872 | 0.055788 | 1.000000 | 0.436099 |
| NameLength | 0.332350 | -0.220001 | 0.448759 | 0.039702 | 0.252282 | 0.155832 | -0.107749 | 0.436099 | 1.000000 |
```python
# 画出相关性热力图
a = plt.subplots(figsize=(15,9))#调整画布大小
a = sns.heatmap(train_corr, vmin=-1, vmax=1 , annot=True , square=True)#画热力图
```
### 特征标准化和降维
* 数据标准化
1. 线性模型需要用标准化的数据建模, 而树类模型不需要标准化的数据
2. 处理标准化的时候,注意将测试集的数据transform到test集上
```py
def do_FeatureEngineering(data, COMPONENT_NUM=0.9):
# scale values 对一化
scaler = preprocessing.StandardScaler()
s_data = scaler.fit_transform(data)
return s_data
# # 降维(不降维，准确率还上升了)
# '''
# 使用说明：https://www.cnblogs.com/pinard/p/6243025.html
# n_components>=1
# n_components=NUM 设置占特征数量比
# 0 < n_components < 1
# n_components=0.99 设置阈值总方差占比
# '''
# pca = PCA(n_components=COMPONENT_NUM, whiten=False)
# pca.fit(s_data) # Fit the model with X
# pca_data = pca.transform(s_data) # Fit the model with X and 在X上完成降维.
# # pca 方差大小、方差占比、特征数量
# # print("方差大小:\n", pca.explained_variance_, "方差占比:\n", pca.explained_variance_ratio_)
# print("特征数量: %s" % pca.n_components_)
# print("总方差占比: %s" % sum(pca.explained_variance_ratio_))
# return pca_data
```
## 三. 建立模型
### 模型发现
1. 可选单个模型模型有逻辑回归, 随机森林, svm, xgboost, gbdt等.
2. 也可以将多个模型组合起来,进行模型融合,比如voting,stacking等方法
3. 好的特征决定模型上限,好的模型和参数可以无线逼近上限.
4. 我测试了多种模型,模型结果最高的随机森林,最高有0.8.
### 构建模型
```py
# 0.8069524400247253 [0.79329609 0.81564246 0.8258427 0.80337079 0.79661017]
model = LogisticRegression(random_state=1)
# 0.8272091118939124 [0.82122905 0.80446927 0.84831461 0.82022472 0.84180791]
model = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2)
# 0.822670577600365 [0.82681564 0.82122905 0.83146067 0.80898876 0.82485876]
model = RandomForestClassifier(random_state=1, n_estimators=50, min_samples_split=8, min_samples_leaf=4)
# 0.8294499549079417 [0.82122905 0.80446927 0.86516854 0.82022472 0.83615819]
model = XGBClassifier(n_estimators=196, max_depth=4, learning_rate=0.03)
```
## 四. 模型融合
```py
print("模型融合")
"""
Bagging: 同一模型的投票选举
Boosting: 同一模型的再学习
Voting: 不同模型的投票选举
Stacking: 分层预测 – K-1份数据预测1份模型拼接，得到 预测结果*算法数（作为特征） => 从而预测最终结果
Blending: 分层预测 – 将数据分成2部分（A部分训练B部分得到预测结果），得到 预测结果*算法数（作为特征） => 从而预测最终结果
"""
# 1. Bagging 算法实现
# 0.8691726623564537 [0.86179183 0.82700922 0.8855615 0.87700535 0.89449541]
model = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2)
# 2. Boosting 算法实现
# 0.8488710896477386 [0.8198946 0.82285903 0.87780749 0.84906417 0.87473017]
model = AdaBoostClassifier(random_state=1, n_estimators=100, learning_rate=1)
# 3. Voting
# 0.8695399796790022 [0.87259552 0.8370224 0.87433155 0.86885027 0.89490016]
model = VotingClassifier(
estimators=[
('log_clf', LogisticRegression()),
('ab_clf', AdaBoostClassifier()),
('svm_clf', SVC(probability=True)),
('rf_clf', RandomForestClassifier()),
('gbdt_clf', GradientBoostingClassifier()),
('rb_clf', AdaBoostClassifier())
], voting='soft') # , voting='hard')
scores = cross_val_score(model, trainData, trainLabel, cv=5, scoring='roc_auc')
print(scores.mean(), "\n", scores)
# 4. Stacking
# 0.8713813265814722 [0.87747036 0.83886693 0.86590909 0.87085561 0.90380464]
clfs = [
AdaBoostClassifier(),
SVC(probability=True),
AdaBoostClassifier(),
LogisticRegression(C=0.1,max_iter=100),
XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),
RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),
GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)
]
kf = KFold(n_splits=5, shuffle=True, random_state=1)
# 创建零矩阵
dataset_stacking_train = np.zeros((trainData.shape[0], len(clfs)))
# dataset_stacking_label = np.zeros((trainLabel.shape[0], len(clfs)))
for j, clf in enumerate(clfs):
'''依次训练各个单模型'''
for i,(train, test) in enumerate(kf.split(trainLabel)):
'''使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。'''
# print("Fold", i)
X_train, y_train, X_test, y_test = trainData[train], trainLabel[train], trainData[test], trainLabel[test]
clf.fit(X_train, y_train)
y_submission = clf.predict_proba(X_test)[:, 1]
# j 表示每一次的算法，而 test是交叉验证得到的每一行（也就是每一个算法把测试机和都预测了一遍）
dataset_stacking_train[test, j] = y_submission
# 用建立第二层模型
model = LogisticRegression(C=0.1, max_iter=100)
model.fit(dataset_stacking_train, trainLabel)
scores = cross_val_score(model, dataset_stacking_train, trainLabel, cv=5, scoring='roc_auc')
print(scores.mean(), "\n", scores)
# 5. Blending
# 0.8838950287185581 [0.87584416 0.91064935 0.89714286 0.85294118 0.8828976 ]
clfs = [
AdaBoostClassifier(),
SVC(probability=True),
AdaBoostClassifier(),
LogisticRegression(C=0.1,max_iter=100),
XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),
RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),
GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)
]
X_d1, X_d2, y_d1, y_d2 = train_test_split(trainData, trainLabel, test_size=0.5, random_state=2017)
dataset_d1 = np.zeros((X_d2.shape[0], len(clfs)))
dataset_d2 = np.zeros((trainLabel.shape[0], len(clfs)))
for j, clf in enumerate(clfs):
#依次训练各个单模型
# 对于测试集，直接用这k个模型的预测值作为新的特征。
clf.fit(X_d1, y_d1)
dataset_d1[:, j] = clf.predict_proba(X_d2)[:, 1]
# 用建立第二层模型
model = LogisticRegression(C=0.1, max_iter=100)
model.fit(dataset_d1, y_d2)
scores = cross_val_score(model, dataset_d1, y_d2, cv=5, scoring='roc_auc')
print(scores.mean(), "\n", scores)
```
## 五. 参数优化
```py
from sklearn.model_selection import GridSearchCV
param_test = {
# 'n_estimators': np.arange(190, 240, 2),
# 'max_depth': np.arange(4, 7, 1),
# 'learning_rate': np.array([0.01, 0.03, 0.05, 0.08, 0.1, 0.15, 0.2]),
'n_estimators': np.array([196]),
'max_depth': np.array([4]),
'learning_rate': np.array([0.01, 0.02, 0.03, 0.04, 0.05]),
# 'min_child_weight': np.arange(1, 6, 2),
# 'C': (1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9)
}
# 0.8294499549079417 [0.82122905 0.80446927 0.86516854 0.82022472 0.83615819]
model = XGBClassifier()
grid_search = GridSearchCV(estimator=model, param_grid=param_test, scoring='roc_auc', cv=5)
grid_search.fit(trainData, trainLabel)
print("最优得分 >>>", grid_search.best_score_)
print("最优参数 >>>", grid_search.best_params_)
# 0.8685305085155506 [0.85770751 0.82002635 0.89632353 0.87018717 0.89840799]
model = XGBClassifier(n_estimators=196, max_depth=4, learning_rate=0.03)
scores = cross_val_score(model, trainData, trainLabel, cv=5, scoring='roc_auc')
print(scores.mean(), "\n", scores)
```
================================================
FILE: docs/Kaggle/competitions/getting-started/titanic/kaggle泰坦尼克之灾-风风组.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# kaggle泰坦尼克之灾"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"步骤:\n",
"一.导入数据包与数据集\n",
"\n",
"二.数据分析\n",
"1.总体预览:了解每列数据的含义,数据的格式等\n",
"2..数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\n",
"\n",
"三.特征工程\n",
"1.根据业务,常识,以及第二步的数据分析构造特征工程.\n",
"2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\n",
"\n",
"四.模型选择\n",
"1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\n",
"2.比较各个模型的分数,然后取效果较好的模型作为基础模型.\n",
"\n",
"五.修改特征和模型参数\n",
"1.可以通过添加或者修改特征,提高模型的上限.\n",
"2.通过修改模型的参数,是模型逼近上限"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 一.导入数据包与数据集"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# 导入相关数据包\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"root_path = '/opt/git/kaggle/datasets/getting-started/titanic/input'\n",
"\n",
"train = pd.read_csv('%s/%s' % (root_path, 'train.csv'))\n",
"test = pd.read_csv('%s/%s' % (root_path, 'test.csv'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 二.数据分析"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.总体预览\n",
"了解每列数据的含义,数据的格式等"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" PassengerId \n",
" Survived \n",
" Pclass \n",
" Name \n",
" Sex \n",
" Age \n",
" SibSp \n",
" Parch \n",
" Ticket \n",
" Fare \n",
" Cabin \n",
" Embarked \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 3 \n",
" Braund, Mr. Owen Harris \n",
" male \n",
" 22.0 \n",
" 1 \n",
" 0 \n",
" A/5 21171 \n",
" 7.2500 \n",
" NaN \n",
" S \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 1 \n",
" 1 \n",
" Cumings, Mrs. John Bradley (Florence Briggs Th... \n",
" female \n",
" 38.0 \n",
" 1 \n",
" 0 \n",
" PC 17599 \n",
" 71.2833 \n",
" C85 \n",
" C \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 1 \n",
" 3 \n",
" Heikkinen, Miss. Laina \n",
" female \n",
" 26.0 \n",
" 0 \n",
" 0 \n",
" STON/O2. 3101282 \n",
" 7.9250 \n",
" NaN \n",
" S \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 1 \n",
" 1 \n",
" Futrelle, Mrs. Jacques Heath (Lily May Peel) \n",
" female \n",
" 35.0 \n",
" 1 \n",
" 0 \n",
" 113803 \n",
" 53.1000 \n",
" C123 \n",
" S \n",
" \n",
" \n",
" 4 \n",
" 5 \n",
" 0 \n",
" 3 \n",
" Allen, Mr. William Henry \n",
" male \n",
" 35.0 \n",
" 0 \n",
" 0 \n",
" 373450 \n",
" 8.0500 \n",
" NaN \n",
" S \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"PassengerId 891 non-null int64\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 714 non-null float64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Cabin 204 non-null object\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 83.6+ KB\n"
]
}
],
"source": [
"# 返回每列列名,该列非nan值个数,以及该列类型\n",
"train.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 418 entries, 0 to 417\n",
"Data columns (total 11 columns):\n",
"PassengerId 418 non-null int64\n",
"Pclass 418 non-null int64\n",
"Name 418 non-null object\n",
"Sex 418 non-null object\n",
"Age 332 non-null float64\n",
"SibSp 418 non-null int64\n",
"Parch 418 non-null int64\n",
"Ticket 418 non-null object\n",
"Fare 417 non-null float64\n",
"Cabin 91 non-null object\n",
"Embarked 418 non-null object\n",
"dtypes: float64(2), int64(4), object(5)\n",
"memory usage: 36.0+ KB\n"
]
}
],
"source": [
"test.info()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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" Parch \n",
" Fare \n",
" \n",
" \n",
" \n",
" \n",
" count \n",
" 891.000000 \n",
" 891.000000 \n",
" 891.000000 \n",
" 714.000000 \n",
" 891.000000 \n",
" 891.000000 \n",
" 891.000000 \n",
" \n",
" \n",
" mean \n",
" 446.000000 \n",
" 0.383838 \n",
" 2.308642 \n",
" 29.699118 \n",
" 0.523008 \n",
" 0.381594 \n",
" 32.204208 \n",
" \n",
" \n",
" std \n",
" 257.353842 \n",
" 0.486592 \n",
" 0.836071 \n",
" 14.526497 \n",
" 1.102743 \n",
" 0.806057 \n",
" 49.693429 \n",
" \n",
" \n",
" min \n",
" 1.000000 \n",
" 0.000000 \n",
" 1.000000 \n",
" 0.420000 \n",
" 0.000000 \n",
" 0.000000 \n",
" 0.000000 \n",
" \n",
" \n",
" 25% \n",
" 223.500000 \n",
" 0.000000 \n",
" 2.000000 \n",
" 20.125000 \n",
" 0.000000 \n",
" 0.000000 \n",
" 7.910400 \n",
" \n",
" \n",
" 50% \n",
" 446.000000 \n",
" 0.000000 \n",
" 3.000000 \n",
" 28.000000 \n",
" 0.000000 \n",
" 0.000000 \n",
" 14.454200 \n",
" \n",
" \n",
" 75% \n",
" 668.500000 \n",
" 1.000000 \n",
" 3.000000 \n",
" 38.000000 \n",
" 1.000000 \n",
" 0.000000 \n",
" 31.000000 \n",
" \n",
" \n",
" max \n",
" 891.000000 \n",
" 1.000000 \n",
" 3.000000 \n",
" 80.000000 \n",
" 8.000000 \n",
" 6.000000 \n",
" 512.329200 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" PassengerId Survived Pclass Age SibSp \\\n",
"count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
"std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
"min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
"25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n",
"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
"75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n",
"max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
"\n",
" Parch Fare \n",
"count 891.000000 891.000000 \n",
"mean 0.381594 32.204208 \n",
"std 0.806057 49.693429 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 7.910400 \n",
"50% 0.000000 14.454200 \n",
"75% 0.000000 31.000000 \n",
"max 6.000000 512.329200 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 返回数值型变量的统计量\n",
"# train.describe(percentiles=[0.00, 0.25, 0.5, 0.75, 1.00])\n",
"train.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2..数据初步分析,使用统计学与绘图\n",
"目的:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 549\n",
"1 342\n",
"Name: Survived, dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 存活人数\n",
"train['Survived'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1)数值型数据协方差,corr()函数\n",
"来个总览,快速了解个数据的相关性"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Survived \n",
" Pclass \n",
" Age \n",
" SibSp \n",
" Parch \n",
" Fare \n",
" \n",
" \n",
" \n",
" \n",
" Survived \n",
" 1.000000 \n",
" -0.338481 \n",
" -0.077221 \n",
" -0.035322 \n",
" 0.081629 \n",
" 0.257307 \n",
" \n",
" \n",
" Pclass \n",
" -0.338481 \n",
" 1.000000 \n",
" -0.369226 \n",
" 0.083081 \n",
" 0.018443 \n",
" -0.549500 \n",
" \n",
" \n",
" Age \n",
" -0.077221 \n",
" -0.369226 \n",
" 1.000000 \n",
" -0.308247 \n",
" -0.189119 \n",
" 0.096067 \n",
" \n",
" \n",
" SibSp \n",
" -0.035322 \n",
" 0.083081 \n",
" -0.308247 \n",
" 1.000000 \n",
" 0.414838 \n",
" 0.159651 \n",
" \n",
" \n",
" Parch \n",
" 0.081629 \n",
" 0.018443 \n",
" -0.189119 \n",
" 0.414838 \n",
" 1.000000 \n",
" 0.216225 \n",
" \n",
" \n",
" Fare \n",
" 0.257307 \n",
" -0.549500 \n",
" 0.096067 \n",
" 0.159651 \n",
" 0.216225 \n",
" 1.000000 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Survived Pclass Age SibSp Parch Fare\n",
"Survived 1.000000 -0.338481 -0.077221 -0.035322 0.081629 0.257307\n",
"Pclass -0.338481 1.000000 -0.369226 0.083081 0.018443 -0.549500\n",
"Age -0.077221 -0.369226 1.000000 -0.308247 -0.189119 0.096067\n",
"SibSp -0.035322 0.083081 -0.308247 1.000000 0.414838 0.159651\n",
"Parch 0.081629 0.018443 -0.189119 0.414838 1.000000 0.216225\n",
"Fare 0.257307 -0.549500 0.096067 0.159651 0.216225 1.000000"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 相关性协方差表,corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关.\n",
"train_corr = train.drop('PassengerId',axis=1).corr()\n",
"train_corr"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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udScTzALuAYoArLXrgCtrIgERERGRo/J6/f84CmOMG3gOOB/oDlxljOl+WNi9wLvW2p74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\n+Q2QTU4NAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#画出相关性热力图\n",
"a = plt.subplots(figsize=(15,9))#调整画布大小\n",
"a = sns.heatmap(train_corr, vmin=-1, vmax=1 , annot=True , square=True)#画热力图"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2)各个数据与结果的关系\n",
"进一步探索分析各个数据与结果的关系"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### ①Pclass,乘客等级,1是最高级\n",
"结果分析:可以看出Survived和Pclass在Pclass=1的时候有较强的相关性（>0.5），所以最终模型中包含该特征。"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Pclass \n",
" Survived \n",
" \n",
" \n",
" Pclass \n",
" \n",
" \n",
" \n",
" \n",
" 1 \n",
" 1.0 \n",
" 0.629630 \n",
" \n",
" \n",
" 2 \n",
" 2.0 \n",
" 0.472826 \n",
" \n",
" \n",
" 3 \n",
" 3.0 \n",
" 0.242363 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Pclass Survived\n",
"Pclass \n",
"1 1.0 0.629630\n",
"2 2.0 0.472826\n",
"3 3.0 0.242363"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.groupby(['Pclass'])['Pclass','Survived'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train[['Pclass','Survived']].groupby(['Pclass']).mean().plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### ②Sex,性别\n",
"结果分析:女性有更高的活下来的概率（74%）,保留该特征"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Survived \n",
" \n",
" \n",
" Sex \n",
" \n",
" \n",
" \n",
" \n",
" female \n",
" 0.742038 \n",
" \n",
" \n",
" male \n",
" 0.188908 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Survived\n",
"Sex \n",
"female 0.742038\n",
"male 0.188908"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.groupby(['Sex'])['Sex','Survived'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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PQEZJ0jR1My2zANg7bnmks27akqxNMpxkeHR09FieQpLUhZN6QLWqNlbVUFUNDQ4OnsyXlqRZpZtyfxRYNG55YWedJOkU1U25bwWWJlmS5HRgDbD5xMaSJB2PKcu9qg4B64B7gV3AXVW1I8nNSVYCJHl9khHgKuALSXacyNCSpBfWzdkyVNUWYMuEdTeOe7yVsekaSdIpwCtUJalBlrskNchyl6QGWe6S1CDLXZIaZLlLUoMsd0lqkOUuSQ2y3CWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KDLHdJapDlLkkNstwlqUGWuyQ1yHKXpAZ1Ve5JViTZnWRPkg2TbD8jyZ2d7X+RZHGvg0qSujdluScZAG4F3g4sA65OsmzCsGuBn1TVK4HfBT7V66CSpO51s+d+CbCnqh6uqmeBTcCqCWNWAV/qPL4beFOS9C6mJGk65nQxZgGwd9zyCHDp0cZU1aEkTwLnAPvHD0qyFljbWTyQZPexhNak5jPh+30qin/TzUb+bPbWed0M6qbce6aqNgIbT+ZrzhZJhqtqqN85pIn82eyPbqZlHgUWjVte2Fk36Zgkc4CXAo/1IqAkafq6KfetwNIkS5KcDqwBNk8Ysxl4b+fxlcCfVVX1LqYkaTqmnJbpzKGvA+4FBoAvVtWOJDcDw1W1GfjPwJeT7AEeZ+wfAJ1cTnfpVOXPZh/EHWxJao9XqEpSgyx3SWqQ5S5JDbLcJalBlrukEyLJi5L8fL9zzFaW+wyV5Pwk30zyvzrLFyb5eL9zSQBJfhnYDvxJZ/miJBOvj9EJZLnPXL8PfAQ4CFBVD+L1BTp13MTYTQefAKiq7cCSfgaabSz3mevFVfU/J6w71Jck0vMdrKonJ6zzopqT6KTeOEw9tT/Jz9H5hUlyJbCvv5GkI3YkeTcwkGQp8CHggT5nmlW8QnWGSvIKxi7r/qfAT4BHgPdU1Y/7mUsCSPJi4GPAW4EwdvuS36yqp/sabBax3Ge4JC8BTquq/9vvLJJOHZb7DJPkwy+0vao+c7KySBMl+SNeYG69qlaexDizmnPuM89Z/Q4gvYBP9zuAxrjnLkkNcs99hkpyJnAt8IvAmYfXV9Wv9i2U1NE5Q+a3gGU89+fzFX0LNct4nvvM9WXgHwNvA+5n7O0PPaiqU8V/AX6PsWsv/jnwX4E/6GuiWcZpmRkqyV9W1WuTPFhVFyaZC/yPqrqs39mkJNuq6uIkD1XVBePX9TvbbOG0zMx1sPPfJ5K8Gvg/wMv6mEca75kkpwE/7LxN56PAvD5nmlWclpm5Nib5WeAGxt6gfCdwS38jSUesB17M2JWpFwPvAf5VXxPNMk7LSOq5JEOMXaF6HjC3s7qq6sL+pZpdLPcZKsnZjO0JLWbc9FpVfahfmaTDkuwGfgN4CPjp4fVV9dd9CzXLOOc+c20BvsuEXx7pFDFaVd6/vY/cc5+hknyvql7X7xzSZJK8Cbga+CbwzOH1VfXf+hZqlrHcZ6gkvwYcAO7hub88j/ctlNSR5A+AXwB28P//siwvsjt5LPcZKskHgE8y9k43h/8nllcA6lSQZHdV+f6pfeSc+8z168Arq2p/v4NIk3ggybKq2tnvILOV5T5z7QH+vt8hpKO4DNie5BHGpg2Dp0KeVJb7zPUUY7883+K5c+6eCqlTwYp+B5jtLPeZ6w87H9Ipx/PZ+88DqjNYkhcB/6Sqdvc7i6RTi/eWmaGS/DKwHfiTzvJFSbxoRBJguc9kNwGXMHYqJFW1HfA0SEmA5T6THayqJyes8zYEkgAPqM5kO5K8GxjovKXZh4AH+pxJ0inCPfcZJsmXOw9/xNj7pz4DfAX4O+Df9SuXpFOLZ8vMMEl2Am8G/pix96Z8Du8tIwmclpmJPs/YnfZeAQyPWx/G7jHjQVVJ7rnPVEl+r6qu63cOSacmy12SGuQBVUlqkOUuSQ2y3DUrJflYkh1JHkyyPcml/c4k9ZJny2jWSXI58E7gdVX1TJL5wOl9jiX1lHvumo3OBfZX1TMAVbW/qv53kouT3J9kW5J7k5ybZE6SrUmWAyT5rSSf7Gd4qRueLaNZJ8k84M+BFwPfAO5k7NYN9wOrqmo0yWrgbVX1q0l+Ebgb+CDwO8ClVfVsf9JL3XFaRrNOVR1IcjHwBsau8r0T+ATwauBPkwAMAPs643d0bvtwD3C5xa6ZwHLXrFRV/wDcB9yX5CHgA8COqrr8KJ9yAWO3V37ZyUkoHR/n3DXrJPn5zp00D7sI2AUMdg62kmRuZzqGJP8S+EfALwGfS3L2yc4sTZdz7pp1OlMynwPOBg4Be4C1wELgPwIvZeyv2v8A/HfG5uPfVFV7k3wIuLiq3tuP7FK3LHdJapDTMpLUIMtdkhpkuUtSgyx3SWqQ5S5JDbLcJalBlrskNej/AbcSqHcykSlzAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train[['Sex','Survived']].groupby(['Sex']).mean().plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### ③SibSp and Parch 兄妹配偶数/父母子女数\n",
"结果分析:这些特征与特定的值没有相关性不明显，最好是由这些独立的特征派生出一个新特征或者一组新特征"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Survived \n",
" \n",
" \n",
" SibSp \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0.345395 \n",
" \n",
" \n",
" 1 \n",
" 0.535885 \n",
" \n",
" \n",
" 2 \n",
" 0.464286 \n",
" \n",
" \n",
" 3 \n",
" 0.250000 \n",
" \n",
" \n",
" 4 \n",
" 0.166667 \n",
" \n",
" \n",
" 5 \n",
" 0.000000 \n",
" \n",
" \n",
" 8 \n",
" 0.000000 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Survived\n",
"SibSp \n",
"0 0.345395\n",
"1 0.535885\n",
"2 0.464286\n",
"3 0.250000\n",
"4 0.166667\n",
"5 0.000000\n",
"8 0.000000"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train[['SibSp','Survived']].groupby(['SibSp']).mean()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train[['Parch','Survived']].groupby(['Parch']).mean().plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### ④Age年龄与生存情况的分析.\n",
"结果分析:由图,可以看到年龄是影响生存情况的. \n",
"\n",
"但是年龄是有大部分缺失值的,缺失值需要进行处理,可以使用填充或者模型预测."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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V0SRHk+TQoUM73Bx7yf3NjzkHc68PBnrxDvk7B+7Prv9Ir7sv7+4j3X3k4MGDu705ADag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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.FacetGrid(train, col='Survived',size=5)\n",
"g.map(plt.hist, 'Age', bins=40)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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aciUhyLuZTDM2T6Mnxi9m0rthM1bmyJklbHdsnFpoAJjcAvYiIO/8o+cXwdjR\nachSY7OpIBOXVtrY67m59tauQ+v55JOxAMZm9WW4bjqKG1swMHX0pRuKnT6WjL6X8/haBJORborZ\nKzWNoSUkWHLO4fPg45ahwdK1gUngWcHmgY3LqwH7OFSNPpRuFpom7js5d2QKfdcO7ZUKRk/OmzxW\nn8Xooy1TY+r0ketmEtKNpsHz+aFaeKeBOJ74GDD6uYaBA9uL/qjdDMdAUUwiqth2fegag67lJ2q2\nhXWCxD6N8PtmeUH4dsfBpeU2GDtkjT58T02d4ZHzS3juiDhv8vYwXIyGpooVepVGD5TI6MewV9J1\nkG7GEpk66vINOaiOhXQzH8YC0Ch1lmOgKBZbRvn2StcvnI/fsvTo9/BCRwjdIGZ5QfjmgY3VeQsL\njfLf32FAPnpdY3jk/CKu7/QSjeJZRd7TbDw0ld2QzVrFSefw2IzeLUG6UUQ+ECk66lum1rc6sAwN\np0M5dFKoRKFP742NlhqPLd24pdrubC9/MTihbcWsnUhJVOhnlNH3HA9dx8PynIWl9mSWuxRFzOg1\nPHJ+CcDR0Om7thet+5NhqWVioWkMId2oNfpxB52owPdLkG4GffQhoz/iXvr1zS4unmgl9vpOApUo\n9POp5SNlaPRLLROez3FQYtRA3/FzPfSElmWgG14INK6vh0tU2pZR6nFNC+ShX25bE1vXWBSk0ROj\nB45GZHHP8dA0tMyFO5cKWCyJSKjy6IHxF4SXMTCl6iXQje6oN2TXtjoT2yololKFnhqysq3ww2IS\n07G25+dGFBPaph65bojRawKjn8VQMwo0W25bE1vXWBSu0PdYnrNwfql5NBh9AVtxkaGpbOkmZPRj\nSjf9qBk7+g1Dda0To3ePuka/2ZloDj2hEoU+HVWcNexRFJNYPmK7xRl9W9gNSydr1Iyd0QXhWwfB\ne7ncNgNGf6iFPmiME/N9+PzSkWD0sjWCadDQVJYs2XE8GBqLCqaIyHUzRjPW83kUCTKOBNRVNGMp\nDsBxjy6j3++72ArNDZNGJQp9WrophdGHwWZlxun2XR9WwZ2OsmZsxOhndEE4STcrcwGjP+wIBNH9\n9OiFRbx652Amex8iZGsE07i03ELX8XA3o/msWiMIlMPoxVC0MqSb9O8cSTdHmNGTc2qSKwQJlSj0\n6QXhZTD6RYoqLlFH7rvekM3Y4PegczW2V87mgvBIo5+zsDiByeNh4HocplDoHzm/BM6B52/sHdox\nlYGsAk0oElfcVWTRA+XYK0W5Zizpxg6uqbRl+Tg0Y8k5dYykGwr6SjH6MqQbSTH61Neu44+e2xj6\nNYeyV5qxsybdjJ2v0ILwV25Idt0GAAAgAElEQVTv49c/f7XQ15J0c6JlYqllouf4pcTdAsBvfPF1\n/NhvfAU/9htfwSefXsv9ei/F6I9KQ7bn+MqpWAJ5rrNSLDuOJ3XcAHJ7Jeccv/pHL+G1OwfRx37n\nK+v4wtU70tcQYw+yGP2vf/4qrt5S33xVy1GOg70yzqE/Jow+vTe2VOlGUuh/9T+9hI/9aeG9KBFs\nr3ihD7b4BAU+3Yyt0oLwTz97A7/wH19MBLCpsNWxsdg0YOhaqXt5/+i5Dfz0730Dz67v4M+u3sG/\n/ONXcr/H9f2E/nxuqQlDY7g54+Fm3QKpreTSuLGtLvTb4d9KhojRCwX69bsd/OofvYzPXLkZfexf\nPPUy/v0XX5e+hljcVVp/1/bwC//xRXzqazeUx9lRhBea4XVmH3FG37Z0rMxZE/9ZlSj0LVOHxmJG\n37M9MJafEpmFhSZJN8lC7/sc69vdyEEyDIZpxpp6vCEn3Yyt0oJwejQuooVuHthYDk/Ksvbybuz2\n8FO//TU8cn4RT/3ku/HffttFbOz2c7/P9ZKMnjF2JKJtA40++xybbxhoWzpu7anfp9t7fZxelC+y\niOyVQoF+aSNg3eJWJ8fjyl4SFXrG1D76TjQAqT7Pu44rJXSmFrpujjCjX9vq4OJyK9NKWxYqUegZ\nY4nlI51wu9Q4b4Cha5hvGAOM8/Z+H7br4+5+fjFJI2jGFnvLDJ3B54HE4KeasVVaEO6EhbHIBbXV\nsbHcDgr9Ugl7eT2f4x/+1lfRc3z82gcfR8PQcXapif2+m7uBy/X5gKPEOALLKoou3Dmz2Mws9Bu7\nPeW0pawZS4Ve9K07nq980iNdfrFpKtMr6fzOOs8D6WbwySP20c/23zML08ihJ1Si0AOkW8ca/Tj6\nPGGxOdgwpAbWVscZeiflMBq92ExyU1k3c1Z1FoRT5neRC2qrY0ePmYsl7OX9V3/yCr7wyl38zPsf\nwf2n5gEAZ0MWmifBuJ4vbeDNemEo0owFgFMLDWzsyt8j2/Wx1XFwRsHoTZ1BY8km6ksbwYJ10bfu\neD46jvwcJRZ/om0qNXq6AWTJgh3F72sc8WYs5xzrU/LQAxUq9EGwWVzox4k/ICxKLIDiROHWkNZL\newhGT4zE9XmkxWuMBqaqsyCcbkKFCv2BgxPtgMmPy+ifeWMLv/zZl/D9bzuHv/nExejjVJxURUw8\nbkNPF3o284/6Rc/90wsN3FYw+tvh06qK0TPGgh5SDqN3Pa5m9C4VekvpuqHvzcqr70nWCALxFrdZ\n/3uqsNN1sNd3p2KtBCpW6Pd6cQTCOMmVhCCTXs7oAeDuwXDyzTBZN9Fkn+cPZt1YSZfRYYIKfFHp\nZqWd1uiH/x18n+MnPvE1nFls4p/9jccSEt3ZcEHyjVxGz6MnJIKhzf5C6b7jFyr0ZxabypshfVzF\n6IHklinH8/Hq7YPo/xMcP1+6Wc5g9MWlGxmjP9rSzfqUUisJlSn080IsQNHH1zwsNgfH9MUwqLv7\nwzVk+44HSy92XPToaXv+4GRshRh91IzNuaB6joeO7cXN2DEiJvquj9fuHOC/f8c9A8vFzw7B6HUt\nefpaxvjSzWHunnU9H7bnF5ItTy800LE9aS/jVtjMPpWRiNgwtKhAv373IHK3OOlmrKKI0/eeaJnh\noqDB942Wn2RJN13bQ8uUafQUUzzbN24Vohz6CS8cIVSm0IvRvR27LOkmfkogrG12I/nhzpAN2WGy\nbiw99gGnm7HE6NMX6ZO/+if46OeKedrLAjH5PCa83aH4g6DQj7OAnYqKrN/RsnQstcx8jd73BxIe\nDY2NlY1yc6eHb/mZz+I/v7Gl/Jqf+K2v4ic+8dWRf0YWyKZYtBkLyG+It/aCj51eVBd60f774s39\n6ON0Pvih5Kh23QTfeyI8H2TDVxGjV+j8wde4UkYfNWOP6N7YtWgq9tgxeiORXllGM3ZJIt2sb3fw\ntovBntFhGL3vczgeL2yvNDShGeulF48M2itv7fbwws09/NnL8gGVSUHG5GQgO+rKXMzAR41BoJ+l\nksHOLjZxM4fRpwemgIAF2hnZKF95fRP/+5+o5ye+9M1N7PZcvHFXPXH656/cwZVrkwlPI+ab56MH\nYv39lsSKemu3D11jWJ3LZvTUUH1pYw+MASfnrfh8CG+YtutL5z2I0dMTmUy+KSrdSO2VRzzUbG2z\ni8WmMfBEOylUp9A3k83Yclw3Jvb6bnSiup6P69s9PHp+EbrGhvLS2znFKQ1DiFmNsm5oMlayIPwb\n4UTn8zd3pyof0E0o74Ki+ANicIDc1VQEVOhlgVsAcGapWcB1wyOvNcHUsxn9J768jn/2h88rrZtf\nX98GoB7S2ek62NjtZ2bMjINh4rnJI0/sXcTGbg8n563MTWgio39pYw9vWmljoWnG54PwhCeb94ib\nsVToB9+zqBmrKPSez9F35VIVXT/2jPdcVFjfmp7jBqhQoae9sZxzdEtsxgLAXsg6b+z04Pkc96y0\nsTJnDdWMpUfTovZKS7CHpZuxsgXh3whZ4nbHyWWzZYKKbhYTBpKBZoSlERewUxFRFfqzi43c94DS\nK0Xk2Su3OjY4B55d25Z+/tn14Gareg0a5d/q2BPZZUqFvllAHiRZRsro9/qZjVggqdG/tLGHB88s\nwNCYtGcjK9Sij148dhF5jD5rZWg8MHVEGf1Wd2qOG6BChX6+YcDxgjt81/YLPb7mYSk1pk+d7ksr\nbazOWbgzhHRjD1noDcEeFmXdhIWJFoSLy0e+fm0n+vxzU8xVjwam8hj9ATH6+FFzccRMejti9HLG\neXaphTv7/cyiLbNXBgNT6gJMfYZnJIXe8zm+cS0s9Apd+OXQa+75fCLJncNkPC00DLRMXaHR93NX\n0zVNHX3HQ9/18M27naDQ67FryUkwelmh99AwtIi0yKKKu3Z2MzbeF6uOQDiKrhvOecDop6TPAxUq\n9KLlsGu7pQ1MAbHXe20r3rh+cr4x1HTssNKNGT16+lEzVmSg7dTykSvXdvCeB08BAJ6/McVCX3Bg\navMg2YwFaF1j+dLN2cUmOIfSJw7I7ZVFGD0QePjTeO3OfnTjVd0saKgIwEgRGnkYJrWVMYbTiw3p\ndOyt3Z4y/oDQNDX03cBW6fkcD55dgCVIX+L7qCr0TVOPs+0l0g19n+35UmYe74YedN3Q33bW7bIy\n3Nm30XP84yvdAIFuXZpGn1o+sr7ZgcaAcyeaWJmzhrpYqXE1io8+3YwFki6ju/t9XN/p4TvuW8Wl\nldZUo3bjCztfulloGoniPOqWKVomoSz0SwEbzZJvAkaf1ui1zHkAGpB75o3tgT4IyTaAWqN/WUhh\nnEihd4o3Y4GgIZvW6B3Px90DO5fRN4xgYIoGpd4SMfrBuYquxDUTFHotStqUSjfCx2Q2TdUaQUC8\nfo5eoY8I55SslUCFCj0lWG52bPh8vORKQnp6c32ri3NLLZi6htV5ayjXTWwJLHZcsWtAsFcKg0Hi\n8nBagffIhUU8fG5xuoxe0nyTQcy5ISy2DOx2naGbx3nSzZkCMQiu50sGptRZN5xzbHdsLLdN3D2w\noyxwwrPrOxG5UL3Gyxv7eMuZBQCYSEN22F3JpxebAxo9PQWdXshn9D3Hx4s392BoDPeenIMpSF92\nQqMffD964WBXw1QX+p7wJNCTPBVkpdTqWhDTcBSlG/LQT8taCVSp0Icyy53wRC2V0QvSDTVATs43\nsNd3C2/HIY2+sL1SkG7oXBWlmyDyIfjZ5Lh55PwSHjq3iNfuTm9TUtGBqa2OEw1LEZZaJnw+OA+Q\nB3qMV72X55aCv1FWofdkjD5jYGq/78L1Od4dymPPrCXlm69f28GjFxaVN4vdXtAkf8d9KwCGY/R3\n9vuFmrckfxSdIQkYfbLQ07/PZHjo6Wf0XQ8vbezj3pNzsAwtIX2JPRup68bx0DRE6UbN2NP/nxDt\ntVX8vqauHckNU/FUbMUYPWPsScbYi4yxq4yxD2d83X/HGOOMsSeGPRCSbmiIqZzJ2GRU8dpmN7qL\nroZFq+gFGxX6IV03smYsECxb6YQF8hvXdnDPShtLLRMPnVsE58ALN6cj38QDU/nN2OV20vM7agwC\nscZ0oSYst01YhpY5Hev4g4ze1Jgyppgase+4bxVtS8czb8QNWdfzceX6Dh67cCIsdoOvQY3Yt987\nXKFf2+zgXT//FJ564Vbu1w67cOfMYpD0KfZ6boXvWT6j19Fz/MhxAySlr6R0I2HrbmCYaEWMXqLR\nO9mFPpZu5Ln5pq4dyZ2x61sdnJy3lL/3JJBbtRhjOoCPAngfgIcBfJAx9rDk6xYA/AMAfznKgZB0\nc7tERj/fMKCxQLrpux429nqRLrY6HzCeovJNf8hCL2Z1SJuxlsDor+3isQtLAICHzwWbksqSb168\nuZfZ1IwZffYFtXkQ59wQsrZ4ZcHJkW4YYziTY7H0PPnAlMoxQ43Y1TkLb7u4lHDevHxrHz3Hx9su\nLsHUWXRTF0HWyrddOIG2pRc+b/7s6h3Ynp8ZKUwYdoVmNDQlvPZGQUbfMDR0HQ9rW52o0ItPM3aR\nZqyhRU8fMkYv5tDLdH56UlCRury5iFnF2mYXF6Yo2wDFGP3bAVzlnL/KObcBfBzAByRf978C+AUA\nI5nAidFT8l4ZEQiMsTDYzMX17R44R2RpIj94US/9sPbKREyxtBkbuG52Og7e2OzgkQtBgb+43MJC\nwyit0P/I//ElfOT3v6H8PD0a511Q2x17QLpZHDHBMtbo1e/l2cVmZrCZ4/PBCARdU2ajUCN2ec7C\nt15axnPXd6Li9PWwEfvYxSVlXs5LG/tomhouLrewMmdFN448/MUrdwEAdoGVi3Ezttg5JotBuL3b\ng8ZiIqMCXV+cA285G0REi9KXm2Ov7Eeum4xmrO1FZCBLulEVeiPHRTWrCKyV05NtAKDIs8MFAOIS\nz3UA7xC/gDH2OIBLnPM/YIz95CgHQtOiJN2UMTAFxBbAOEQoKPQn58NCPyFGb0YRCINZN0C8IPzK\njaDIPHo+YPSMMTx0brEU543t+ri+08Nu744yYpkejWUsltBzPBzY3oB0o2L0X3jlDn7yE19LFN22\npeM3fvQduLTSjopI1nt5dqmFT3/9Br795/4o+tjphQZ+58fehaapSyMQrIzFI9u02Lxt4vF7TsDx\nOK5c38VfedMynr22jYWGgXtX55TOnZdv7eOB0/PQNIbVOatQM5Zzji+Ehb6ITbDneNBY8T6QlNHv\n9rE638icigWShOXNJN1ocTM2OTAl0+j9wHVDGr3k/OnaHlbnLOx0nWzpRqXRa9lzEbMIz+e4tt3F\n+x47N9WfW+SMkp0x0bvPGNMA/AqAf5T7Qox9iDH2NGPs6du3byc+RwvC7+wFF1AZGj0QWwDjEKGU\ndFOU0WcEcclgRKFmcTNWZPS0IJyGdGi5NQA8dG4Bz9/YHXv6kqx3+30XT7++Kf0aN2L0+YNGA4xe\nsZf36+s7uL7Tw3e/5RTe+9AZPH7pBF6/28HV24HOTUUkrbGL+NvfeRk/8MQlvPehM3jvQ2dw78k5\nXLm+G2njgesmvWFKba+k3+FE28Ljl4KsI/LTf319B49eWIKmMaUX/+WNPbz5dFAQA2tu/nnzyu39\niLgU2X3aDcP8im5WIx3+lsDob+31cmUbAJFbxjI0vCkkP4aw/jLXR++GjD7LXml70ZOzbGgqbzd0\nVnN9VrGx24Pj8ak2YoFijH4dwCXh3xcBXBf+vQDgUQCfD0/QswA+xRh7P+f8afGFOOcfA/AxAHji\niScSV6Sha2gYWtyMLUG6AWIL4PpWF6bOosfdOUtHw9CKM3ry0ReMKRalG0LaXtl1PHxtfQfnl5qJ\nR+2Hzi2iY3t4Y7ODyyfnCv08GcRH+j9+8Tbedf/Jga8hJp+VErgVsWGFRp9qxtJr/tzfeAymruEb\n13bw2ec2oo8XkW4ev2cZj9+zHP37//7P6/jSa5vRa7i+JI9eZ8qCGmX1tEwYuoYLJ1p4Zm0btuvj\n+Rt7+JHvvBwe0+Br7PUc3Njp4c1nAoljZa6BFws0y0m2AbKfmAg9d7j5kcWWgYahJRh9kfgDAGiG\nhOX+U/NRU9wUpK8ik7FNQ4emMVi6phiYcrEaPjnLGb0LLWM3tKHN/iKZNCJloYIa/ZcBvJkxdi9j\nzALwgwA+RZ/knO9wzk9yzi9zzi8D+CKAgSJfBAtNI2oclqHRAyTduFjb7ODCiVb0SMsYw8n5RuEY\nhIjRF4wpNsVQM3+wGUvrBL/82iYeDRuxhIdKasiSxn16oYHPvSh3fUQbpjIYPcUfpAv9fGrymGB7\nPhiLGTtdyHZqCreoDCZ+Lf0dZANTlsBI09juOFhoGtH3PH7PCXz1jW28tLEH2/Px2MXgbyBj9C/f\nCp5EYkYfePHz5ge+8MpdnF9qSm8eMnTtYktHCNF0rHBD39jtF2L09HPeEt68AIQ+etLo87NuSLZp\nmJq8Get4EYGRWTRpX6zqCcbUtULv2yxBjGGZJnKvNM65C+DHAXwGwPMAPsE5v8IY+1nG2PvLPJi5\nhoG9fnYnfljE0k13YECh6CM4MLyPXoxZdSWFnhaE39rrDxT6t5xdgMbGL/TkQ/+bT1zESxv7uLbd\nHfiaIvZKamSupKQbXWNYaBgDGr3t+rB0LbqArXShd/MZfRr0vkeMXjowpUUL2Qd/h+TA1+P3LOPa\ndhf/6bkNAIGbho4prQtf3aBCHzP6vutnrsjzfY4vvnoX77z/JKwMN5CInjP8wp0zC01shENTrufj\n7kEfp3KslUBc6B88uxB9TOxPJFw3iiJOr0GefBGO58PxOE6G54x0oCrn9zUzbtyzirWtDhgDzp/I\n/xuViUJXGuf805zzBznn93POfy782Ec455+SfO17RmHzQMxyAXWDZljQOsFrW52BkePV+WJNNWAc\neyWX2ivF3/XRC4uJ722aOu47NY/nxiz0G7s9NAwNf/1bLwAAPp9i9ZzzmCFnXFCbQiMzDdm6xn6q\n8Stj44DaXikDvUbf9eH7HD7H4M5YQ71+bqvjJI7/8XuCwv4fvvQGTrTN6NwwJQ3dlzb20DC0iIXR\nDEaW7PfCzT1sdRy88/5VWEYxZjpK9EeQdxPc0O/sB+mcefEHQGx2ePB0XOhF6YsK/kLDGGD0vs9h\nu36k89OUbfp3AYLzw9CYshmbZboI7JVHTbrp4sxCs/CEfVmozGQsEHvpgXIZfd/1cWffHmD0q3ON\nwhr9sANTprB4RNaMnRN+V3LciCjDeXNjp4ezS008cHoeF0608LkXkg1wkflmuRvi5Epr4HOyBeyO\n5yeefNJsvIhGn4b4VEAX/+DAlDrxcLtjJ47/kfOLsHQNt/f6eOzCUvT0ESwvGZRu7j81H92oVwoM\n2/3Fq4E+/877V3PD1ghBM3a4S/L0QhyDQAW/iEb/7ZdX8LMfeATvfsup6GOWpBm72DIHPPBEeuhY\nm4Y+wNhF62TL0pWFPuvGZkj+FrOOdQnhnAYqVejJeQMUd7fkgaZjgUFd7OS8hTv7/UJZLbbrQ2PZ\nThERmsaghwMoXuhsEZuxlNZ5aqEhTRp86NwCrm13sdMZPQ53Y7eHs4tNMMbw3W89hS+8cifxiC0W\n97zUx/mGIb3JLbUGl4+krZzpyNm8UDMZxHx/ukENhprF6xtlv4PI6BuGjodDp9PbLsY3WpmP/uWN\nPTwoaNkr8wUK/St38abVNi6caMEyNOmqvTTIyTIMTi8GUR4d240knCKM3jI0/NA7Lyf+BqL0RT2b\nxZY5UKSj3Hwjlm7ShV4MLGtbutx1k8PoLV07cox+fas79UYsULlCHxTl1hAWszwsCqu60pam1XkL\nfddP5MKrYHtB8RrmuEw9cA3Ism5oneCj5xdl3xo3ZG8G8s3tvT7+/m8+g7/+0T8vHCJ2czdg9ADw\nngdPo2N7+PJrccaLmCOSmfp4YGN5Tr7ybLE5uHyE3isCFem+0IzVGHK93iJERu+klq0TqPDL8lG2\nD5yBJxKSbx4L9XlgUKPf6zm4vtOLvOaAIN0oCr3nc/zla3fxzvtWg2NXxCqk0c1huDLEFsv+UIxe\nBlH6Ima/2ByUbih7vpkh3URTr6aBtmUo0ivdzCd3I2MuYhbheD5u7Ex34QihUoWepJuyhqWAZKFP\n30lXwp2amwXkm77jFW7EEkxNC0PNiNHHnyNG/9iFQdkGAB4JC/1z13fxu8+s46/9yh/j//nadXx1\nbTuaHs4C5xwbO32cDS/6dz2wCkvXEjq92CDMuqA2O85A/AFBtjeWmrGEtHTjeP5QbB5I6vyeN9jz\nEH9Ouqg6no+9vjvgGvqet57GQsPAX3lTbONMa/RXbyUbsUA8T6Bq5F+5voO9not33r8aHXuRydiR\nmrGL8dDUrd1+tPt1FERbnXyekG4GGX1KujH1gcZ0V2D0LVOXDl0F0o3a4W1oxW6Qs4Ib2z34HLg4\nZccNUNFCX5a1EoiHepqmNnABkMf3TgHnje3FzaeiMI3AxeDxYIpTfBq4uNzGt19exn/5yFnp955a\naGB1zsIvffZF/MPf+hruOzmHn/7+hwDEXtwsbB7YsD0/YvRty8A77ltJ2CzFx+Ise2Va3xYh2zKV\nlm6CQaS40TfMknWCeLNwFdKNOKSWPH4a+Eo+lXzXm0/h6z/zX+GUIHWkLX2RtVJg9AsNA6bOomUs\naZB/nhi9TPeXYaRm7EIcg3Brr4fVuYYyLC4PkYHA9aMCu9hUSzctM1+6ydLo81aGWsbRYvTi4qNp\no1KFPpJuSmT0S63gNS8utwdkl5NzxYPN+imWWgSGFoQyef4g+2xZOj759941YK0kMMbwbW9ahs+B\nj/zXD+OTf+9d+O63ngYAvFGg0FMg2FnhMf49bzmNV24fRDcKsfhk2f82D+wBayVhKWR84gVpSxi7\nJRQ7x/MHHDN5SDZjc6SbgUJvR8ea+3NSjdOXN/ZgGRruEVgYYyzTmvuFV+7i/lNzUe8l0P2LSTfD\nkpw0oy+iz6tgCtJXzOiNATYe77YV7ZVp6Sa+GbQzmrFZhd7Qjpa9Ms6hr6UbAOVNxQKxdCMLEVqN\n8m4KMHrXH7pBHDA5Ds/3oY/Qc/ilH/gWfOHD34O//Vfvha4xXFxugTHgjbuDfvg0yENPjB4A3hM6\nLEi+ERl9VqjZdsdJ7IoVEUVBC6xelqsjjrOPJN2Qzq8IiQu+Rr5+Lgo0UzyVJI4zFaOQdtwQVuYa\n0mas4/n48jc3E1PIqkTMNHrucANTQHDzsgwNt/Z62NjrRUvDR4E45Od6HBoLrsmO4yX6QiTdNCLX\nzeDAFDl1YulG3ozN89EfJelmfasLXWM4tzRdDz1QsUI/N4lCH0o3skm0lZymmoi0N7wIKGZVxuiL\nYLFpJvJlGoaOc4vN4Ri9cFLdd3IO96y08fkXA5tlkoXLL6i+62G/76o1+vZgDILtDd4URUYvY/x5\noPfeSUg3gwNTgJrRFyn06Qbgyxv7CccNQRVs9uz6Djq2F+nzwbHruT56L/SmD3vuM8aCBSS7AaM/\nU2BYSgVx/SXdjFuWDs6RYOyDzdgs140RMPqURZNzjo7t5vroj5p0c/5Ec2RpbRxUrNAHf/QypZum\nqeOH33UZ/823nJd+br5hFJJuRmH0FLPq+f5IhV6GSyttvLF5kPt1GztBXO0pIUOHMYZHzi/i9fBG\nIV5Eqkdk2qV636nBYgfIg83SzViAGpLxMM6wN81EMzaSbgafGoBBRh8HmuVLN2k9/fZ+P3GzJKh2\nDl8Jt4V9m5DTYxXQ6CPdu2BEsYjTCw3c2Onizn5/LEYvSl+Ox2HqWjS4KEov/QF7pWRgKqHRGwPr\nCG3Ph8/VS0eA8OnqCNkr1zY7uHhi+vo8ULFCPwnpBgD+6fsfwbdfXpF+LpiOLSbdDM/og0dPasaW\ngXtW2oUY/Y2dHk4tDDbmxHF1UaJQ2SufeuEWDI3hux4cDEQDBtc1AvL3yjI09AXppug8QvT9QjPW\nUUg3piafjI1C2RR9hvRx0utzHrDspmSKUVXor97ax3zDSOTNFGkqDrtdSsTphSaev7EHn0M6k1EU\novQVMHoWFWLRVSNz3fTcpLzTHfDRJxl9kSUrhs4KRUfMCta3uocyLAVUrNBPohmbh9W5YkvC097w\nIqBHT1l2+qi4Z6WNjd1+7q7bm+GwVBpNU0PfSU4/MqaO0f3cC7fwxOXliLmnkV7ADhRrxg4r3Ri6\nBo0FhV45MGXE6xtFbHUcmDqLLK1ZEOUCkitkQXYrcxb2eu4AU796ax/3n55PNP6tAuFcVPhGcZyd\nWWxE7/84zVhR+nJ9H0Yo3QTHJ26LGpRuOB/MxzH1IPY5kG6SNwJxoEoF6wjtjO05Hm7t9Q/FcQNU\nrNBPwl6Zh9X5RhSNnIW+O4KPPmzseT4fqRkrwz2rwYmyvpXN6m/u9KSDMw1DFwaXgguvberSZuy1\n7S5euLmH7wndPjLEe2PjQu8oGH28po5HRXkYkPVRlWdvKBg92UOLDLuRXOD7PC70CkYPYGDT1NVb\n+3ggJXNlrTgkpJ0sw0Bk8WMVeoHR225ggaVCLEo36WMlSVOUb8ThL5nOL9ovs47nqDRjo4XgNaOf\nnHSThaLbggKNfrjjMjQWasqjNWNloKZynnxzc7cn7e43DC2SbqggtixDuoT5c+FC66xCr2L0A4Ve\nZPSuH8kEw4B0fk/RjJXtAAAG4w+yIFoM6X2S9WZkwWa7PQe39vp44HSy0BcJNaMiOZp0Exf3Uadi\nAXGhPTF6FhXiZKEflG6AWLsPvt6NZB+Zzl9EujF1DV540511HKaHHqhYoZ+bwGRsHlbnLWwd2Lkn\n0ygavWVo4Yap8pqx5Od+46660B/0Xez1XJxRFnofnPOIxbcs+SPyUy/cwj0rbdyvaMQCwcVu6iwR\ngyCbOUg0Y/3hpRs6dtuLNXrZcvDg9QelG9XAVxridG0/KmhqRi/q9DRFmy70RQam8rYtZUFk9Cdz\ndsVmwRB+d5easaTRy3KA8c0AACAASURBVBi9kHUTfDzJ2Ol3iW8WrvB5sl9mN2MBeaTFrOGwcugJ\nlSr0C00Dlq4VckeUhdW5BlyfD4zxpzGKvTIYmOLweHmMfnXOQtvS8cam2ktP1kopoxf0VNsl6cYY\nYME9x8MXXrmD73nr6UzJgzE2EIMgcyiJrNb2BpeGFAE9FUSMPu260dXSTXFGH0+HZjJ6msEQGvmq\nQt8owOjTuvcwoMbv6pw19DkqIvrdfR922DBvSdh4L5QxaQdyvDc2ydhj6WbwZtEpcGMjKe4obJla\n3+zAMrSEC26aKLJKcGpomjp+58fehftOjb4+b1hEMQj76jF/YHTXTVCYymP0jLFc583GjjrciopW\nX5gwbVn6wI3uL165i57jR9O4WVhsxjEIlHE/SekmmoxVSjeDjP7bCjJ6MWkzGgyS/N3Jk78lMPpX\nbu/D0rWB4bxCjL6AlKECxSCcGkOfB4T3zw1CzcyERi88sTl+okEt2xsrTr1mSTfZPnp17PSsYW2r\ng4snWtHNcdqoFKMHgMcuLiWy2icNetTNm44ddTLW9cttxgLB419W3o0s/oDQiPTUuKnZtvSBi+mp\nF26hZep4x71yW6oIcfmI53NwPhhBLEo3o7huxNdQTcYaEkbPOc/M6kmDjsv2fMF1M1iMguZuUrp5\n5dY+Lp9sD644NNSbrwhxg3P492W5bcLU2VjWSiApfQU++lijT9ork5k89DUJ6UYIaJM1dIu4bkzF\npPMsYn2reyhhZoTKFfppo8gSCSAYvR9auonslSj1Tk6MXhVXfEMSf0CIGb0Xu24sPXExcc7x1Au3\n8J0PnCwkJYiFniQKKaMfIwIBiF03rlK6iZuJhAM7+D2HlW5cj2dKN7rGsNxONvKv3tofkG3E48pi\n9b0xNHrGGB44vYAHJT97GIg3yiCPSO26Ec8LujmJN4OuMPUa3wiEzxf4fcV1nLOOtc2ONIZlWjj2\nhT5OsFQXehqcaYxsrxx+QCgL96y00XU85WLzjd0eFpuGtNGVkG7C4t6ykho97ZfNctuIWGwaUQSC\narduktHzkRk9ebwBiXSjERuPb1qqxeYqiHJBbK+UH6s4NNVzPLyx2RmwVtJxB8elLljjDEwBwCf+\n7nfgp558y0jfSxAb0bQljGSZtOtGfPJoKKUbMlcYA6/RLdCMjZrDEkfYLGG/72Kr4wxsuJsmjn2h\npwyXLOmGLtChY4rDaF6Pl8/oAbXF8uZOT8rmgfiiTEg3pp5oeD1VwFYpQtwgpFq5KDZjaepyWFh6\n4BiKm7HynbEiox8m/gBISTc5DdIVwZr7zbsH8Dlwv4RVUz8ii9GP04wFgIWmOfYeUpHRuz6HoTNo\nYUNWLOLpTVix6yYl7wxIN6Lrpoi9Mm4OzzJo5uWwpmKButDD0DUst83M6VgVS80DbbEvm9GTRUul\n0webpeQnVUNwSMQ+eh2O70dS0OdeuIWHzy0qbxZptITFE6ol6uNOxtJrJiMQUnn0klAzGmgayV6Z\nw+hX56zoiUHluKHjTh9XGj3bA2PlrdAcBfT+uZ4P243/RkHMcFyke46XiIUgdt9P2SvbZlK6SUo7\nHhqGlmlSOCrN2LXQIXdYHnqgLvQAgunYrLybYReDE2hDTtnNWMqzzmT0inCrZoLRxxo9D5uFOx0H\nX3ljqzCbB4CmFRd6uignId00jNjFBMhcN4PNu+0uRRQPOTDl+VHhUj3JLQvSzdVb+2AM0pkDMUtf\nhW5YPMtaoTkKxJuc6/Po/UwvDummXTfE6MOeBuc8sVRE1YzNm5eJey6zLd0cZg49oS70CB/BMxi9\niqXmwQzDrHwf0Ep8p5umjrOKuGLH84PERYUDgy7QvutFEgc9Prs+x7PXtuH5HO8SYnZzj8fQYbs+\nfJ8rm7FitIDj+ZHMMgziCAS5dMMYg6ElA8S2h2T0RX30QMjoO8Gw3dVb+7i43JJKL0WYac/xp5rx\nJEO6GSsyetED3x9oxialm57jg/PYPy/T+TsF9uPS8eTNIFQd61tdtC1dubxnGqgLPYIdm1kxCHbO\nI7wKphYUN9f3B2SGcaHy0t/e64NzqKUboRkrSjdAcEEd9IOLcWmIobXIVeF6mc1Y+hnU6BsW1IxV\nhZoBg9G2WwdDavTCcRZpxvo8eGp45faBcoI4vRxdhlHWCJYN0V7pejw6Z1umnu26SWXdxIvBg49r\nGkPT1FLBaNmLwQFhh+2sM/qtDi5JNtxNE3WhRzAdmxVs1ouY3bDN2CCrw/V56YMS96zKvfTxwhG5\ndBM1Y10fTvh4Lj4ip/eBFgF9bdf2lDJXHHzlweeD+noRkM5PhVym76YXh2x17HDHa7GfJ8oX9F6o\n/u7E0O7s9/Hq7cEwM4JZQKPvOt5IHvoyYQpNY8fzYRmxdNNNu26Ev6+hazA0Fr1f4tIRQtsyJNJN\n9ryMatJ51rC22TlU2QaoCz2AwGK53XGUyzdIh00vl84DPXr2HK/UZiwQMPqbu72BuOJoheBiDqN3\nPDhhw018ZJddpHloRRqtn+m6AQKrGYCRpJt4YEqeXgnQDoCkdHNiiL9b2l6pMSgdQqvhzuFn13fQ\nd31pIxZAZMvN9NHnrNWbBkj6csOdsXQzbltGYkNU2nUD0Jap4PeTeeTT6wQ7BX5f1Q7gWQLnPMyh\nP7xGLFAXegBBMxYANjty+Yb0+2EDo8RHdq3kx7Z7VtrgPIgSFiHbFSsidt2EFjqNRY/IjueP5Oem\n1+zaXqSnyvLogZjtjSrdiIxetmDc1FniUX+r4xT20NP3A3Ghb2Q0SInRf+m1uwDkjhtAvflKRDfl\nZDksUDSw6/Ho/U03Y3uSp4+mqUVPvrKp17bQsKfXyGvGik9Xs4qdroP9vlsz+ipgNWc6ljz2q0M2\nU0RGX/aayEuKFMuN3R4sQ1O6TMRIWcqkIXbteDzSUYdhly2hGafqZxCjPyBGP2KhTy4HH3wNQ0sG\niA0TfyAel+0GPnrZ0hECFfovf3MLgLrQR9uxPPWyGNF3fpigJyJb6KO0BTbOOQ8HppLH2jB0QboZ\nPIfaqZtFEdeNEU0pzy6jj6yVNaM/fMiyxUXcPbChseLODQI9evacyTRjgUGL5Y2dYLOUioUmJ2OD\n4xL9013Hg66xoQaaRJ90lusGQNTslbHxPMQavQ/G5Bp9EA2dZvTDSzduuHgkqwFPUt5rdw5wct5S\nnh9FIhC6kuJ5GKBpbhqYApJFmhrKg9JNvLmsK5H/0jp/kG6Zp9GHjH6G8+gph75m9BUASTeqhuzd\nAxsrc9bQCZSWwOjLbsaenLfQMvWBQq9aIRgfUzICwTSYoEvzaNBlGIdAU8LoVa6b/XEYfXTjVPc8\n0vbKYOnIeNKNCg1Dx0IYwJeV2R87jrJDzQ7bdQME71/f9eD58axDyzLigThFRn/Lihk9fW1Suknq\n/B0hC0cF0eo6q4inYmtGf+g4OZ/D6Pf7I3lgiSn33XInYwF1XPHGbk+6cET8PtoyZXs+TE1LFLee\n46E5pIRQxHWTlm5G1eiB4LFf9YREC9mB4Allr+cOtd8gsle6wXuRZ6ldCc8dlWwDJBebq9At4Cuf\nBkxdQ9dJ9lnalh7tASAdfkCjN2INXhZvkNb5hxqYmuEIhLXNLpZapnLn8rRQ6GpjjD3JGHuRMXaV\nMfZhyed/gjH2HGPsWcbY/8cYe1P5hzo5LDZNGBpTTsfe3bcjh8UwEPeilt2MBQbjijnnuLEjXyEo\nomEEj9nUcBMvqFEKDrG7rhPHKqQZeyNqxo6n0QNBUVTdOMXl3vFUbPGbdDoCIU9OIQJQhNHn2Sur\nodGzqE9jCtINEPzt0tulCE0hD0eWNS/q/J5f7L2NB6ZmW7o5bNkGKFDoGWM6gI8CeB+AhwF8kDH2\ncOrLngHwBOf8bQB+G8AvlH2gk4SmscQ4exp3D+wo5XIYmEIxKrsZCwzGFe90Hdiun7sgumnqYUxx\n0l5pu7wQ00pDjKFVZt0Qow8v9pFCzaLXcJUafzAwFRb6aCp2VHtlPqOn/k4Woxf96Sp0cxq/04Kp\naxHzjgamrPiJraeQbpqmJgxMSeyVAqOXSTvSYxF6R7OK9a3uoWbcEIqcWW8HcJVz/irn3AbwcQAf\nEL+Ac/45zjlRyy8CuFjuYU4eq3OWMvb3zn5/pF2cImvVS27GAsA9Ky10bC+a6qUtT3lN40bYOHP8\nYKWfJTJ6Z9AjnQdxMjJK+lQ2Y0dn9Gb0VOAp309DZ1Gs7VZneEavawwai7Nu8oovvXamdJPD6H0/\niMGugnRjCIWenkjFdYJxymbqic3UI1mHnghE1i82Y+N9sTmFvsCTUJUReOg7h5paSShytV0AsCb8\nez38mAo/CuAPZZ9gjH2IMfY0Y+zp27dvFz/KKeDkfEMaVWy7gc47rLUSSDpLJsHoL4RM4XropadG\n53wj+wJqGHowGRuu9BMHU7pjMPruhJuxDUGjVz0RmHq86Hx7hEJPr2EXaMYCwINnFnDhRCtTLqPf\nVRWBQAWyCoXe0llUkOmJVAwlizdhpRi9oUeNWsqxEQ0IbdMIlsaE5xgQZ+GoQPLcrProb+/30XP8\nQ82hJxS52mRXlPSdZ4z9LQBPAPhF2ec55x/jnD/BOX/i1KlTxY9yClhV5N2QnLMygnQjFruy7ZVA\nsPADAPbDpR9kXcxbxUjNWMrgEVMfR8lcIeZGzVhDYwMuo0Ef/Wj2Svo5KgcU2QMBMaJ4uEaYpWtw\nXF6oGfujf/VePPWT7850KVl6NjPtSqSOw4Kha5E7RnTdAEE+jWrlYSDdhIxdMgwV3Swcr9AaQfHn\nzyqjX98iD/3hM/oic+7rAC4J/74I4Hr6ixhj7wXwvwB4N+c8ewFrBbE615C6bu5Ew1LDSzdi6NYk\nmrFU0Pf6VOiJ0Rcp9D5sj6NlaYkLqjvCKL6mBU6eIMdGHkFMxe4gJQsMg6gZ63jKJwLRXjmKRk/H\nFtsrs49T0xgaWvb7pYVzCSqNftylI2UiaMYmY6CTjJ6kuexmbPocivo4gvyTd56RjDaroWZklJgV\njf7LAN7MGLuXMWYB+EEAnxK/gDH2OIB/BeD9nPNb5R/m5LE6b2G/7w5kxxDLPzlKM3bC0s1CyOip\nwO8XLvR66LrxYWosekR2R2T0QHyh2658t26jRHvlQd9VM3ojzrrZ6jgwNJb7fqRBN4u+JNNlVKQz\neESo5JDDgKlrgutmUKPvuwrpxtTQc+P0SiWjt73YlVPg9xWluFkDMfoLs+C64Zy7AH4cwGcAPA/g\nE5zzK4yxn2WMvT/8sl8EMA/gk4yxrzLGPqV4ucpCFYMQxR9UsBlLjH4/xejzpBvKJUnbK+1wMnYU\nCYG2TFGsQhqRB37MyVggx16psUjTpfiDYeNhkxp9OX83cfFKGsSSq6DRGxpDxyFnVOyjB8h1o/bR\ne+GugSCwLHkOioV+mOA8M5TRZhFrmx2cnLeGCgicFAodAef80wA+nfrYR4T//96Sj2vqoEJ+d9/G\n+RPxHZgK/yj2ykk3Y+dThX6/YKEnRk/2SjGmuEiqoAwtS0fXCXbBytg6/YxxmrFmUXslMfqD4eIP\nCBSjELhuymP0qgUa4y4GLxOmriF06wrSDWn0cbNdll4JBE8nPccbYOuizi/LwlEfD5vZgan1rW4l\nGrFAPRkbgQr5ndTQ1J19G6bOolH3YTBpRt8wgkbqQDM25wJqmEEz1vGp0AcXdD9cHDKudCNjwfFU\n6xjSTfg9Ple/n4auRQM2w8YfEEhPL+KjL4ogp0fOTONm7OFfjuI5S+93q4jrhlJRHV86iyGVbgoU\neiND8qo61rY6hx59QDj8M6siODkXM3oRd/f7WJ1rjLQdxhSKUZk7YwmMMcw1jFi6sV00TU26eUkE\nNWMdN1g8Ql+/1yvmb5aBXBeqxd+GxsAYsN9PygLDQCy6pkK6sQQGuNN1sNgantGbuoZOuCClVOkm\nh9FXQ6OP31cjZa/s2m48MJVeLCMwemkz1hyUbooQCkuItJgleD7H9e1uJaZigYLSzXEA2Sc3U4x+\n1KlYILlcYxLSDRDIN6J0U6TxSD56jSExMLUbDlyNwuhpsYShMalGz1gg6VAfYSSN3hCfkBShZroW\nhWDtdh0snR+t0O/3gveirOJrCceVRpWasSJJIKnMDDdIdWwPHEiQAwKdM33Xk+6DFadri7puguNh\nM8noN3Z7cDxeCccNUDP6CHOWjoahyRn9CI1YIOmdn4R0A4SFPmTi+z03V58HKFI2dMgIEQi7YXHL\nG2SRIa8ZCwSFuuuMzujF11XaK3UWxdru9lwsjcDoLV2Lbp5lMXrTYEpGP8r6xklBfF/FJ1KKMOgp\nFqREeUe2n+u66dguNFbsvTU0NpP2yshaWQEPPVAX+giMMZycbwzEINw9sHFyxO3tVkKjH+vwlBAZ\n/UHfxVyBIt0w9MSGKXpE3+3SUucRpBtLsFcqfllLov8Og2TPQyXdBJqu6/nY77tYbI3QWzHivsew\ne4JVsDK05u4QUsakIUo34hNpO4ww6Cka1JFG73qhcyvlugmz5zu2G+2LLSKHZjWxq4y10FpZN2Mr\niGA6NiXd7NsjRRQDadfNhBh900j46ItJN8HS8p7jwTQ0MBYM9BCjH0mjN4KdoSofPZBm5ONJN6rv\nN7TANUK5P6MwelPXoiG0soLGTF1TRiBQLHAlJmOF81T8/0GevCddIwjEjH6/58Lx+MA5JAbfDbNN\nS3RRzRLWtzpgDDh/IjtJdlqoC72AlTkrId10bBddxxtZukkw0PJ7sQACK+We0IydbxYo9GbsXqGm\npqlrUaEfRStuWVoo3cgnY4G4UKu2Q+XBKsDoiYXSoNsoOeDmBKQby8hg9KF0U9bPGgciixff76AH\n44aFXsLowycfip1IF3rLiHX+YRJSA3vlLEo3XZxdbJb2RDguDv/MqhBW5xqJgSkq+iM3YxOMfjKV\nfkHQ6A/6XiGNXjz5qKlmaCyWbkYdmLI92BmWRCocpqaN5GJKZAcpbiakK1N0xWiuGxZ5ycuUbtQD\nUwFLHuU9KRuiLi8+kbZFjV7K6IOP0fUjO4dI55c1a1UwMt63KqMqOfSEutALODlv4c5+P8p3Hyf+\nAAh0f9K/J9mMTUo3+RdQQ9LUNHUNe2NIN60wpjavGRv8rNEKGmXGAMhcPALEN+lRpRtCWdJNJqOv\nyHYpIPm7i4U+bsb6mc1YFaOnj3VDH33Rc8zStZlk9NcqkkNPqAu9gNV5C33Xj4K37o4RaEagi2VS\nzdi5hoEDO9jxWbQZKz56U2EMpJvRm7ENUwfngRdf2YylQj+GREGvnWWvBOK/3UjNWLHQl8XoMyMQ\nqlPoxeIu/h3bYbO9p8j/aUSMniy6g+876fyBK6fY32UW7ZWO5+PGThcXKzIsBdSFPoHVaGiqH/43\njCgesRkLxI/Ck2L0C0JUcccuKt0MMnpDZ/BC5jSqdAME3nUVoxefHkZF5O1WvJ9UnDbH1OgJpdkr\nMwZ/uiPs6Z0UVPJY2zJiRp/RjN3OYPSk8w8Ts5H1vlUV17e78Dlq6aaqoKEpslhSHMKoGj0QF6ZJ\nMnoAuLXXA5CfXAkk5QhicOnG27CgC9fn6kJORXMUayUhYvQq10348TsHo0s3lvDasqI2CixD7bpR\nedMPAyKjNxPvQyDd9B1Pbq8Mj7+IRj9MQqo5g4x+bTPMoa+lm2qCYhDoZL27b6Nt6WOlz5GWPIk8\neiAu7Dd3g0I/bDNWZPSE0bJuBI98TjN2lKnY9GurIhBE6UbX2Ej9holIN7oG2/Wkn3v9bid3ofu0\noBqYCvR1V3lTMvUgO540etk51BaasUX/LoY2e/bK9a1qDUsBdaFPgJg7STebY8QfEKJCOkEfPQBs\n7AbHPDd0MzbW6Olz6e1QRSBe2PnN2DEYffSEpLqZxM3YxWaxoZw0xB5CufbKQQmia3t45fY+Hjm/\nWMrPGRdm1FNKbglrW3rgo3fl0g1jDE1TjzR6tXQjz8JRH8/sSTdrWx0YGsPZxWrcvIG60CdAWjy5\nbe6EgWbjwJxwM5YY/cbuENKNlNEH/x11aEds0CntlWUU+kjnVw9MAcHfcBTZJnht0XVTVkyxPALh\nhZu78Dnw8PmlUn7OuKD3L+1qall6NIimeuJrmbrgupE1Y3V0wpjiYXz0syjdnDvRzA0XnCaqcyQV\nQNPUsdAwIg/23X17pKXgIqhoTNJeCQA3d4pLNyIjowubmHCRrT8yJBi9yuOuJ3/WKIgZvXrDFBDc\npEfx0APJ4ysvpjhYzOGlrIJXru8CQHUYvaKPQueF53PlQF3T1DMb+i3LwE7Hgc+LLR0BsjdzVRXr\nW51K6fNAXegHsDIfT8fePeiPLd0YUaEf+9CkSGv0wzN68qUHBziq+0Nm2UyDivQ4TMfSs1+DtPu9\nEQPNgAm5bgxawJ4sWleu72KpZVbGoUHvX7qPIhZmVYNabPKrfPTDWngNffZCzdYq5qEH6kI/gNU5\nC5sHNjjnoUY/nnRD7HDSzdihpBvhgowsj2FBG6V5CSQZnKVoYObJLkUQ3SxyGD0wmrUSiG8illHe\ntCr97mn55rnrO3j43GIlpmIBtQVW/PsqGX34dzeF9ZQixHNrmIGpWdoZ23M83N7rV+bGTagLfQqr\n8w3c2e9jNwxnGle6MSbcjJ0bQboRWSoxN2Jyow7uFGnGNkpsxqqcO+INYJRhKSC+OZeZPUPHLQ5N\nuZ6PF27uVUa2AYTzIS3dCIVZ1bcgpq/U8IXXKNoLCgamZofR00LwqmyWItSFPoWT8xbuHtiR8+bk\nmIye2OuE6jwsQ4NlaFFfYVjpJm15HCWLHkiyvDzXTRk+enUEgsDox5RuygykouMWpZtXbh+g7/p4\n5EJ1Cr3KAptg9Iq/L50DKv1d7P8Mo9F7Poc/IzEIaxW0VgJ1oR8ABZvd3gsK5zhTscDk7ZVAEGzm\nc0BjxQZ8kow+ybJbIw4IJXz0Oc3YMiZjVc3tRKEfUbqh1yhrWEp8TZHRX7m+AwB4pCKOG2DwfCAk\nNXp1Mzb4WvnnxdcoPjAV3iBnRL5ZDxeOVCWHnlAX+hRW5ix4Pserdw4AjDcVC0AINRv70JQgL/1c\no5hvXNPYgF4eF/rxm7GWkdeMHV2PbuTo/OLHR2b0RjxTUBbodxcZ/ZXru2gYGu47OVfazxkXqtA4\n8bxQF3ot+/OjSDfhccxKQ3Z9qwvL0HBqTCWgbNSFPgUq7C9t7AEoQ7oJ3uJJNWMBREFmRWQbQlov\nN8eUbkxdi17D0rObsWNJN3n2SuG1R3XdxBp9edINHVc/xejfem6xUn7ryAJrpBm9WOgV0o2Rw+jN\n4ZuxpkTyqjIonniUocNJojpnWEVAhf3ljX0AwHK7+tKNyOiLgpw3RmSnG4/RAzGTm8ZkrCrUTHxa\nWCywhEWGSKMvUbpppJqxnHM8d323Uo1YIH7/ZANTBBVjpyatiq2P4roh8jArDdm1zW7lZBugLvQD\nIEb/4sYellqmsmgVxaSbsUCg0QNDFvrICpdk2aPaK4EhCr1C2imCvJjicpuxk5BugoK1vtXFbs+t\nXKFX9VESjF7xpENMX3UOjeK6mTVGHwxLVasRC9SFfgAUeXB7rz+2tRKYvL0SiAt8kaUjhLR0Qwxu\nnL2l9DSgkmamYa8Umf64A1OTkG6I0VexEQvE71/6byQWd6V0k+e6ET5ePI8++FmzoNHv911sdZzK\nWSuButAPYLkdF4dxG7GAGIEw9kspEUk3Q+jr9JgdNWON8aWbqNArGHsprpucG6f4tDCq64aOv0zX\nTboZe+X6LnSN4a1nF0r7GWWA3r90s1vTWPT3zRuYKiLdDBNTDMyG62YtdNxUbSoWqAv9AAxdi4r9\nuIFmgCDdTLAZS03YIovBCY2IGZPmPT6jJ1dFXjO2lMnYnFAzYPSBKXqNchl9cLx9Ny7095+aG2kR\n+yQRhZplTLaqjrllhdJNzsBUw9AK71CeJelmLbJW1tLNTIBiD8pk9BNtxjZGd91YKZY9VjOWXnOC\nzdjoBpWzM7ZpaiMX6klo9I0BRr9TOdkGyHZGtaJCnyfdZDP6YfpA5gxJN1WdigXqQi8FafPj5twA\nMfOcZDN2fpRmbHhRRi6LMWOKxe+dhutGZUmkheyjyjbBzwjtlRMamLqz38fGbr9yjVhAPB8Gb6R5\njD6WbuTnYWy/LH6e0nHIIp6rhrWtDuYsPSH/VgWFzmTG2JOMsRcZY1cZYx+WfL7BGPut8PN/yRi7\nXPaBThNksTxZAqO3Ks7oiRlHPvpSNPrsQj/JCAQgKKqjNmLp+4GSIxAERh9HE1eP0Wc9gbZMHbom\nDywD4hujarpa0xiapjZU78OaIUZP1sqqBNSJyH3HGWM6gI8CeB+AhwF8kDH2cOrLfhTAFuf8AQC/\nAuCfl32g0wTFHowbfwDEF8xEGX3UjB3OdWPqLDop6eIdx16Z57opY5Vg3NxWv4ahs5GtleLPKFO6\niRi950eOm4cryOijoTdJQ71l6cqcGyDfdUOfG4rRa/J45ypifatTuYwbQpEz+e0ArnLOX+Wc2wA+\nDuADqa/5AID/M/z/vw3ge1kVb2sFQdp8Gc1YKmr6JCdjR5Bumub/3969x0h1lnEc//7YXcpNbstK\nKSyXRVgotKWwtlAMYVtbwJiqlSYQ0/QPDf2DauslptWksRpjTIzWmHiLorExNWmLSjZEJBSbaExx\nacGCFGkDtitQwBbQKgrt4x/vOzCd7mVmdtjz7tnnk0xmztkzs7+dc+bZc94z533r3rbXVn+xbbv6\nQl/6TZ5SNb1gqpd/Fg11w6q+WKrwfOjfe1GquPfK/UfP0jxxZL+OOi4XSdQNU7d79KOG1/f6nozo\n44IpCDsDlTQP1g+Sk7FmRtfraV4sBVDOp2Eq8ErRdBdwY0/LmNkFSWeARuBULUIOtFqejE256aa4\nWDbUoo2+oY7hdT334V56ArgahdfobcSuhn7u0V88IVnLC6bia37/6Zf457kLtLe+u2avXWs99Sc/\ncnhdr4V+ZB8n2FrHagAABzlJREFUYws/q+SosfC+fWHz8xXtyAw0I3yPPsVv3EB5hb67T21pg1k5\nyyBpA7ABYPr06WX86mysunoyx07/h9lNY/r/Wguu5Nz5N6v+ql85rpk6jntWtLB8zqSyn3NnWzPz\np1xqOljZ2sTG9tnMaqy+g607Fk/t9dC1ecIo7m1/DyvnNVX9O5bObuSeFS1cPaXnZo/P3DqXln6s\nu7Ej6/ncbXNZs/DKql+j1IiGOja2z+bwqTcQ4q5lM2r22rX2wOp5tM2c+I75dy2dwcq5Pa+7a6eN\nY8OKFm5saexxmU/eMqeiI5k5k8ew7r3NnD13vuznZGXBVWNZtaB220wtyaz3kxySlgFfMrNVcfpB\nADP7WtEy2+Iyf5RUDxwHmqyXF29ra7POzs4a/AnOOTd0SNptZm2VPKecY9M/AXMkzZI0HFgHbClZ\nZgtwd3y8FniqtyLvnHNu4PTZnhDb3O8FtgF1wCYz2y/py0CnmW0Bfgw8KulF4DXCPwPnnHMJKKvh\n2My2AltL5j1U9PgccGdtoznnnKsFvzLWOedyzgu9c87lnBd655zLOS/0zjmXc17onXMu5/q8YOqy\n/WLpJPC3TH55eSYxOLpwGCw5YfBk9Zy15Tlrq9XMKhqaLLPOI8ys+uvgB4CkzkqvPsvCYMkJgyer\n56wtz1lbkiruUsCbbpxzLue80DvnXM55oe/ZD7MOUKbBkhMGT1bPWVues7YqzpnZyVjnnHMDw/fo\nnXMu57zQA5I2STohaV/RvImStks6FO8nZJkxZmqWtFPSAUn7Jd2XYlZJIyTtkrQ35nw4zp8VB48/\nFAeT7/8QXjUgqU7Sc5I64nRyOSUdkfS8pD2Fb12ktt5jpvGSnpD0QtxOl6WWU1JrfB8Lt7OS7k8t\nZ8z66fgZ2ifpsfjZqnj79EIf/BRYXTLvAWCHmc0BdsTprF0APmtm84GlwMY4UHtqWf8L3Gxm1wGL\ngNWSlhIGjf9WzPk6YVD5FNwHHCiaTjVnu5ktKvoKYGrrHeDbwG/MbB5wHeF9TSqnmR2M7+MiYAnw\nb+CXJJZT0lTgU0CbmS0kdBO/jmq2TzPzWzhPMRPYVzR9EJgSH08BDmadsZvMvwZuTTkrMAp4ljDO\n8CmgPs5fBmxLIN80wof6ZqCDMCxmijmPAJNK5iW13oGxwGHiub9Uc5Zkuw34Q4o5uTQW90TCNU8d\nwKpqtk/fo+/ZZDM7BhDvkxrNWdJM4HrgGRLMGptD9gAngO3AS8BpM7sQF+kibMhZewT4PPBWnG4k\nzZwG/FbS7jj2MqS33luAk8BPYlPYjySNJr2cxdYBj8XHSeU0s78D3wBeBo4BZ4DdVLF9eqEfhCSN\nAZ4E7jezs1nn6Y6ZvWnh0HgacAMwv7vFBjbV20n6IHDCzHYXz+5m0RS+mrbczBYDawhNdiuyDtSN\nemAx8D0zux54gzSak7oV27ZvBx7POkt34jmCDwGzgKuA0YT1X6rP7dMLfc9elTQFIN6fyDgPAJIa\nCEX+52a2Oc5OMiuAmZ0Gfkc4pzA+Dh4P4R/A0axyRcuB2yUdAX5BaL55hPRyYmZH4/0JQnvyDaS3\n3ruALjN7Jk4/QSj8qeUsWAM8a2avxunUcr4fOGxmJ83sPLAZuIkqtk8v9D0rHvD8bkJ7eKYkiTA+\n7wEz+2bRj5LKKqlJ0vj4eCRhgz0A7CQMHg8J5DSzB81smpnNJBzCP2VmHyOxnJJGS3pX4TGhXXkf\nia13MzsOvCKpNc66BfgLieUssp5LzTaQXs6XgaWSRsXPfuH9rHz7zPpkSAo3wso+Bpwn7JV8nNBW\nuwM4FO8nJpDzfYTDtD8De+LtA6llBa4Fnos59wEPxfktwC7gRcLh8hVZv6dFmVcCHSnmjHn2xtt+\n4ItxflLrPWZaBHTGdf8rYEKiOUcB/wDGFc1LMefDwAvxc/QocEU126dfGeuccznnTTfOOZdzXuid\ncy7nvNA751zOeaF3zrmc80LvnHM554XeDUmSPiLJJM3LOotzl5sXejdUrQd+T7hQyrlc80LvhpzY\nV9BywoVx6+K8YZK+G/v+7pC0VdLa+LMlkp6OHYptK1wm79xg4YXeDUUfJvSZ/lfgNUmLgTsIXVVf\nA3yC0P1roW+h7wBrzWwJsAn4ahahnatWfd+LOJc76wmdl0HozGw90AA8bmZvAccl7Yw/bwUWAttD\ndyPUEbrLcG7Q8ELvhhRJjYReKhdKMkLhNkKPkN0+BdhvZssGKKJzNedNN26oWQv8zMxmmNlMM2sm\njIp0CvhobKufTOjkDMKoQ02SLjblSFqQRXDnquWF3g0163nn3vuThIEdugi9BP6AMHLXGTP7H+Gf\nw9cl7SX0GHrTwMV1rv+890rnIkljzOxfsXlnF2FUp+NZ53Kuv7yN3rlLOuKAKcOBr3iRd3nhe/TO\nOZdz3kbvnHM554XeOedyzgu9c87lnBd655zLOS/0zjmXc17onXMu5/4PXrp9xD+bOYEAAAAASUVO\nRK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train.groupby(['Age'])['Survived'].mean().plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### ⑤Embarked登港港口与生存情况的分析\n",
"结果分析:C地的生存率更高,这个也应该保留为模型特征."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot('Embarked',hue='Survived',data=train)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"⑥其他因素\n",
"*在数据的Name项中包含了对该乘客的称呼，如Mr、Miss等，这些信息包含了乘客的年龄、性别、也有可能包含社会地位，如Dr、Lady、Major、Master等称呼。这一项不方便用图表展示，但是在特征工程中，我们会将其提取出来,然后放到模型中。\n",
"\n",
"*剩余因素还有船票价格、船舱号和船票号，这三个因素都可能会影响乘客在船中的位置从而影响逃生顺序，但是因为这三个因素与生存之间看不出明显规律，所以在后期模型融合时，将这些因素交给模型来决定其重要性。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 三.特征工程\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"#先将数据集合并,一起做特征工程(注意,标准化的时候需要分开处理)\n",
"#先将test补齐,然后通过pd.apped()合并\n",
"test['Survived'] = 0\n",
"train_test = train.append(test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### ①Pclass,乘客等级,1是最高级\n",
"两种方式:一是该特征不做处理,可以直接保留.二是再处理:也进行分列处理(比较那种方式模型效果更好,就选那种)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"train_test = pd.get_dummies(train_test,columns=['Pclass'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ②Sex,性别¶\n",
"无缺失值,直接分列"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"train_test = pd.get_dummies(train_test,columns=[\"Sex\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ③SibSp and Parch 兄妹配偶数/父母子女数\n",
"第一次直接保留:这两个都影响生存率,且都是数值型,先直接保存.\n",
"\n",
"第二次进行两项求和,并进行分列处理.(兄妹配偶数和父母子女数都是认识人的数量,所以总数可能也会更好)(模型结果提高到了)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"#这是剑豪模型后回来添加的新特征,模型的分数最终有所提高了.\n",
"train_test['SibSp_Parch'] = train_test['SibSp'] + train_test['Parch']"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"train_test = pd.get_dummies(train_test,columns = ['SibSp','Parch','SibSp_Parch']) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ④Embarked \n",
"数据有极少量(3个)缺失值,但是在分列的时候,缺失值的所有列可以均为0,所以可以考虑不填充.\n",
"\n",
"另外,也可以考虑用测试集众数来填充.先找出众数,再采用df.fillna()方法"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"train_test = pd.get_dummies(train_test,columns=[\"Embarked\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### ⑤ Name\n",
"1.在数据的Name项中包含了对该乘客的称呼,将这些关键词提取出来,然后做分列处理.(参考别人的)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)\n",
" \n"
]
}
],
"source": [
"#从名字中提取出称呼： df['Name].str.extract()是提取函数,配合正则一起使用\n",
"train_test['Name1'] = train_test['Name'].str.extract('.+,(.+)').str.extract( '^(.+?)\\.').str.strip()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"#将姓名分类处理()\n",
"train_test['Name1'].replace(['Capt', 'Col', 'Major', 'Dr', 'Rev'], 'Officer' , inplace = True)\n",
"train_test['Name1'].replace(['Jonkheer', 'Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty' , inplace = True)\n",
"train_test['Name1'].replace(['Mme', 'Ms', 'Mrs'], 'Mrs')\n",
"train_test['Name1'].replace(['Mlle', 'Miss'], 'Miss')\n",
"train_test['Name1'].replace(['Mr'], 'Mr' , inplace = True)\n",
"train_test['Name1'].replace(['Master'], 'Master' , inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"#分列处理\n",
"train_test = pd.get_dummies(train_test,columns=['Name1'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 从姓名中提取出姓做特征"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"#从姓名中提取出姓\n",
"train_test['Name2'] = train_test['Name'].apply(lambda x: x.split('.')[1])\n",
"\n",
"#计算数量,然后合并数据集\n",
"Name2_sum = train_test['Name2'].value_counts().reset_index()\n",
"Name2_sum.columns=['Name2','Name2_sum']\n",
"train_test = pd.merge(train_test,Name2_sum,how='left',on='Name2')\n",
"\n",
"#由于出现一次时该特征时无效特征,用one来代替出现一次的姓\n",
"train_test.loc[train_test['Name2_sum'] == 1 , 'Name2_new'] = 'one'\n",
"train_test.loc[train_test['Name2_sum'] > 1 , 'Name2_new'] = train_test['Name2']\n",
"del train_test['Name2']\n",
"\n",
"#分列处理\n",
"train_test = pd.get_dummies(train_test,columns=['Name2_new'])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"#删掉姓名这个特征\n",
"del train_test['Name']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ⑥ Fare\n",
"该特征有缺失值,先找出缺失值的那调数据,然后用平均数填充"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Age \n",
" Cabin \n",
" Fare \n",
" PassengerId \n",
" Survived \n",
" Ticket \n",
" Pclass_1 \n",
" Pclass_2 \n",
" Pclass_3 \n",
" Sex_female \n",
" ... \n",
" Name2_new_ Thomas Henry \n",
" Name2_new_ Victor \n",
" Name2_new_ Washington \n",
" Name2_new_ William \n",
" Name2_new_ William Edward \n",
" Name2_new_ William Henry \n",
" Name2_new_ William James \n",
" Name2_new_ William John \n",
" Name2_new_ William Thomas \n",
" Name2_new_one \n",
" \n",
" \n",
" \n",
" \n",
" 1043 \n",
" 60.5 \n",
" NaN \n",
" NaN \n",
" 1044 \n",
" 0 \n",
" 3701 \n",
" 0 \n",
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""
],
"text/plain": [
" Age Cabin Fare PassengerId Survived Ticket Pclass_1 Pclass_2 \\\n",
"1043 60.5 NaN NaN 1044 0 3701 0 0 \n",
"\n",
" Pclass_3 Sex_female ... Name2_new_ Thomas Henry \\\n",
"1043 1 0 ... 0 \n",
"\n",
" Name2_new_ Victor Name2_new_ Washington Name2_new_ William \\\n",
"1043 0 0 0 \n",
"\n",
" Name2_new_ William Edward Name2_new_ William Henry \\\n",
"1043 0 0 \n",
"\n",
" Name2_new_ William James Name2_new_ William John \\\n",
"1043 0 0 \n",
"\n",
" Name2_new_ William Thomas Name2_new_one \n",
"1043 0 0 \n",
"\n",
"[1 rows x 137 columns]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#从上面的分析,发现该特征train集无miss值,test有一个缺失值,先查看\n",
"train_test.loc[train_test[\"Fare\"].isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pclass Embarked\n",
"1 C 104.718529\n",
" Q 90.000000\n",
" S 70.364862\n",
"2 C 25.358335\n",
" Q 12.350000\n",
" S 20.327439\n",
"3 C 11.214083\n",
" Q 11.183393\n",
" S 14.644083\n",
"Name: Fare, dtype: float64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#票价与pclass和Embarked有关,所以用train分组后的平均数填充\n",
"train.groupby(by=[\"Pclass\",\"Embarked\"]).Fare.mean()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"#用pclass=3和Embarked=S的平均数14.644083来填充\n",
"train_test[\"Fare\"].fillna(14.435422,inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ⑦ Ticket\n",
"该列和名字做类似的处理,先提取,然后分列"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"#将Ticket提取字符列\n",
"#str.isnumeric() 如果S中只有数字字符，则返回True，否则返回False\n",
"train_test['Ticket_Letter'] = train_test['Ticket'].str.split().str[0]\n",
"train_test['Ticket_Letter'] = train_test['Ticket_Letter'].apply(lambda x:np.nan if x.isnumeric() else x)\n",
"train_test.drop('Ticket',inplace=True,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"#分列,此时nan值可以不做处理\n",
"train_test = pd.get_dummies(train_test,columns=['Ticket_Letter'],drop_first=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### ⑧ Age\n",
"1.该列有大量缺失值,考虑用一个回归模型进行填充.\n",
"\n",
"2.在模型修改的时候,考虑到年龄缺失值可能影响死亡情况,用年龄是否缺失值来构造新特征"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.19771863117870722"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"这是模型就好后回来增加的新特征\n",
"考虑年龄缺失值可能影响死亡情况,数据表明,年龄缺失的死亡率为0.19.\"\"\"\n",
"train_test.loc[train_test[\"Age\"].isnull()]['Survived'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"# 所以用年龄是否缺失值来构造新特征\n",
"train_test.loc[train_test[\"Age\"].isnull() ,\"age_nan\"] = 1\n",
"train_test.loc[train_test[\"Age\"].notnull() ,\"age_nan\"] = 0\n",
"train_test = pd.get_dummies(train_test,columns=['age_nan'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 利用其他组特征量，采用机器学习算法来预测Age"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Int64Index: 1309 entries, 0 to 1308\n",
"Columns: 187 entries, Age to age_nan_1.0\n",
"dtypes: float64(2), int64(3), object(1), uint8(181)\n",
"memory usage: 343.0+ KB\n"
]
}
],
"source": [
"train_test.info()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"#创建没有['Age','Survived']的数据集\n",
"missing_age = train_test.drop(['Survived','Cabin'],axis=1)\n",
"#将Age完整的项作为训练集、将Age缺失的项作为测试集。\n",
"missing_age_train = missing_age[missing_age['Age'].notnull()]\n",
"missing_age_test = missing_age[missing_age['Age'].isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"#构建训练集合预测集的X和Y值\n",
"missing_age_X_train = missing_age_train.drop(['Age'], axis=1)\n",
"missing_age_Y_train = missing_age_train['Age']\n",
"missing_age_X_test = missing_age_test.drop(['Age'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"# 先将数据标准化\n",
"from sklearn.preprocessing import StandardScaler\n",
"ss = StandardScaler()\n",
"#用测试集训练并标准化\n",
"ss.fit(missing_age_X_train)\n",
"missing_age_X_train = ss.transform(missing_age_X_train)\n",
"missing_age_X_test = ss.transform(missing_age_X_test)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"#使用贝叶斯预测年龄\n",
"from sklearn import linear_model\n",
"lin = linear_model.BayesianRidge()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True,\n",
" fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300,\n",
" normalize=False, tol=0.001, verbose=False)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lin.fit(missing_age_X_train,missing_age_Y_train)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#利用loc将预测值填入数据集\n",
"train_test.loc[(train_test['Age'].isnull()), 'Age'] = lin.predict(missing_age_X_test)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"#将年龄划分是个阶段10以下,10-18,18-30,30-50,50以上\n",
"train_test['Age'] = pd.cut(train_test['Age'], bins=[0,10,18,30,50,100],labels=[1,2,3,4,5])\n",
"\n",
"train_test = pd.get_dummies(train_test,columns=['Age'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ⑨ Cabin\n",
"cabin项缺失太多，只能将有无Cain首字母进行分类,缺失值为一类,作为特征值进行建模,也可以考虑直接舍去该特征"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"#cabin项缺失太多，只能将有无Cain首字母进行分类,缺失值为一类,作为特征值进行建模\n",
"train_test['Cabin'] = train_test['Cabin'].apply(lambda x:str(x)[0] if pd.notnull(x) else x)\n",
"train_test = pd.get_dummies(train_test,columns=['Cabin'])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#cabin项缺失太多，只能将有无Cain首字母进行分类,\n",
"train_test.loc[train_test[\"Cabin\"].isnull() ,\"Cabin_nan\"] = 1\n",
"train_test.loc[train_test[\"Cabin\"].notnull() ,\"Cabin_nan\"] = 0\n",
"train_test = pd.get_dummies(train_test,columns=['Cabin_nan'])\n",
"train_test.drop('Cabin',axis=1,inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ⑩ 特征工程处理完了,划分数据集"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_data = train_test[:891]\n",
"test_data = train_test[891:]\n",
"train_data_X = train_data.drop(['Survived'],axis=1)\n",
"train_data_Y = train_data['Survived']\n",
"test_data_X = test_data.drop(['Survived'],axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 四 建立模型"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 数据标准化\n",
"1.线性模型需要用标准化的数据建模,而树类模型不需要标准化的数据\n",
"\n",
"2.处理标准化的时候,注意将测试集的数据transform到test集上"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"ss2 = StandardScaler()\n",
"ss2.fit(train_data_X)\n",
"train_data_X_sd = ss2.transform(train_data_X)\n",
"test_data_X_sd = ss2.transform(test_data_X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 开始建模\n",
"1.可选单个模型模型有随机森林,逻辑回归,svm,xgboost,gbdt等.\n",
"\n",
"2.也可以将多个模型组合起来,进行模型融合,比如voting,stacking等方法\n",
"\n",
"3.好的特征决定模型上限,好的模型和参数可以无线逼近上限.\n",
"\n",
"4.我测试了多种模型,模型结果最高的随机森林,最高有0.8."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 随机森林"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"rf = RandomForestClassifier(n_estimators=150,min_samples_leaf=3,max_depth=6,oob_score=True)\n",
"rf.fit(train_data_X,train_data_Y)\n",
"\n",
"test[\"Survived\"] = rf.predict(test_data_X)\n",
"RF = test[['PassengerId','Survived']].set_index('PassengerId')\n",
"RF.to_csv('RF.csv')"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"#随机森林是随机选取特征进行建模的,所以每次的结果可能都有点小差异\n",
"#如果分数足够好,可以将该模型保存起来,下次直接调出来使用0.81339 'rf10.pkl'\n",
"from sklearn.externals import joblib\n",
"joblib.dump(rf, 'rf10.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### LogisticRegression"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true
},
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"lr = LogisticRegression()\n",
"\n",
"from sklearn.grid_search import GridSearchCV\n",
"\n",
"param = {'C':[0.001,0.01,0.1,1,10],\"max_iter\":[100,250]}\n",
"\n",
"clf = GridSearchCV(lr,param,cv=5,n_jobs=-1,verbose=1,scoring=\"roc_auc\")\n",
"\n",
"clf.fit(train_data_X_sd,train_data_Y)\n",
"\n",
"#打印参数的得分情况\n",
"clf.grid_scores_\n",
"\n",
"#打印最佳参数\n",
"clf.best_params_\n",
"\n",
"#将最佳参数传入训练模型\n",
"lr = LogisticRegression(clf.best_params_)\n",
"\n",
"lr.fit(train_data_X_sd,train_data_Y)\n",
"\n",
"#预测结果\n",
"lr.predict(test_data_X_sd)\n",
"\n",
"#打印结果\n",
"test[\"Survived\"] = lr.predict(test_data_X_sd)\n",
"LS = test[['PassengerId','Survived']].set_index('PassengerId')\n",
"\n",
"#输出结果\n",
"LS.to_csv('LS5.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SVM"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"from sklearn import svm\n",
"svc = svm.SVC()\n",
"\n",
"clf = GridSearchCV(svc,param,cv=5,n_jobs=-1,verbose=1,scoring=\"roc_auc\")\n",
"clf.fit(train_data_X_sd,train_data_Y)\n",
"\n",
"clf.best_params_\n",
"\n",
"svc = svm.SVC(C=1,max_iter=250)\n",
"\n",
"#训练模型并预测结果\n",
"svc.fit(train_data_X_sd,train_data_Y)\n",
"svc.predict(test_data_X_sd)\n",
"\n",
"#打印结果\n",
"test[\"Survived\"] = svc.predict(test_data_X_sd)\n",
"SVM = test[['PassengerId','Survived']].set_index('PassengerId')\n",
"SVM.to_csv('svm1.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### xgboost"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true
},
"source": [
"import xgboost as xgb\n",
"\n",
"xgb_model = xgb.XGBClassifier(n_estimators=150,min_samples_leaf=3,max_depth=6)\n",
"\n",
"xgb_model.fit(train_data_X,train_data_Y)\n",
"\n",
"test[\"Survived\"] = xgb_model.predict(test_data_X)\n",
"XGB = test[['PassengerId','Survived']].set_index('PassengerId')\n",
"XGB.to_csv('XGB5.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### GBDT"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true
},
"source": [
"from sklearn.ensemble import GradientBoostingClassifier\n",
"\n",
"gbdt = GradientBoostingClassifier(learning_rate=0.7,max_depth=6,n_estimators=100,min_samples_leaf=2)\n",
"\n",
"gbdt.fit(train_data_X,train_data_Y)\n",
"\n",
"test[\"Survived\"] = gbdt.predict(test_data_X)\n",
"test[['PassengerId','Survived']].set_index('PassengerId').to_csv('gbdt3.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### 模型融合voting"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true
},
"source": [
"from sklearn.ensemble import VotingClassifier\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"lr = LogisticRegression(C=0.1,max_iter=100)\n",
"\n",
"import xgboost as xgb\n",
"xgb_model = xgb.XGBClassifier(max_depth=6,min_samples_leaf=2,n_estimators=100,num_round = 5)\n",
"\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"rf = RandomForestClassifier(n_estimators=200,min_samples_leaf=2,max_depth=6,oob_score=True)\n",
"\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"gbdt = GradientBoostingClassifier(learning_rate=0.1,min_samples_leaf=2,max_depth=6,n_estimators=100)\n",
"\n",
"vot = VotingClassifier(estimators=[('lr', lr), ('rf', rf),('gbdt',gbdt),('xgb',xgb_model)], voting='hard')\n",
"\n",
"vot.fit(train_data_X_sd,train_data_Y)\n",
"\n",
"vot.predict(test_data_X_sd)\n",
"\n",
"test[\"Survived\"] = vot.predict(test_data_X_sd)\n",
"test[['PassengerId','Survived']].set_index('PassengerId').to_csv('vot5.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 模型融合stacking"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"#划分train数据集,调用代码,把数据集名字转成和代码一样\n",
"X = train_data_X_sd\n",
"X_predict = test_data_X_sd\n",
"y = train_data_Y\n",
"\n",
"'''模型融合中使用到的各个单模型'''\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn import svm\n",
"import xgboost as xgb\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"\n",
"clfs = [LogisticRegression(C=0.1,max_iter=100),\n",
" xgb.XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),\n",
" RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),\n",
" GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)]\n",
"\n",
"#创建n_folds\n",
"from sklearn.cross_validation import StratifiedKFold\n",
"n_folds = 5\n",
"skf = list(StratifiedKFold(y, n_folds))\n",
"\n",
"#创建零矩阵\n",
"dataset_blend_train = np.zeros((X.shape[0], len(clfs)))\n",
"dataset_blend_test = np.zeros((X_predict.shape[0], len(clfs)))\n",
"\n",
"#建立模型\n",
"for j, clf in enumerate(clfs):\n",
" '''依次训练各个单模型'''\n",
" # print(j, clf)\n",
" dataset_blend_test_j = np.zeros((X_predict.shape[0], len(skf)))\n",
" for i, (train, test) in enumerate(skf):\n",
" '''使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。'''\n",
" # print(\"Fold\", i)\n",
" X_train, y_train, X_test, y_test = X[train], y[train], X[test], y[test]\n",
" clf.fit(X_train, y_train)\n",
" y_submission = clf.predict_proba(X_test)[:, 1]\n",
" dataset_blend_train[test, j] = y_submission\n",
" dataset_blend_test_j[:, i] = clf.predict_proba(X_predict)[:, 1]\n",
" '''对于测试集，直接用这k个模型的预测值均值作为新的特征。'''\n",
" dataset_blend_test[:, j] = dataset_blend_test_j.mean(1)\n",
"\n",
"# 用建立第二层模型\n",
"clf2 = LogisticRegression(C=0.1,max_iter=100)\n",
"clf2.fit(dataset_blend_train, y)\n",
"y_submission = clf2.predict_proba(dataset_blend_test)[:, 1]\n",
"\n",
"\n",
"test = pd.read_csv(\"test.csv\")\n",
"test[\"Survived\"] = clf2.predict(dataset_blend_test)\n",
"test[['PassengerId','Survived']].set_index('PassengerId').to_csv('stack3.csv')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: docs/Kaggle/competitions/getting-started/titanic/titanic-data-science-solutions.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ea25cdf7-bdbc-3cf1-0737-bc51675e3374",
"_uuid": "9e170e99b8fc43e1d5ed5075e05397f73c10dcc7"
},
"source": [
"# 《泰坦尼克号》数据科学解决方案\n",
"\n",
"## 翻译相关\n",
"原文地址: \n",
"开源组织: [ApacheCN](http://www.apachecn.org) \n",
"贡献者: [@那伊抹微笑](https://github.com/wangyangting), [@李铭哲](https://github.com/limingzhe), [@刘海飞](https://github.com/WindZQ), [@王德红](https://github.com/VPrincekin) [@成飘飘](https://github.com/chengpiaopiao) \n",
"最近更新: 2018-01-03\n",
"\n",
"---\n",
"\n",
"### 我已经发布了一个新的 Python 包 [Speedml](https://speedml.com), 它将该 notebook 中的使用的技术编译成一个 intuitive（直观的），powerful（功能强大的）且 productive（高效的）API.\n",
"\n",
"### Speedml 帮助我在 Kaggle 排行榜上从最低的 80% 跳到最高的 20%, 迭代的次数很少.\n",
"\n",
"### 还有一件事...Speedml 实现了这一点, 代码行数减少了近 70%!\n",
"\n",
"### 下载并且运行代码 [Speedml 版本的泰坦尼克号解决方案](https://github.com/Speedml/notebooks/blob/master/titanic/titanic-solution-using-speedml.ipynb).\n",
"\n",
"---\n",
"\n",
"该 notebook 是 [Data Science Solutions](https://startupsci.com) 书籍的一个手册. 该 notebook 引导我们通过一个典型的工作流程来解决像 Kaggle 这样类似的网站的数据科学竞赛.\n",
"\n",
"有几个优秀的 notebooks 可以用来研究数据科学竞赛作品.\n",
"然而许多手册将会跳过一些关于如何开发解决方案的解释, 因为这些 notebooks 是专门为这些专家开发的.\n",
"该 notebook 的目标是遵循一步一步的工作流程, 解释我们在解决方案开发过程中所做的每一个决策的每个步骤和理由.\n",
"\n",
"## 工作流阶段\n",
"\n",
"1. 问题或问题的定义.\n",
"2. 获取 training（训练）和 testing（测试）数据.\n",
"3. Wrangle（整理）, prepare（准备）, cleanse（清洗）数据\n",
"4. Analyze（分析）, identify patterns 以及探索数据.\n",
"5. Model（模型）, predict（预测）以及解决问题.\n",
"6. Visualize（可视化）, report（报告）和提出解决问题的步骤以及最终解决方案.\n",
"7. 提供或提交结果.\n",
"\n",
"该工作流指出了，每个阶段如何遵循另一个阶段的常见顺序.\n",
"但是也有例外的场景.\n",
"\n",
"- 我们可能结合多个工作流阶段. 我们可以通过可视化数据进行分析.\n",
"- 比 indicated（说明）更早的进行一个阶段. 我们可能在 wrangling（整理）过程的前后来分析数据.\n",
"- 在我们的工作流程中多次执行一个阶段. 可视化阶段可能被使用多次.\n",
"- Drop a stage altogether. We may not need supply stage to productize or service enable our dataset for a competition.\n",
"\n",
"\n",
"## 问题和问题定义\n",
"\n",
"像 Kaggle 这样的竞赛网站, 它们会定义要解决或质疑的问题, 同时提供用于训练数据科学模型和根据测试数据集测试模型结果的数据集,（即, 训练集 和 测试集）.\n",
"针对《泰坦尼克号生存竞赛》的问题或定义在 [这里是 Kaggle 描述](https://www.kaggle.com/c/titanic) 中有描述.\n",
"\n",
"> 从泰坦尼克号的灾难中幸存下来或没有幸存的乘客的样本训练集（train.csv）中，如果测试数据集（test.csv）中的这些乘客幸存下来，我们的模型是否可以基于给定的测试数据集（test.csv）来确定。\n",
"\n",
"我们也可能希望对我们问题的领域有所了解.\n",
"这在 [Kaggle 竞赛描述](https://www.kaggle.com/c/titanic) 页面有详细的描述.\n",
"以下是要注意的事项.\n",
"\n",
"- 1912年4月15日, 在首航期间, 泰坦尼克号撞上一座冰山后沉没, 2224 名乘客和机组人员中有 1502 人遇难. 生成率解释为 32%.\n",
"- 还难导致生命损失的原因之一是没有足够的救生艇给乘客和船员.\n",
"- 尽管幸存下来的运气有一些因素, 但一些人比其他人更有可能幸存下来，比如妇女, 儿童和上层阶级.\n",
"\n",
"## 工作流目标\n",
"\n",
"数据科学解决方案工作流程有以下七个主要的目标.\n",
"\n",
"**Classifying（分类）.** 我们可能想对我们的样本进行分类或加以类别. 我们也可能想要了解不同类别与解决方案目标的含义或相关性.\n",
"\n",
"**Correlating（相关）.** 可以根据训练数据集中的可用特征来处理这个问题. 数据集中的哪些特征对我们的解决方案目标有重大贡献？从统计学上讲, 特征和解决方案的目标中有一个[相关](https://en.wikiversity.org/wiki/Correlation)？随着特征值的改变, 解决方案的状态也会随之改变, 反之亦然？这可以针对给定数据集中的数字和分类特征进行测试. 我们也可能想要确定以后的目标和工作流程阶段的生存以外的特征之间的相关性. 关联某些特征可能有助于创建, 完善或纠正特征。\n",
"\n",
"**Converting（转换）.** 对于建模阶段, 需要准备数据. 根据模型算法的选择, 可能需要将所有特征转换为数值等价值. 所以例如将文本分类值转换为数字的值.\n",
"\n",
"**Completing（完整）.** 数据准备也可能要求我们估计一个特征中的任何缺失值. 当没有缺失值时，模型算法可能效果最好.\n",
"\n",
"**Correcting（校正）.** 我们还可以分析给定的训练数据集以找出错误或者可能在特征内不准确的值, 并尝试对这些值进行校正或排除包含错误的样本. 一种方法是检测样本或特征中的任何异常值. 如果对分析没有贡献, 或者可能会显着扭曲结果, 我们也可能完全丢弃一个特征.\n",
"\n",
"**Creating（创建）.** 我们可以根据现有特征或一组特征来创建新特征, 以便新特征遵循 correlation（相关）, conversion（转换）, completeness（完整）的目标.\n",
"\n",
"**Charting（绘图）.** 如何根据数据的性质和解决方案的目标来选择正确的可视化图表工具以及绘图."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "56a3be4e-76ef-20c6-25e8-da16147cf6d7",
"_uuid": "9c7643f2494b7eb0db4e1a5a7696ff3e993352b8",
"collapsed": true
},
"source": [
"## 重构的发布日期 2017年1月29日\n",
"\n",
"We are significantly refactoring the notebook based on (a) comments received by readers, (b) issues in porting notebook from Jupyter kernel (2.7) to Kaggle kernel (3.5), and (c) review of few more best practice kernels.\n",
"\n",
"### 用户评论\n",
"\n",
"- Combine training and test data for certain operations like converting titles across dataset to numerical values. (thanks @Sharan Naribole)\n",
"- Correct observation - nearly 30% of the passengers had siblings and/or spouses aboard. (thanks @Reinhard)\n",
"- Correctly interpreting logistic regresssion coefficients. (thanks @Reinhard)\n",
"\n",
"### 移植问题\n",
"\n",
"- Specify plot dimensions, bring legend into plot.\n",
"\n",
"\n",
"### 最佳实践\n",
"\n",
"- 在项目早期进行特征相关分析.\n",
"- 为了可读性, 使用多个图而不是覆盖图."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "5767a33c-8f18-4034-e52d-bf7a8f7d8ab8",
"_uuid": "5ff730724498e5e39a020d13bebcceeb2128465b",
"collapsed": true
},
"outputs": [],
"source": [
"# 数据分析和整理\n",
"import pandas as pd\n",
"import numpy as np\n",
"import random as rnd\n",
"\n",
"# 可视化\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# 机器学习\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC, LinearSVC\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.linear_model import Perceptron\n",
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn.tree import DecisionTreeClassifier"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6b5dc743-15b1-aac6-405e-081def6ecca1",
"_uuid": "4060db2c31aae28179c44383a0870842045806e3"
},
"source": [
"## 获取数据\n",
"\n",
"Python 的 Pandas 包帮助我们处理我们的数据集.\n",
"我们首先将训练和测试数据集收集到 Pandas DataFrame 中.\n",
"我们还将这些数据集组合在一起, 在两个数据集上运行某些操作."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "e7319668-86fe-8adc-438d-0eef3fd0a982",
"_uuid": "394ed5fda2730c9819ab4b3271a197c1c6f63bca",
"collapsed": true
},
"outputs": [],
"source": [
"train_df = pd.read_csv('../input/train.csv')\n",
"test_df = pd.read_csv('../input/test.csv')\n",
"combine = [train_df, test_df]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "3d6188f3-dc82-8ae6-dabd-83e28fcbf10d",
"_uuid": "6319a800eb192dca6fed152fc63ae7127d3de4a8"
},
"source": [
"## 通过 describing（描述）数据进行分析\n",
"\n",
"在我们的项目早期, Pandas 还帮助描述回答数据集中的以下问题.\n",
"\n",
"**数据集中哪些特征是可用的?**\n",
"\n",
"注意: 直接操作或分析这些特征的名称.\n",
"这些特征名称在 [Kaggle 数据页面](https://www.kaggle.com/c/titanic/data) 页面上有描述."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "ce473d29-8d19-76b8-24a4-48c217286e42",
"_uuid": "6ca47bb664dde8eeb7fd6db2194ffbc58d7b4e9c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
" 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
]
}
],
"source": [
"print(train_df.columns.values)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "cd19a6f6-347f-be19-607b-dca950590b37",
"_uuid": "6d633da86183125117bd25785e185329444fa106"
},
"source": [
"**哪些特征是 categorical（分类的）?**\n",
"\n",
"这些值将样本分成几组相似的样本.\n",
"在分类特征中的值是 nominal（标称的）, ordinal（顺序的）或 ratio（比例的）还是 interval based（基于区间的）值？\n",
"除此之外, 这有助于我们选择合适的图表进行可视化.\n",
"\n",
"- Categorical（分类的）: Survived, Sex, and Embarked. Ordinal（顺序的）: Pclass.\n",
"\n",
"**哪些特征是 numerical（数值的）?**\n",
"\n",
"哪些特征是数值的？\n",
"这些值随样本而变化.\n",
"在数值特征中的值是 discrete（离散的）和 continuous（连续的） 还是 timeseries based（基于时间序列的）？\n",
"\n",
"- Continous（连续的）: Age, Fare. Discrete（离散的）: SibSp, Parch."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "8d7ac195-ac1a-30a4-3f3f-80b8cf2c1c0f",
"_uuid": "7ddf8763ea0486359b22711ffada0f7b1201a7da",
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" PassengerId \n",
" Survived \n",
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" Age \n",
" SibSp \n",
" Parch \n",
" Ticket \n",
" Fare \n",
" Cabin \n",
" Embarked \n",
" \n",
" \n",
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" 38.0 \n",
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" 71.2833 \n",
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" \n",
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" female \n",
" 26.0 \n",
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" 7.9250 \n",
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" NaN \n",
" S \n",
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\n",
""
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 预览数据\n",
"train_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "97f4e6f8-2fea-46c4-e4e8-b69062ee3d46",
"_uuid": "1648e560e93ea947298439d01598524155bbe743"
},
"source": [
"**哪些特征是混合的数据类型?**\n",
"\n",
"相同特征中的 numerical（数值的）, alphanumeric（字母数值的）.\n",
"这些是校正目标的候选特征.\n",
"\n",
"- Ticket 是numerical（数值的）和 alphanumeric（字母数值的）数据类型的混合类型. Cabin 是 alphanumeric（字母数值的）.\n",
"\n",
"**哪些特征也许包含错误或拼写错误?**\n",
"\n",
"对于一个大型的数据集来说, 这是很难审查的, 但是从较小的数据集中查看一些样本可能会直接告诉我们, 哪些特征可能需要校正.\n",
"\n",
"- Name 特征也许包含错误或拼写错误, 因为有几种方法可以用来描述名称, 包括头衔，圆括号和用于替代或短名称的引号."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "f6e761c2-e2ff-d300-164c-af257083bb46",
"_uuid": "030837af7736facdcc436275e13576130c10f9a7"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"\n",
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" PassengerId \n",
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" Age \n",
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\n",
""
],
"text/plain": [
" PassengerId Survived Pclass Name \\\n",
"886 887 0 2 Montvila, Rev. Juozas \n",
"887 888 1 1 Graham, Miss. Margaret Edith \n",
"888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n",
"889 890 1 1 Behr, Mr. Karl Howell \n",
"890 891 0 3 Dooley, Mr. Patrick \n",
"\n",
" Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
"886 male 27.0 0 0 211536 13.00 NaN S \n",
"887 female 19.0 0 0 112053 30.00 B42 S \n",
"888 female NaN 1 2 W./C. 6607 23.45 NaN S \n",
"889 male 26.0 0 0 111369 30.00 C148 C \n",
"890 male 32.0 0 0 370376 7.75 NaN Q "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "8bfe9610-689a-29b2-26ee-f67cd4719079",
"_uuid": "2980c677698d5b219f3eca1faa748d4ef1c06b3f"
},
"source": [
"**哪些特征包含 blank（空格）, null（无效的）或 empty values（空值）?**\n",
"\n",
"这些将需要校正.\n",
"\n",
"- Cabin > Age > Embarked features contain a number of null values in that order for the training dataset.\n",
"- Cabin > Age are incomplete in case of test dataset.\n",
"\n",
"**各个特征的数据类型是什么样的?**\n",
"\n",
"在转换的目标时可以帮助我们.\n",
"\n",
"- 7 个特征是 integer 或 floats. 6 个在测试数据集中.\n",
"- 5 个特征是 strings (object)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "9b805f69-665a-2b2e-f31d-50d87d52865d",
"_uuid": "0e026227df676ac169811999565a86288fa129b1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"PassengerId 891 non-null int64\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 714 non-null float64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Cabin 204 non-null object\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 83.6+ KB\n",
"________________________________________\n",
"\n",
"RangeIndex: 418 entries, 0 to 417\n",
"Data columns (total 11 columns):\n",
"PassengerId 418 non-null int64\n",
"Pclass 418 non-null int64\n",
"Name 418 non-null object\n",
"Sex 418 non-null object\n",
"Age 332 non-null float64\n",
"SibSp 418 non-null int64\n",
"Parch 418 non-null int64\n",
"Ticket 418 non-null object\n",
"Fare 417 non-null float64\n",
"Cabin 91 non-null object\n",
"Embarked 418 non-null object\n",
"dtypes: float64(2), int64(4), object(5)\n",
"memory usage: 36.0+ KB\n"
]
}
],
"source": [
"train_df.info()\n",
"print('_'*40)\n",
"test_df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "859102e1-10df-d451-2649-2d4571e5f082",
"_uuid": "241d3667c3850d6d5ed83ffab6991f185a44cdec"
},
"source": [
"**样本中数值特征值的分布是什么?**\n",
"\n",
"这有助于我们确定, 除了其他早期的思考, 在实际问题领域的训练数据集是如何具有代表性的.\n",
"\n",
"- 总样本是 891 或者在泰坦尼克号（2,224）上实际旅客的 40%.\n",
"- Survived（生存）是一个具有 0 或 1 值的分类特征.\n",
"- 大约 38% 样本幸存了下来, 然而实际的幸存率是 32%.\n",
"- 大多数旅客 (> 75%) 没有和父母或孩子一起旅行.\n",
"- 近 30% 的旅客有兄弟姐妹 和/或 配偶.\n",
"- 少数旅客 Fares（票价）差异显著 (<1%), 最高达 $512.\n",
"- 很少有年长的旅客 (<1%) 在年龄范围 65-80."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "58e387fe-86e4-e068-8307-70e37fe3f37b",
"_uuid": "c4bd85c7847593602a09d809337af4616d8c9e02"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" PassengerId \n",
" Survived \n",
" Pclass \n",
" Age \n",
" SibSp \n",
" Parch \n",
" Fare \n",
" \n",
" \n",
" \n",
" \n",
" count \n",
" 891.000000 \n",
" 891.000000 \n",
" 891.000000 \n",
" 714.000000 \n",
" 891.000000 \n",
" 891.000000 \n",
" 891.000000 \n",
" \n",
" \n",
" mean \n",
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" 29.699118 \n",
" 0.523008 \n",
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" \n",
" \n",
" std \n",
" 257.353842 \n",
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" \n",
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""
],
"text/plain": [
" PassengerId Survived Pclass Age SibSp \\\n",
"count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
"std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
"min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
"25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n",
"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
"75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n",
"max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
"\n",
" Parch Fare \n",
"count 891.000000 891.000000 \n",
"mean 0.381594 32.204208 \n",
"std 0.806057 49.693429 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 7.910400 \n",
"50% 0.000000 14.454200 \n",
"75% 0.000000 31.000000 \n",
"max 6.000000 512.329200 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.describe()\n",
"# Review survived rate using `percentiles=[.61, .62]` knowing our problem description mentions 38% survival rate.\n",
"# Review Parch distribution using `percentiles=[.75, .8]`\n",
"# SibSp distribution `[.68, .69]`\n",
"# Age and Fare `[.1, .2, .3, .4, .5, .6, .7, .8, .9, .99]`"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "5462bc60-258c-76bf-0a73-9adc00a2f493",
"_uuid": "93ac7caf79ef8a43bf2f88e1b15e70b9521fea04"
},
"source": [
"**分类特征的分布是什么样的?**\n",
"\n",
"- Names（名称）特征在数据集中是唯一的 (count=unique=891)\n",
"- Sex（性别）变量有两个可能的值, 男性为 65% (top=male, freq=577/count=891).\n",
"- Cabin（房间号）值在样本中有重复. 或者几个旅客共享一个客舱.\n",
"- Embarked（出发港）有 3 个可能的值. 大多数乘客使用 S 港口(top=S)\n",
"- Ticket（船票号码）特征有很高 (22%) 的重复值 (unique=681)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "8066b378-1964-92e8-1352-dcac934c6af3",
"_uuid": "eccc6479282828bd97b4d1f391ff09eded517ce1"
},
"outputs": [
{
"data": {
"text/html": [
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" Name \n",
" Sex \n",
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" \n",
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"freq 1 577 7 4 644"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.describe(include=['O'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2cb22b88-937d-6f14-8b06-ea3361357889",
"_uuid": "e7b4b885268793a3f7f818a28e86aa97bdc84a69"
},
"source": [
"### 基于数据分析的假设\n",
"\n",
"到目前为止, 基于数据分析, 我们得出以下假设.\n",
"在采取适当的行动之前, 我们可能会进一步验证这些假设.\n",
"\n",
"**Correlating（相关）.**\n",
"\n",
"我们想知道每个特征与生存相关的程度.\n",
"我们希望在项目早期做到这一点, 并将这些快速相关性与项目后期的模型相关性相匹配.\n",
"\n",
"**Completing（完整）.**\n",
"\n",
"1. 我们可能想要去补全丢失的 Age（年龄）特征，因为它肯定与生存相关.\n",
"2. 我们也想要去补全丢失的 Embarked（出发港）特征, 因为它也可能与生存或者其它重要的特征相关联.\n",
"\n",
"**Correcting（校正）.**\n",
"\n",
"1. Ticket（船票号码）特征可能会从我们的分析中删除, 因为它包含了很高的重复比例 (22%), 并且票号和生存之间可能没有关联.\n",
"2. Cabin（房间号）特征可能因为高度不完整而丢失, 或者在 训练和测试数据集中都包含许多 null 值.\n",
"3. PassengerId（旅客ID）可能会从训练数据集中删除, 因为它对生存来说没有贡献.\n",
"4. Name（名称）特征是比较不规范的, 可能不直接影响生产, 所以也许会删除.\n",
"\n",
"**Creating（创建）.**\n",
"\n",
"1. 我们可能希望创建一个名为 Family 的基于 Parch 和 SibSp 的新特征，以获取船上家庭成员的总数.\n",
"2. 我们可能想要设计 Name 功能以将 Title 抽取为新特征.\n",
"3. 我们可能要为 Age（年龄）段创建新的特征. 这将一个连续的数字特征转变为一个顺序的分类特征.\n",
"4. 如果它有助于我们的分析, 我们也可能想要创建 Fare（票价）范围的特征。\n",
"\n",
"**Classifying（分类）.**\n",
"\n",
"根据前面提到的问题描述, 我们也可以增加我们的假设.\n",
"\n",
"1. Women (Sex=female) 更有可能幸存下来.\n",
"2. Children (Age 0.5）. 我们决定在我们的模型中包含这个特征.\n",
"- **Sex** 在 Sex=female（性别=女性）的问题定义中确认了74％（分类＃1）的幸存率非常高的观察意见.\n",
"- **SibSp and Parch** 这些特征对于某些值具有零相关性. 从这些单独的特征（创建＃1）派生一个特征或一组特征可能是最好的"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "0964832a-a4be-2d6f-a89e-63526389cee9",
"_uuid": "62fb816bd0d5612f6b12cce8ed46036acb75527b"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"\n",
" \n",
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" \n",
" 0 \n",
" 1 \n",
" 0.629630 \n",
" \n",
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"text/plain": [
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"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "68908ba6-bfe9-5b31-cfde-6987fc0fbe9a",
"_uuid": "2cfacba29530bf3e52fedf89c5bdd12eb9a924c3"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Sex \n",
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" \n",
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"text/plain": [
" Sex Survived\n",
"0 female 0.742038\n",
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"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "01c06927-c5a6-342a-5aa8-2e486ec3fd7c",
"_uuid": "c0f9f9a3d89294ba3073f040d12a9625d26af1f1"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" SibSp \n",
" Survived \n",
" \n",
" \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 0.535885 \n",
" \n",
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"4 4 0.166667\n",
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"6 8 0.000000"
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},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "e686f98b-a8c9-68f8-36a4-d4598638bbd5",
"_uuid": "3c6193e348b7616bfa8624ca9562a7ad5749906d"
},
"outputs": [
{
"data": {
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" \n",
" 3 \n",
" 3 \n",
" 0.600000 \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 0.550847 \n",
" \n",
" \n",
" 2 \n",
" 2 \n",
" 0.500000 \n",
" \n",
" \n",
" 0 \n",
" 0 \n",
" 0.343658 \n",
" \n",
" \n",
" 5 \n",
" 5 \n",
" 0.200000 \n",
" \n",
" \n",
" 4 \n",
" 4 \n",
" 0.000000 \n",
" \n",
" \n",
" 6 \n",
" 6 \n",
" 0.000000 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Parch Survived\n",
"3 3 0.600000\n",
"1 1 0.550847\n",
"2 2 0.500000\n",
"0 0 0.343658\n",
"5 5 0.200000\n",
"4 4 0.000000\n",
"6 6 0.000000"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "0d43550e-9eff-3859-3568-8856570eff76",
"_uuid": "0a396ef5d708292e88f66829bd4d3f3d48465e36"
},
"source": [
"## 通过可视化数据进行分析\n",
"\n",
"现在我们可以继续使用可视化分析数据来确认我们的一些假设.\n",
"\n",
"### 关联数值的特征\n",
"\n",
"让我们从理解数值的特征和解决方案目标（生存）之间的相关性开始.\n",
"\n",
"柱状图可用于分析连续的数字变量，如 Age（年龄），其中条带或范围将有助于识别有用的模式.\n",
"直方图可以使用自动定义的 bins 或等分范围的 bins 来说明样本的分布.\n",
"这有助于我们回答有关特定频段的问题（婴儿有更好的幸存率吗？）\n",
"\n",
"请注意，直方图可视化中的 x 轴表示样本或旅客的数量.\n",
"\n",
"**Observations（观察）.**\n",
"\n",
"- 婴儿（4 岁以下）存活率高.\n",
"- 最老的乘客（年龄= 80）幸存下来.\n",
"- 大量的 15-25 岁的孩子没有幸.\n",
"- 大多数乘客在 15-35 年龄范围内.\n",
"\n",
"**Decisions（决策）.**\n",
"\n",
"这个简单的分析证实了我们的假设, 作为后续工作流程阶段的决策.\n",
"\n",
"- 在我们的模型训练中, 我们应该考虑年龄（我们假设分类＃2）.\n",
"- 完成空值的年龄功能（完成＃1）.\n",
"- 我们应该 band（组合）年龄组（创建＃3）."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "50294eac-263a-af78-cb7e-3778eb9ad41f",
"_uuid": "5e581f2b5be92a31c510f7db4bfceb3c9727c473"
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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V6DAv24B3VtWLgROAt3UxrQKuq6ojgOu65VE6F7hrh+ULgA918T0CnDOSqJ5yIb0v5L0I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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.FacetGrid(train_df, col='Survived')\n",
"g.map(plt.hist, 'Age', bins=20)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "87096158-4017-9213-7225-a19aea67a800",
"_uuid": "f3b60c4ce160be82ebd9ad8ff14fcf2578ff2b03"
},
"source": [
"### 关联数字和顺序的特征\n",
"\n",
"我们可以结合多个特征使用一个图来确定其相关性.\n",
"这可以通过具有数字值的数字和分类特征来完成。\n",
"\n",
"**Observations（观察）.**\n",
"\n",
"- Pclass=3 拥有最多的乘客，但大多数没有生存. 确认我们的分类假设 ＃2.\n",
"- Pclass=2 和 Pclass = 3 的婴儿乘客大多存活. 进一步限定了我们的分类假设 ＃2.\n",
"- Pclass=1 的大多数乘客幸存下来。 确认我们的分类假设 ＃3。\n",
"- Pclass 在乘客的年龄分布方面有所不同.\n",
"\n",
"**Decisions（决策）.**\n",
"\n",
"- 考虑 Pclass 用于模型训练."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "916fdc6b-0190-9267-1ea9-907a3d87330d",
"_uuid": "c1c736a54c925c1d1db4133b30ebe144aabc295f"
},
"outputs": [
{
"data": {
"image/png": 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A7kmyAqC93bqzBavqnKpaVVWrpqamFvjnJA2ZOS9NmIWOAFwKnAy8r729pG8R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\nAkCSpA6yAJAkqYMsACRJ6iA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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\n",
"grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\n",
"grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
"grid.add_legend();"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "36f5a7c0-c55c-f76f-fdf8-945a32a68cb0",
"_uuid": "5cdb1388a846ad8b19a2c649da034f4f45cae31b"
},
"source": [
"### 关联分类特征\n",
"\n",
"现在我们可以将分类特征与我们的解决方案目标关联起来.\n",
"\n",
"**Observations（观察）.**\n",
"\n",
"- Female（女性）旅客的幸存率比 male（男性）好得多. 确认分类（＃1）。\n",
"- Embarked= C 的例外, 其中男性的成活率较高. 这可能是 Pclass 和 Embarked 之间的相关性, 反过来, Pclass 和 Survived 之间, 不一定是Embarked和 Survived之间的相关性。\n",
"- 与 C 和 Q 港口的 Pclass = 2 相比, Pclass = 3 时男性的生存率更高. 完成（＃2）。\n",
"- 出发港口的 Pclass=3 和男性乘客的生存率不同. 相关（＃1）。\n",
"\n",
"**Decisions（决策）.**\n",
"\n",
"- 增加 Sex 特征以用于模型训练.\n",
"- 补全丢失值并添加 Embarked 特征以用于模型训练."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "db57aabd-0e26-9ff9-9ebd-56d401cdf6e8",
"_uuid": "cb86b6c046ba3b91dfc8811b08caefd515b7f1bb",
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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xUcGSnTEmKrSba3Yikg/simAIXYFDEdx/JNl3j5xDqjo9gvtvM9pNsos0EclW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\nzezy/+6dmW/17NxWigeBBTgl2jcDN4UX4RSRzwHTVPUOEbk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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# grid = sns.FacetGrid(train_df, col='Embarked')\n",
"grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\n",
"grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
"grid.add_legend()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6b3f73f4-4600-c1ce-34e0-bd7d9eeb074a",
"_uuid": "6e031e6217b278363f1bd659b2e591bcb30562bd"
},
"source": [
"### 关联分类和数值的特征\n",
"\n",
"我们也可能想要关联分类特征（非数值的）和数值的特征.\n",
"我们可以考虑将 Embarked（类别非数字）, Sex（类别非数字）, Fare（数字连续）与生存（分类数字）相关联.\n",
"\n",
"**Observations（观察）.**\n",
"\n",
"- Higher fare paying passengers had better survival. Confirms our assumption for creating (#4) fare ranges.\n",
"- Port of embarkation correlates with survival rates. Confirms correlating (#1) and completing (#2).\n",
"\n",
"- 更高的票价付费旅客有更好的生存. 证实我们对创造（＃4）票价范围的假设.\n",
"- 搭乘港口与生存率相关. 确认关联（＃1）和完成（＃2）.\n",
"\n",
"**Decisions（决策）.**\n",
"\n",
"- 考虑关联Fare(票价)特征"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "a21f66ac-c30d-f429-cc64-1da5460d16a9",
"_uuid": "af9099be9d7fe9f358ccdcf704991abde0880409"
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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OTHIccCfwmhZjkDRc5rU0JlorAKrqUmB9JwYPbGu7ktpjXkvjwzsBSpLUQRYAkiR1kAWA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\nTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\n",
"grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
"grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
"grid.add_legend()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "cfac6291-33cc-506e-e548-6cad9408623d",
"_uuid": "53c762e996de3aefbe4e7e70671a43930189d9b9"
},
"source": [
"## 整理数据\n",
"\n",
"我们收集了关于我们的数据集和解决方案要求的一些假设和决策.\n",
"到目前为止, 我们没有必要改变一个单个的特征或值来达到目标.\n",
"让我们现在执行我们的决定和假设来 correcting(校正), creating（创建）和 completing（完整）目标.\n",
"\n",
"### 通过删除特征进行校正\n",
"\n",
"这是一个很好的开始执行目标. 通过丢弃特征, 我们正在处理更少的数据点. 加快我们的 notebook, 并简化分析.\n",
"\n",
"根据我们的假设和决策, 我们要放弃 Cabin（房间号）（更正＃2）和 Ticket（票号）（更正＃1）的特征.\n",
"\n",
"请注意, 如果适用, 我们将对训练和测试数据集进行操作, 以保持一致."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "da057efe-88f0-bf49-917b-bb2fec418ed9",
"_uuid": "6c7b899f23e0c2cb0b2b05447eece4f0aab769f5",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before (891, 12) (418, 11) (891, 12) (418, 11)\n"
]
},
{
"data": {
"text/plain": [
"('After', (891, 10), (418, 9), (891, 10), (418, 9))"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Before\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\n",
"\n",
"train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
"test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
"combine = [train_df, test_df]\n",
"\n",
"\"After\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6b3a1216-64b6-7fe2-50bc-e89cc964a41c",
"_uuid": "762ea4993c2d5c7b9667521badc4bb0802f35a24"
},
"source": [
"### 从现在的提取以创建性特征\n",
"\n",
"我们想要分析一下, Name 特征是否可以被设计来提取 titles（头衔）和测试titles与survival（生存）之间的相关性, 然后再删除Name 和 PassengerId 特征.\n",
"\n",
"在下面的代码中, 我们使用正则表达式提取 Title 特征. 正则表达式`(\\w+\\.)`匹配 Name 特征中以点号字符结尾的第一个单词.\n",
"`expand = False` 标志返回一个 DataFrame.\n",
"\n",
"**Observations（观察）.**\n",
"\n",
"当我们绘制出 Title, Age 和 Survived 的图时, 我们可以发现以下观察.\n",
"\n",
"- 大多数 titles band 年龄组准确. 例如: Master Title的Age平均为5岁。\n",
"- Title 中的生存年龄段略有不同.\n",
"- 某些 Title 大多存活(Mme, Lady, Sir)或某些Title没有存活(Don, Rev, Jonkheer)\n",
"\n",
"**Decision（决策）.**\n",
"\n",
"- 我们决定保留模型训练的新 Title 特征."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "df7f0cd4-992c-4a79-fb19-bf6f0c024d4b",
"_uuid": "6e1e16b53b683ba5c4e0b7f22ff97209d12f8e0c"
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"outputs": [
{
"data": {
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"Major 0 2\n",
"Master 0 40\n",
"Miss 182 0\n",
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"Mme 1 0\n",
"Mr 0 517\n",
"Mrs 125 0\n",
"Ms 1 0\n",
"Rev 0 6\n",
"Sir 0 1"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for dataset in combine:\n",
" dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
"\n",
"pd.crosstab(train_df['Title'], train_df['Sex'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "908c08a6-3395-19a5-0cd7-13341054012a",
"_uuid": "a780a39e03cc679742db03ad2531cff65492cc20"
},
"source": [
"我们可以用更常见的Title来替换很多Title, 或者将它们分类为 `Rare`(稀有)."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "553f56d7-002a-ee63-21a4-c0efad10cfe9",
"_uuid": "a77aab132073d20a9050d63a57aab0b77931cf98",
"scrolled": true
},
"outputs": [
{
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"\n",
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" Title \n",
" Survived \n",
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" Master \n",
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" Miss \n",
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\n",
""
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"text/plain": [
" Title Survived\n",
"0 Master 0.575000\n",
"1 Miss 0.702703\n",
"2 Mr 0.156673\n",
"3 Mrs 0.793651\n",
"4 Rare 0.347826"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for dataset in combine:\n",
" dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
" \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
"\n",
" dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
" dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
" dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
" \n",
"train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6d46be9a-812a-f334-73b9-56ed912c9eca",
"_uuid": "570174039502747cfc590e3a23b2b2407649c9df"
},
"source": [
"我们可以将 titles（头衔）转换为顺序的."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "67444ebc-4d11-bac1-74a6-059133b6e2e8",
"_uuid": "12d48fc3ce08e6daff750ecdf741852f7ada87fd"
},
"outputs": [
{
"data": {
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" PassengerId \n",
" Survived \n",
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" Age \n",
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" Fare \n",
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"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
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"\n",
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"4 0 8.0500 S 1 "
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},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
"for dataset in combine:\n",
" dataset['Title'] = dataset['Title'].map(title_mapping)\n",
" dataset['Title'] = dataset['Title'].fillna(0)\n",
"\n",
"train_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f27bb974-a3d7-07a1-f7e4-876f6da87e62",
"_uuid": "ebbb6df8eeb4898e25cf2e375a5d5540906ba554"
},
"source": [
"现在我们可以放心地从训练和测试数据集中删除 Name 特征.\n",
"我们也不需要训练数据集中的 PassengerId 特征."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "9d61dded-5ff0-5018-7580-aecb4ea17506",
"_uuid": "3699ea6473ef7e16779b28b319ccff0da2615e2f"
},
"outputs": [
{
"data": {
"text/plain": [
"((891, 9), (418, 9))"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
"test_df = test_df.drop(['Name'], axis=1)\n",
"combine = [train_df, test_df]\n",
"train_df.shape, test_df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2c8e84bb-196d-bd4a-4df9-f5213561b5d3",
"_uuid": "d55d78bc8cab25fa58194dbc46610e2395d1d3e0"
},
"source": [
"### 转换分类的特征\n",
"\n",
"现在我们可以将包含字符串的特征转换为数字值.\n",
"这是大多数模型算法所要求的.\n",
"这样做也将帮助我们实现特征完成目标.\n",
"让我们开始将 Sex（性别）特征转换为名为 Gender（性别）的新特征, 其中 female=1, male=0."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "c20c1df2-157c-e5a0-3e24-15a828095c96",
"_uuid": "f61f46627bdef1b318c019c852f2e370f6bfd9e0"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"\n",
" \n",
" \n",
" Survived \n",
" Pclass \n",
" Sex \n",
" Age \n",
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" Parch \n",
" Fare \n",
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"text/plain": [
" Survived Pclass Sex Age SibSp Parch Fare Embarked Title\n",
"0 0 3 0 22.0 1 0 7.2500 S 1\n",
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"2 1 3 1 26.0 0 0 7.9250 S 2\n",
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"4 0 3 0 35.0 0 0 8.0500 S 1"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for dataset in combine:\n",
" dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
"\n",
"train_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d72cb29e-5034-1597-b459-83a9640d3d3a",
"_uuid": "2799e6b10611997bdeca13d86fcacced318853c9"
},
"source": [
"### 完整化数值字连续特征\n",
"\n",
"现在我们应该开始估计和完成缺少或空值的特征.\n",
"我们将首先为 Age（年龄）特征执行此操作.\n",
"\n",
"我们可以考虑三种方法来完整化一个数值连续的特征.\n",
"\n",
"1.简单的方法是在平均值和 [标准偏差](https://en.wikipedia.org/wiki/Standard_deviation) 之间生成随机数.\n",
"\n",
"2.更准确地猜测缺失值的方法是使用其他相关特征. 在我们的例子中, 我们注意到 Age（年龄）, Sex（性别）和 Pclass 之间的相关性.猜测年龄值，使用Pclass和Gender特征组合中Age的[中位数](https://en.wikipedia.org/wiki/Median). 因此, Pclass=1 和 Gender=0，Pclass=1 和 Gender=1 的年龄中位数等等...\n",
"\n",
"3.结合方法 1 和 2. 因此. 不要根据中位数来猜测年龄值, 而应根据 Pclass 和 Sex 组合, 使用平均数和标准差之间的随机数.\n",
"\n",
"方法 1 和 3 将在我们的模型中引入随机噪声. 多次执行的结果可能会有所不同. 我们更喜欢方法 2."
]
},
{
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"execution_count": 22,
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"outputs": [
{
"data": {
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""
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},
"execution_count": 22,
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{
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4sLHbJ58B9p22/QG8B/g6cDPwceCh07gvFhijOT8Bk/k+GdM4ct47AUqS1KBJeQtAkiSN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\nNej/B+1qWsqKF3O5AAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# grid = sns.FacetGrid(train_df, col='Pclass', hue='Gender')\n",
"grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\n",
"grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
"grid.add_legend()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a4f166f9-f5f9-1819-66c3-d89dd5b0d8ff",
"_uuid": "d3ef5e2578429c297e6f7eefa7c769a7cd59c564"
},
"source": [
"让我们开始准备一个空数组, 以包含基于 Pclass x Gender 组合以猜测 Age 值."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "9299523c-dcf1-fb00-e52f-e2fb860a3920",
"_uuid": "f28a31b2520fa6cb7684868464f96c8ed4f5897b"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0.],\n",
" [ 0., 0., 0.]])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"guess_ages = np.zeros((2,3))\n",
"guess_ages"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ec9fed37-16b1-5518-4fa8-0a7f579dbc82",
"_uuid": "4e6dab5115d513ce035864f6693a87c26897e47b"
},
"source": [
"现在我们迭代 Sex（0 或 1）和 Pclass（1, 2, 3）来计算 6 个组合的 Age 的猜测值."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"_cell_guid": "a4015dfa-a0ab-65bc-0cbe-efecf1eb2569",
"_uuid": "30fe7bd956f87da84afb5bc9bed792cc6dd0831f"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Survived \n",
" Pclass \n",
" Sex \n",
" Age \n",
" SibSp \n",
" Parch \n",
" Fare \n",
" Embarked \n",
" Title \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0 \n",
" 3 \n",
" 0 \n",
" 22 \n",
" 1 \n",
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" 7.2500 \n",
" S \n",
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" 1 \n",
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" 1 \n",
" 38 \n",
" 1 \n",
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" 71.2833 \n",
" C \n",
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" \n",
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" 1 \n",
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" 26 \n",
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" 1 \n",
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" 53.1000 \n",
" S \n",
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" 0 \n",
" 8.0500 \n",
" S \n",
" 1 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Survived Pclass Sex Age SibSp Parch Fare Embarked Title\n",
"0 0 3 0 22 1 0 7.2500 S 1\n",
"1 1 1 1 38 1 0 71.2833 C 3\n",
"2 1 3 1 26 0 0 7.9250 S 2\n",
"3 1 1 1 35 1 0 53.1000 S 3\n",
"4 0 3 0 35 0 0 8.0500 S 1"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for dataset in combine:\n",
" for i in range(0, 2):\n",
" for j in range(0, 3):\n",
" guess_df = dataset[(dataset['Sex'] == i) & \\\n",
" (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
"\n",
" # age_mean = guess_df.mean()\n",
" # age_std = guess_df.std()\n",
" # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n",
"\n",
" age_guess = guess_df.median()\n",
"\n",
" # Convert random age float to nearest .5 age\n",
" guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
" \n",
" for i in range(0, 2):\n",
" for j in range(0, 3):\n",
" dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n",
" 'Age'] = guess_ages[i,j]\n",
"\n",
" dataset['Age'] = dataset['Age'].astype(int)\n",
"\n",
"train_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "dbe0a8bf-40bc-c581-e10e-76f07b3b71d4",
"_uuid": "ed080ed0742604812ae933aa2dcd9ffe0f752abf"
},
"source": [
"让我们创建年龄段并确定与 Survived 的相关性."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"_cell_guid": "725d1c84-6323-9d70-5812-baf9994d3aa1",
"_uuid": "572a2e9a8a268159849d89ed7ec5902a305b0d14"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" AgeBand \n",
" Survived \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" (-0.08, 16.0] \n",
" 0.550000 \n",
" \n",
" \n",
" 1 \n",
" (16.0, 32.0] \n",
" 0.337374 \n",
" \n",
" \n",
" 2 \n",
" (32.0, 48.0] \n",
" 0.412037 \n",
" \n",
" \n",
" 3 \n",
" (48.0, 64.0] \n",
" 0.434783 \n",
" \n",
" \n",
" 4 \n",
" (64.0, 80.0] \n",
" 0.090909 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" AgeBand Survived\n",
"0 (-0.08, 16.0] 0.550000\n",
"1 (16.0, 32.0] 0.337374\n",
"2 (32.0, 48.0] 0.412037\n",
"3 (48.0, 64.0] 0.434783\n",
"4 (64.0, 80.0] 0.090909"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
"train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ba4be3a0-e524-9c57-fbec-c8ecc5cde5c6",
"_uuid": "ef3552778390d811d6105a2dd0c2b81e77407283"
},
"source": [
"让我们使用年龄段的顺序值来替换 Aage."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"_cell_guid": "797b986d-2c45-a9ee-e5b5-088de817c8b2",
"_uuid": "f7661c3d74c059e1d3664fb502690cb9bfd27339"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Survived \n",
" Pclass \n",
" Sex \n",
" Age \n",
" SibSp \n",
" Parch \n",
" Fare \n",
" Embarked \n",
" Title \n",
" AgeBand \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0 \n",
" 3 \n",
" 0 \n",
" 1 \n",
" 1 \n",
" 0 \n",
" 7.2500 \n",
" S \n",
" 1 \n",
" (16.0, 32.0] \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 2 \n",
" 1 \n",
" 0 \n",
" 71.2833 \n",
" C \n",
" 3 \n",
" (32.0, 48.0] \n",
" \n",
" \n",
" 2 \n",
" 1 \n",
" 3 \n",
" 1 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 7.9250 \n",
" S \n",
" 2 \n",
" (16.0, 32.0] \n",
" \n",
" \n",
" 3 \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 2 \n",
" 1 \n",
" 0 \n",
" 53.1000 \n",
" S \n",
" 3 \n",
" (32.0, 48.0] \n",
" \n",
" \n",
" 4 \n",
" 0 \n",
" 3 \n",
" 0 \n",
" 2 \n",
" 0 \n",
" 0 \n",
" 8.0500 \n",
" S \n",
" 1 \n",
" (32.0, 48.0] \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Survived Pclass Sex Age SibSp Parch Fare Embarked Title \\\n",
"0 0 3 0 1 1 0 7.2500 S 1 \n",
"1 1 1 1 2 1 0 71.2833 C 3 \n",
"2 1 3 1 1 0 0 7.9250 S 2 \n",
"3 1 1 1 2 1 0 53.1000 S 3 \n",
"4 0 3 0 2 0 0 8.0500 S 1 \n",
"\n",
" AgeBand \n",
"0 (16.0, 32.0] \n",
"1 (32.0, 48.0] \n",
"2 (16.0, 32.0] \n",
"3 (32.0, 48.0] \n",
"4 (32.0, 48.0] "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for dataset in combine: \n",
" dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n",
" dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
" dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
" dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
" dataset.loc[ dataset['Age'] > 64, 'Age']\n",
"train_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "004568b6-dd9a-ff89-43d5-13d4e9370b1d",
"_uuid": "76893d49eff6e15e05569448e030358486870409"
},
"source": [
"我们删除 AgeBand 特征."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"_cell_guid": "875e55d4-51b0-5061-b72c-8a23946133a3",
"_uuid": "0726fd8a5151812ae337d9ee80bd4fd1c08e592a"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Survived \n",
" Pclass \n",
" Sex \n",
" Age \n",
" SibSp \n",
" Parch \n",
" Fare \n",
" Embarked \n",
" Title \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0 \n",
" 3 \n",
" 0 \n",
" 1 \n",
" 1 \n",
" 0 \n",
" 7.2500 \n",
" S \n",
" 1 \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 2 \n",
" 1 \n",
" 0 \n",
" 71.2833 \n",
" C \n",
" 3 \n",
" \n",
" \n",
" 2 \n",
" 1 \n",
" 3 \n",
" 1 \n",
" 1 \n",
" 0 \n",
" 0 \n",
" 7.9250 \n",
" S \n",
" 2 \n",
" \n",
" \n",
" 3 \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 2 \n",
" 1 \n",
" 0 \n",
" 53.1000 \n",
" S \n",
" 3 \n",
" \n",
" \n",
" 4 \n",
" 0 \n",
" 3 \n",
" 0 \n",
" 2 \n",
" 0 \n",
" 0 \n",
" 8.0500 \n",
" S \n",
" 1 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Survived Pclass Sex Age SibSp Parch Fare Embarked Title\n",
"0 0 3 0 1 1 0 7.2500 S 1\n",
"1 1 1 1 2 1 0 71.2833 C 3\n",
"2 1 3 1 1 0 0 7.9250 S 2\n",
"3 1 1 1 2 1 0 53.1000 S 3\n",
"4 0 3 0 2 0 0 8.0500 S 1"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df = train_df.drop(['AgeBand'], axis=1)\n",
"combine = [train_df, test_df]\n",
"train_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1c237b76-d7ac-098f-0156-480a838a64a9",
"_uuid": "9c0f835b1ce116035e125c9f33b6b529bafe5f77"
},
"source": [
"### 结合现有特征创建新特征\n",
"\n",
"我们可以为 Parch 和 SibSp 结合的 FamilySize 创建一个新的特征.\n",
"这将使我们能够从我们的数据集中删除 Parch 和 SibSp."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"_cell_guid": "7e6c04ed-cfaa-3139-4378-574fd095d6ba",
"_uuid": "d532b86557317521f6cdfab2b05a366e8272738c"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" FamilySize \n",
" Survived \n",
" \n",
" \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 0.724138 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 0.578431 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 0.552795 \n",
" \n",
" \n",
" 6 \n",
" 7 \n",
" 0.333333 \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 0.303538 \n",
" \n",
" \n",
" 4 \n",
" 5 \n",
" 0.200000 \n",
" \n",
" \n",
" 5 \n",
" 6 \n",
" 0.136364 \n",
" \n",
" \n",
" 7 \n",
" 8 \n",
" 0.000000 \n",
" \n",
" \n",
" 8 \n",
" 11 \n",
" 0.000000 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" FamilySize Survived\n",
"3 4 0.724138\n",
"2 3 0.578431\n",
"1 2 0.552795\n",
"6 7 0.333333\n",
"0 1 0.303538\n",
"4 5 0.200000\n",
"5 6 0.136364\n",
"7 8 0.000000\n",
"8 11 0.000000"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for dataset in combine:\n",
" dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
"\n",
"train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "842188e6-acf8-2476-ccec-9e3451e4fa86",
"_uuid": "fe2ce46b514c097ac6d9b55b01ec5cca1e3cceed"
},
"source": [
"我们可以创建另一个名为 IsAlone 特征."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"_cell_guid": "5c778c69-a9ae-1b6b-44fe-a0898d07be7a",
"_uuid": "3251ed4ebd08403d7dab1b7a1ce7088f63f1f1b9"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" IsAlone \n",
" Survived \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0 \n",
" 0.505650 \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 0.303538 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" IsAlone Survived\n",
"0 0 0.505650\n",
"1 1 0.303538"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for dataset in combine:\n",
" dataset['IsAlone'] = 0\n",
" dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
"\n",
"train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e6b87c09-e7b2-f098-5b04-4360080d26bc",
"_uuid": "0eea444a8e105e416b6786fbc98b4ed6855c50f0"
},
"source": [
"让我们放弃 Parch, SibSp 和 FamilySize 特征, 转而使用 IsAlone 特征."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"_cell_guid": "74ee56a6-7357-f3bc-b605-6c41f8aa6566",
"_uuid": "56fadcd70a1cd2ac2110809c2279fd0b60204174"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Survived \n",
" Pclass \n",
" Sex \n",
" Age \n",
" Fare \n",
" Embarked \n",
" Title \n",
" IsAlone \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0 \n",
" 3 \n",
" 0 \n",
" 1 \n",
" 7.2500 \n",
" S \n",
" 1 \n",
" 0 \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 2 \n",
" 71.2833 \n",
" C \n",
" 3 \n",
" 0 \n",
" \n",
" \n",
" 2 \n",
" 1 \n",
" 3 \n",
" 1 \n",
" 1 \n",
" 7.9250 \n",
" S \n",
" 2 \n",
" 1 \n",
" \n",
" \n",
" 3 \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 2 \n",
" 53.1000 \n",
" S \n",
" 3 \n",
" 0 \n",
" \n",
" \n",
" 4 \n",
" 0 \n",
" 3 \n",
" 0 \n",
" 2 \n",
" 8.0500 \n",
" S \n",
" 1 \n",
" 1 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Survived Pclass Sex Age Fare Embarked Title IsAlone\n",
"0 0 3 0 1 7.2500 S 1 0\n",
"1 1 1 1 2 71.2833 C 3 0\n",
"2 1 3 1 1 7.9250 S 2 1\n",
"3 1 1 1 2 53.1000 S 3 0\n",
"4 0 3 0 2 8.0500 S 1 1"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
"test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
"combine = [train_df, test_df]\n",
"\n",
"train_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f890b730-b1fe-919e-fb07-352fbd7edd44",
"_uuid": "512eac83ac2ba3117b275e48518596352a4ca0ff"
},
"source": [
"我们还可以创建一个结合 Pclass 和 Age 的人造特征."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"_cell_guid": "305402aa-1ea1-c245-c367-056eef8fe453",
"_uuid": "d8ec9d2b6d57f680a248275d032a1735f2ee5f1a"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Age*Class \n",
" Age \n",
" Pclass \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 3 \n",
" 1 \n",
" 3 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 2 \n",
" 1 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 1 \n",
" 3 \n",
" \n",
" \n",
" 3 \n",
" 2 \n",
" 2 \n",
" 1 \n",
" \n",
" \n",
" 4 \n",
" 6 \n",
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" 3 \n",
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" 1 \n",
" \n",
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" 7 \n",
" 0 \n",
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" \n",
" 8 \n",
" 3 \n",
" 1 \n",
" 3 \n",
" \n",
" \n",
" 9 \n",
" 0 \n",
" 0 \n",
" 2 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Age*Class Age Pclass\n",
"0 3 1 3\n",
"1 2 2 1\n",
"2 3 1 3\n",
"3 2 2 1\n",
"4 6 2 3\n",
"5 3 1 3\n",
"6 3 3 1\n",
"7 0 0 3\n",
"8 3 1 3\n",
"9 0 0 2"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for dataset in combine:\n",
" dataset['Age*Class'] = dataset.Age * dataset.Pclass\n",
"\n",
"train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "13292c1b-020d-d9aa-525c-941331bb996a",
"_uuid": "9ffa98cb5aec630439e16831382f8456e0db4ce7"
},
"source": [
"### 完整化分类特征\n",
"\n",
"Embarked（出发港）特征有 S, Q, C 三个基于出发港口的值.\n",
"我们的训练集有两个丢失值.\n",
"我们简单的使用最常发生的情况来填充它."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"_cell_guid": "bf351113-9b7f-ef56-7211-e8dd00665b18",
"_uuid": "9c05cd517bc2727bfba3b88fc283f9b2f59cb51f"
},
"outputs": [
{
"data": {
"text/plain": [
"'S'"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"freq_port = train_df.Embarked.dropna().mode()[0]\n",
"freq_port"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"_cell_guid": "51c21fcc-f066-cd80-18c8-3d140be6cbae",
"_uuid": "f820e03ca066443667a164be135a2d2217958d75"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Embarked \n",
" Survived \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" C \n",
" 0.553571 \n",
" \n",
" \n",
" 1 \n",
" Q \n",
" 0.389610 \n",
" \n",
" \n",
" 2 \n",
" S \n",
" 0.339009 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Embarked Survived\n",
"0 C 0.553571\n",
"1 Q 0.389610\n",
"2 S 0.339009"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for dataset in combine:\n",
" dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
" \n",
"train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f6acf7b2-0db3-e583-de50-7e14b495de34",
"_uuid": "b683a829d3a58ef016d38ba2146931c87241e4ec"
},
"source": [
"### 转换分类特征为数值的\n",
"\n",
"我们现在可以通过创建一个新的数字港特征来转换 EmbarkedFill 特征."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"_cell_guid": "89a91d76-2cc0-9bbb-c5c5-3c9ecae33c66",
"_uuid": "c9378a196d37cf745a6989f7de3bfec5dab80b35"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Survived \n",
" Pclass \n",
" Sex \n",
" Age \n",
" Fare \n",
" Embarked \n",
" Title \n",
" IsAlone \n",
" Age*Class \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0 \n",
" 3 \n",
" 0 \n",
" 1 \n",
" 7.2500 \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 3 \n",
" \n",
" \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 2 \n",
" 71.2833 \n",
" 1 \n",
" 3 \n",
" 0 \n",
" 2 \n",
" \n",
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" 2 \n",
" 53.1000 \n",
" 0 \n",
" 3 \n",
" 0 \n",
" 2 \n",
" \n",
" \n",
" 4 \n",
" 0 \n",
" 3 \n",
" 0 \n",
" 2 \n",
" 8.0500 \n",
" 0 \n",
" 1 \n",
" 1 \n",
" 6 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Survived Pclass Sex Age Fare Embarked Title IsAlone Age*Class\n",
"0 0 3 0 1 7.2500 0 1 0 3\n",
"1 1 1 1 2 71.2833 1 3 0 2\n",
"2 1 3 1 1 7.9250 0 2 1 3\n",
"3 1 1 1 2 53.1000 0 3 0 2\n",
"4 0 3 0 2 8.0500 0 1 1 6"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for dataset in combine:\n",
" dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
"\n",
"train_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e3dfc817-e1c1-a274-a111-62c1c814cecf",
"_uuid": "b6f1a6648c4cfcce63bba886403bae239413b912"
},
"source": [
"### 快速完整化兵转换数值的特征\n",
"\n",
"现在，我们可以在测试数据集使用模式下为单个缺失值完整化票价特征, 以获取此特征最常出现的值. 我们用一行代码来完成.\n",
"\n",
"请注意, 我们并没有创建中间用的新特征, 也没有对相关性进行任何进一步的分析以猜测丢失的特征, 因为我们只替换单个值. 完成目标达到了模型算法对非空值操作的期望要求.\n",
"\n",
"我们可能还想把票价四舍五入到小数点后两位, 因为它代表货币."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"_cell_guid": "3600cb86-cf5f-d87b-1b33-638dc8db1564",
"_uuid": "b697a634d61798fe5b64d128b330eaae7e219eb2"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" PassengerId \n",
" Pclass \n",
" Sex \n",
" Age \n",
" Fare \n",
" Embarked \n",
" Title \n",
" IsAlone \n",
" Age*Class \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 892 \n",
" 3 \n",
" 0 \n",
" 2 \n",
" 7.8292 \n",
" 2 \n",
" 1 \n",
" 1 \n",
" 6 \n",
" \n",
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" 1 \n",
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" 2 \n",
" 7.0000 \n",
" 0 \n",
" 3 \n",
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" 6 \n",
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" 0 \n",
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" 8.6625 \n",
" 0 \n",
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" 3 \n",
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" 12.2875 \n",
" 0 \n",
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"
\n",
""
],
"text/plain": [
" PassengerId Pclass Sex Age Fare Embarked Title IsAlone Age*Class\n",
"0 892 3 0 2 7.8292 2 1 1 6\n",
"1 893 3 1 2 7.0000 0 3 0 6\n",
"2 894 2 0 3 9.6875 2 1 1 6\n",
"3 895 3 0 1 8.6625 0 1 1 3\n",
"4 896 3 1 1 12.2875 0 3 0 3"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
"test_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4b816bc7-d1fb-c02b-ed1d-ee34b819497d",
"_uuid": "8ae1f2214210644f0670e9d16e4ab19a5c1e9f36"
},
"source": [
"我们创建 FareBand 特征."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"_cell_guid": "0e9018b1-ced5-9999-8ce1-258a0952cbf2",
"_uuid": "098b3896b60d1385b2297c16c405d33778e44993"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" FareBand \n",
" Survived \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" (-0.001, 7.91] \n",
" 0.197309 \n",
" \n",
" \n",
" 1 \n",
" (7.91, 14.454] \n",
" 0.303571 \n",
" \n",
" \n",
" 2 \n",
" (14.454, 31.0] \n",
" 0.454955 \n",
" \n",
" \n",
" 3 \n",
" (31.0, 512.329] \n",
" 0.581081 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" FareBand Survived\n",
"0 (-0.001, 7.91] 0.197309\n",
"1 (7.91, 14.454] 0.303571\n",
"2 (14.454, 31.0] 0.454955\n",
"3 (31.0, 512.329] 0.581081"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
"train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d65901a5-3684-6869-e904-5f1a7cce8a6d",
"_uuid": "c6d38425e429ad5f7370174aee7db5fc28b9d0c1"
},
"source": [
"将 Fare 特征转换为基于 FareBand 的顺序值."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"_cell_guid": "385f217a-4e00-76dc-1570-1de4eec0c29c",
"_uuid": "bc6ba15392d78699e3ed46cf539d1251d3d64ec2"
},
"outputs": [
{
"data": {
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" 0 \n",
" 3 \n",
" 0 \n",
" 0 \n",
" 2 \n",
" 0 \n",
" 4 \n",
" 0 \n",
" 0 \n",
" \n",
" \n",
" 8 \n",
" 1 \n",
" 3 \n",
" 1 \n",
" 1 \n",
" 1 \n",
" 0 \n",
" 3 \n",
" 0 \n",
" 3 \n",
" \n",
" \n",
" 9 \n",
" 1 \n",
" 2 \n",
" 1 \n",
" 0 \n",
" 2 \n",
" 1 \n",
" 3 \n",
" 0 \n",
" 0 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Survived Pclass Sex Age Fare Embarked Title IsAlone Age*Class\n",
"0 0 3 0 1 0 0 1 0 3\n",
"1 1 1 1 2 3 1 3 0 2\n",
"2 1 3 1 1 1 0 2 1 3\n",
"3 1 1 1 2 3 0 3 0 2\n",
"4 0 3 0 2 1 0 1 1 6\n",
"5 0 3 0 1 1 2 1 1 3\n",
"6 0 1 0 3 3 0 1 1 3\n",
"7 0 3 0 0 2 0 4 0 0\n",
"8 1 3 1 1 1 0 3 0 3\n",
"9 1 2 1 0 2 1 3 0 0"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for dataset in combine:\n",
" dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
" dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
" dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2\n",
" dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
" dataset['Fare'] = dataset['Fare'].astype(int)\n",
"\n",
"train_df = train_df.drop(['FareBand'], axis=1)\n",
"combine = [train_df, test_df]\n",
" \n",
"train_df.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "27272bb9-3c64-4f9a-4a3b-54f02e1c8289",
"_uuid": "daa1ef2745944d7029c5c1d4659c1e393f19013b"
},
"source": [
"并且测试数据集也一样."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"_cell_guid": "d2334d33-4fe5-964d-beac-6aa620066e15",
"_uuid": "550dccae1471b9c2f6d627ae48d376ca59a322c1"
},
"outputs": [
{
"data": {
"text/html": [
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" \n",
"
\n",
""
],
"text/plain": [
" PassengerId Pclass Sex Age Fare Embarked Title IsAlone Age*Class\n",
"0 892 3 0 2 0 2 1 1 6\n",
"1 893 3 1 2 0 0 3 0 6\n",
"2 894 2 0 3 1 2 1 1 6\n",
"3 895 3 0 1 1 0 1 1 3\n",
"4 896 3 1 1 1 0 3 0 3\n",
"5 897 3 0 0 1 0 1 1 0\n",
"6 898 3 1 1 0 2 2 1 3\n",
"7 899 2 0 1 2 0 1 0 2\n",
"8 900 3 1 1 0 1 3 1 3\n",
"9 901 3 0 1 2 0 1 0 3"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "69783c08-c8cc-a6ca-2a9a-5e75581c6d31",
"_uuid": "d5bf81957541c7ce9dcb2f25210f1bb9fe2f0602"
},
"source": [
"## 模型, 预测和解决方案\n",
"\n",
"现在我们准备训练模型并通过训练得到的模型预测结果。有60多种用于预测的模型可供选择。我们必须了解问题的类型和解决方案的要求，将模型数量缩小到少数几个。我们的问题是分类和回归问题，因为需要确定输出（生存与否）与其他变量或特征（性别，年龄，港口...）之间的关系。此外，我们的问题应该属于监督学习，因为我们用已知类别的数据集来训练我们的模型。有了监督学习、分类和回归这两个标准，我们可以将模型选择的范围缩小到几个。这些包括：\n",
"- Logistic回归\n",
"- KNN或K—近邻\n",
"- 支持向量机\n",
"- 朴素贝叶斯分类器\n",
"- 决策树\n",
"- 随机森林\n",
"- 感知器\n",
"- 人工神经网络\n",
"- 相关向量机\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"_cell_guid": "0acf54f9-6cf5-24b5-72d9-29b30052823a",
"_uuid": "2ba5475a0389b56ceb1ddcb5b1ed927107e80fe8"
},
"outputs": [
{
"data": {
"text/plain": [
"((891, 8), (891,), (418, 8))"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train = train_df.drop(\"Survived\", axis=1)\n",
"Y_train = train_df[\"Survived\"]\n",
"X_test = test_df.drop(\"PassengerId\", axis=1).copy()\n",
"X_train.shape, Y_train.shape, X_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "579bc004-926a-bcfe-e9bb-c8df83356876",
"_uuid": "dcd0657cf810fe62e145a86ba7cdc5c1f7370e7a"
},
"source": [
"Logistic回归形式简单，易于建模，适合用于早期的工作流程。Logistics回归使用线性回归模型的预测结果去逼近真实标记的对数几率，形式为参数化的Logistics分布。参考维基百科[Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression).\n",
"\n",
"注意模型产生的“置信度评分”是基于训练集的。"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"_cell_guid": "0edd9322-db0b-9c37-172d-a3a4f8dec229",
"_uuid": "f0b92b7d35145c43a11ef16d4be0d257c616fb37"
},
"outputs": [
{
"data": {
"text/plain": [
"80.359999999999999"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Logistic Regression\n",
"\n",
"logreg = LogisticRegression()\n",
"logreg.fit(X_train, Y_train)\n",
"Y_pred = logreg.predict(X_test)\n",
"acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
"acc_log"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "3af439ae-1f04-9236-cdc2-ec8170a0d4ee",
"_uuid": "461f8f4d266fb785bd3f29fa0aa9fd47353a4053"
},
"source": [
"我们可以使用Logistic回归来验证我们之前对特征的创建所做的假设。这可以通过计算决策函数中的特征的系数来完成。\n",
"\n",
"系数为正说明该特征增加了结果的对数几率（因而增加了概率），系数为负说明该特征降低了结果的对数几率（从而降低了概率）\n",
"\n",
"- Sex特征有最高的正系数，意味着当Sex从男（0）变成女（1）时，Survived = 1的概率增加最多。\n",
"- 相反地，随着Pclass特征的增加，Survived = 1的概率减少的最多。\n",
"- Age * Class是一个很好的人造特征，因为它与Survived具有次高的负相关性。\n",
"- Title特征有第二高的正相关系数。"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"_cell_guid": "e545d5aa-4767-7a41-5799-a4c5e529ce72",
"_uuid": "d13fa4e9617cc61c8801689fd4d9e5470510d7ad"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Feature \n",
" Correlation \n",
" \n",
" \n",
" \n",
" \n",
" 1 \n",
" Sex \n",
" 2.201527 \n",
" \n",
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" Title \n",
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" Age \n",
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" \n",
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" IsAlone \n",
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" \n",
" \n",
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\n",
""
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"text/plain": [
" Feature Correlation\n",
"1 Sex 2.201527\n",
"5 Title 0.398234\n",
"2 Age 0.287164\n",
"4 Embarked 0.261762\n",
"6 IsAlone 0.129140\n",
"3 Fare -0.085150\n",
"7 Age*Class -0.311199\n",
"0 Pclass -0.749006"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
"coeff_df.columns = ['Feature']\n",
"coeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n",
"\n",
"coeff_df.sort_values(by='Correlation', ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ac041064-1693-8584-156b-66674117e4d0",
"_uuid": "07a0a0f3a820c9d4ca0472d1b9ec05fa822d3479"
},
"source": [
"接下来，我们使用支持向量机（SVM）模型。支持向量机是一个监督学习模型，它使用相关学习算法来分析数据，可以用于分类和回归问题。在二元分类的情况下，SVM算法建立一个模型，去找两类训练样本“正中间”的划分超平面，因为该划分超平面对训练样本局部扰动的“容忍性”最好。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Support_vector_machine).\n",
"\n",
"注意SVM模型生成的“置信度评分”高于Logistics回归模型。"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"_cell_guid": "7a63bf04-a410-9c81-5310-bdef7963298f",
"_uuid": "32a425989ee9cf681fad51c867ce3c76126f5b05"
},
"outputs": [
{
"data": {
"text/plain": [
"83.840000000000003"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Support Vector Machines\n",
"\n",
"svc = SVC()\n",
"svc.fit(X_train, Y_train)\n",
"Y_pred = svc.predict(X_test)\n",
"acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n",
"acc_svc"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "172a6286-d495-5ac4-1a9c-5b77b74ca6d2",
"_uuid": "0075b5fb532a249c701efa7ef84b2f52c9f29776"
},
"source": [
"在模式识别中，k-近邻算法（简称k-NN）是一种用于分类和回归的无参数方法。测试样本找出训练集中与其最靠近的k个训练样本，选择这k个样本中出现最多的类别标记作为预测结果（k是一个正整数，通常很小）。如果k = 1，则该对象的类别和最近邻样本的类别一致。 参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm).\n",
"\n",
"KNN的“置信度评分”比Logistics回归好，但比SVM差。"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"_cell_guid": "ca14ae53-f05e-eb73-201c-064d7c3ed610",
"_uuid": "f65598719a7e411ec09f6665d98f86e3e26a1f85"
},
"outputs": [
{
"data": {
"text/plain": [
"84.739999999999995"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn = KNeighborsClassifier(n_neighbors = 3)\n",
"knn.fit(X_train, Y_train)\n",
"Y_pred = knn.predict(X_test)\n",
"acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
"acc_knn"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "810f723d-2313-8dfd-e3e2-26673b9caa90",
"_uuid": "c1e80aa85d47f1076aa3d0628a37d903b1959ad4"
},
"source": [
"在机器学习中，朴素贝叶斯分类器是一个基于所有特征互相独立的贝叶斯理论的简单概率分类器。朴素贝叶斯分类器具有高度可扩展性，在学习过程中需要大量的线性特征作为参数。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier).\n",
"\n",
"该模型生成的“置信度评分”是目前模型中最低的。"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"_cell_guid": "50378071-7043-ed8d-a782-70c947520dae",
"_uuid": "db060b792effa86c5483bf02786b8a69bb043fd5"
},
"outputs": [
{
"data": {
"text/plain": [
"72.280000000000001"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Gaussian Naive Bayes\n",
"\n",
"gaussian = GaussianNB()\n",
"gaussian.fit(X_train, Y_train)\n",
"Y_pred = gaussian.predict(X_test)\n",
"acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
"acc_gaussian"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1e286e19-b714-385a-fcfa-8cf5ec19956a",
"_uuid": "c5f397f24dda3a6181708bee43314f6f316d1328"
},
"source": [
"感知器是用于二元分类器的监督学习的算法（可以决定包含一个向量的输入是否属于某个类别）。它是一种线性分类器，即一种分类算法，通过一个线性预测函数将一组权重与特征向量组合来进行预测。该算法允许在线学习，因为它在一次迭代中只处理一个训练集中的元素。 参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Perceptron)."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"_cell_guid": "ccc22a86-b7cb-c2dd-74bd-53b218d6ed0d",
"_uuid": "4ae3698170341015098b1fcc4b716bbee4b01f54"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Anaconda\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
" \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"78.0"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Perceptron\n",
"\n",
"perceptron = Perceptron()\n",
"perceptron.fit(X_train, Y_train)\n",
"Y_pred = perceptron.predict(X_test)\n",
"acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
"acc_perceptron"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"_cell_guid": "a4d56857-9432-55bb-14c0-52ebeb64d198",
"_uuid": "a7667ed1e8c4f753d0c8a04111beee9526f3e0e6"
},
"outputs": [
{
"data": {
"text/plain": [
"79.120000000000005"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Linear SVC\n",
"\n",
"linear_svc = LinearSVC()\n",
"linear_svc.fit(X_train, Y_train)\n",
"Y_pred = linear_svc.predict(X_test)\n",
"acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
"acc_linear_svc"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"_cell_guid": "dc98ed72-3aeb-861f-804d-b6e3d178bf4b",
"_uuid": "4d0cc9dd1855a0e8c2206a7ae53f2aa5c35b87b3"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Anaconda\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
" \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"80.019999999999996"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Stochastic Gradient Descent\n",
"\n",
"sgd = SGDClassifier()\n",
"sgd.fit(X_train, Y_train)\n",
"Y_pred = sgd.predict(X_test)\n",
"acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
"acc_sgd"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "bae7f8d7-9da0-f4fd-bdb1-d97e719a18d7",
"_uuid": "5e191ae0e5c2fad6c4601d792cbc3d7b71097822"
},
"source": [
"该模型使用决策树作为预测模型，将特征（树的分支）映射到决策结果（树的叶结点）。目标变量是有限的一组值的树称为分类树; 在这些树结构中，叶结点对应于决策结果，其他每个结点对应于一个属性测试，每个结点包含的样本集合根据属性测试的结果被划分到子结点中。目标变量可以取连续值（通常是实数）的决策树称为回归树。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning).\n",
"\n",
"该模型的“置信度评分”是目前模型中最高的。"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"_cell_guid": "dd85f2b7-ace2-0306-b4ec-79c68cd3fea0",
"_uuid": "acc5910f9900f1404b6fcdba93dd28fdda0766b7"
},
"outputs": [
{
"data": {
"text/plain": [
"86.760000000000005"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Decision Tree\n",
"\n",
"decision_tree = DecisionTreeClassifier()\n",
"decision_tree.fit(X_train, Y_train)\n",
"Y_pred = decision_tree.predict(X_test)\n",
"acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
"acc_decision_tree"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "85693668-0cd5-4319-7768-eddb62d2b7d0",
"_uuid": "0c37a62dd5b0c6e9a6f644d45a92eb3851bc2991"
},
"source": [
"随机森林是最流行的模型之一。随机森林或随机决策树森林是一种用于分类，回归或其他任务的集成学习模型，它通过在训练时构造大量的决策树（n_estimators = 100），再使用某种策略将这些“个体学习器”结合起来。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Random_forest).\n",
"\n",
"该模型的“置信度评分”是目前模型中最高的。我们决定使用这个模型的输出（Y_pred）来作为竞赛结果。"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"_cell_guid": "f0694a8e-b618-8ed9-6f0d-8c6fba2c4567",
"_uuid": "a3a92337489f7f75e233d7a5d645cdbf791071e7"
},
"outputs": [
{
"data": {
"text/plain": [
"86.760000000000005"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Random Forest\n",
"\n",
"random_forest = RandomForestClassifier(n_estimators=100)\n",
"random_forest.fit(X_train, Y_train)\n",
"Y_pred = random_forest.predict(X_test)\n",
"random_forest.score(X_train, Y_train)\n",
"acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
"acc_random_forest"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f6c9eef8-83dd-581c-2d8e-ce932fe3a44d",
"_uuid": "24d40c30b491d0d109908035ed86f0929860dd5e"
},
"source": [
"### 模型评估\n",
"\n",
"现在, 我们可以对所有模型进行评估, 为我们的问题选择最好的模型。\n",
"虽然决策树和随机森林评分相同, 但我们选择使用随机森林，因为随机森林会校正决策树“过拟合”的缺点。"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"_cell_guid": "1f3cebe0-31af-70b2-1ce4-0fd406bcdfc6",
"_uuid": "79536b2878fa12ceaf3648bfd5bb5de63b903709"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" \n",
" Model \n",
" Score \n",
" \n",
" \n",
" \n",
" \n",
" 3 \n",
" Random Forest \n",
" 86.76 \n",
" \n",
" \n",
" 8 \n",
" Decision Tree \n",
" 86.76 \n",
" \n",
" \n",
" 1 \n",
" KNN \n",
" 84.74 \n",
" \n",
" \n",
" 0 \n",
" Support Vector Machines \n",
" 83.84 \n",
" \n",
" \n",
" 2 \n",
" Logistic Regression \n",
" 80.36 \n",
" \n",
" \n",
" 6 \n",
" Stochastic Gradient Decent \n",
" 80.02 \n",
" \n",
" \n",
" 7 \n",
" Linear SVC \n",
" 79.12 \n",
" \n",
" \n",
" 5 \n",
" Perceptron \n",
" 78.00 \n",
" \n",
" \n",
" 4 \n",
" Naive Bayes \n",
" 72.28 \n",
" \n",
" \n",
"
\n",
""
],
"text/plain": [
" Model Score\n",
"3 Random Forest 86.76\n",
"8 Decision Tree 86.76\n",
"1 KNN 84.74\n",
"0 Support Vector Machines 83.84\n",
"2 Logistic Regression 80.36\n",
"6 Stochastic Gradient Decent 80.02\n",
"7 Linear SVC 79.12\n",
"5 Perceptron 78.00\n",
"4 Naive Bayes 72.28"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"models = pd.DataFrame({\n",
" 'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
" 'Random Forest', 'Naive Bayes', 'Perceptron', \n",
" 'Stochastic Gradient Decent', 'Linear SVC', \n",
" 'Decision Tree'],\n",
" 'Score': [acc_svc, acc_knn, acc_log, \n",
" acc_random_forest, acc_gaussian, acc_perceptron, \n",
" acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
"models.sort_values(by='Score', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"_cell_guid": "28854d36-051f-3ef0-5535-fa5ba6a9bef7",
"_uuid": "a2cda3bdd06c9b6a0cb2c02ca276c049865108fb",
"collapsed": true
},
"outputs": [],
"source": [
"submission = pd.DataFrame({\n",
" \"PassengerId\": test_df[\"PassengerId\"],\n",
" \"Survived\": Y_pred\n",
" })\n",
"# submission.to_csv('../output/submission.csv', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "fcfc8d9f-e955-cf70-5843-1fb764c54699",
"_uuid": "b8e1264e98af00d119e07a776643e6ce08b59666"
},
"source": [
"我们提交给竞赛网站 Kaggle 的比赛结果在 6,082 个参赛作品中获得 3883 名.\n",
"当竞赛正在进行时，这个结果是具有指导意义的.\n",
"这个结果只占提交数据集的一部分.\n",
"对我们的第一次尝试是不错的.\n",
"欢迎任何提高我们的分数的建议."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "aeec9210-f9d8-cd7c-c4cf-a87376d5f693",
"_uuid": "b4758c6c3b2f72da72397cf2f82bc0b92bec3c5b"
},
"source": [
"## 参考文献\n",
"\n",
"该手册是基于完成解决《泰坦尼克号》竞赛和其它来源的伟大工作而创建的.\n",
"\n",
"- [泰坦尼克号之旅](https://www.kaggle.com/omarelgabry/titanic/a-journey-through-titanic)\n",
"- [ Pandas 入门指南: Kaggle 的泰坦尼克号竞赛](https://www.kaggle.com/c/titanic/details/getting-started-with-random-forests)\n",
"- [泰坦尼克号的最佳处理分类器](https://www.kaggle.com/sinakhorami/titanic/titanic-best-working-classifier)"
]
}
],
"metadata": {
"_change_revision": 0,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
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"nbformat_minor": 1
}
================================================
FILE: docs/Kaggle/competitions/getting-started/titanic/titanic-data-science-solutions.md
================================================
# 《泰坦尼克号》数据科学解决方案
## 翻译相关
原文地址:
开源组织: [ApacheCN ~ apachecn.org](http://www.apachecn.org)
贡献者: [@那伊抹微笑](https://github.com/wangyangting), [@李铭哲](https://github.com/limingzhe), [@刘海飞](https://github.com/WindZQ), [@DL - 小王子](https://github.com/VPrincekin), [@成飘飘](https://github.com/chengpiaopiao)
最近更新: 2018-01-03
---
### 我已经发布了一个新的 Python 包 [Speedml](https://speedml.com), 它将该 notebook 中的使用的技术编译成一个 intuitive（直观的），powerful（功能强大的）且 productive（高效的）API.
### Speedml 帮助我在 Kaggle 排行榜上从最低的 80% 跳到最高的 20%, 迭代的次数很少.
### 还有一件事...Speedml 实现了这一点, 代码行数减少了近 70%!
### 下载并且运行代码 [Speedml 版本的泰坦尼克号解决方案](https://github.com/Speedml/notebooks/blob/master/titanic/titanic-solution-using-speedml.ipynb).
---
该 notebook 是 [Data Science Solutions](https://startupsci.com) 书籍的一个手册. 该 notebook 引导我们通过一个典型的工作流程来解决像 Kaggle 这样类似的网站的数据科学竞赛.
有几个优秀的 notebooks 可以用来研究数据科学竞赛作品.
然而许多手册将会跳过一些关于如何开发解决方案的解释, 因为这些 notebooks 是专门为这些专家开发的.
该 notebook 的目标是遵循一步一步的工作流程, 解释我们在解决方案开发过程中所做的每一个决策的每个步骤和理由.
## 工作流阶段
1. 问题或问题的定义.
2. 获取 training（训练）和 testing（测试）数据.
3. Wrangle（整理）, prepare（准备）, cleanse（清洗）数据
4. Analyze（分析）, identify patterns 以及探索数据.
5. Model（模型）, predict（预测）以及解决问题.
6. Visualize（可视化）, report（报告）和提出解决问题的步骤以及最终解决方案.
7. 提供或提交结果.
该工作流指出了，每个阶段如何遵循另一个阶段的常见顺序.
但是也有例外的场景.
- 我们可能结合多个工作流阶段. 我们可以通过可视化数据进行分析.
- 比 indicated（说明）更早的进行一个阶段. 我们可能在 wrangling（整理）过程的前后来分析数据.
- 在我们的工作流程中多次执行一个阶段. 可视化阶段可能被使用多次.
- Drop a stage altogether. We may not need supply stage to productize or service enable our dataset for a competition.
## 问题和问题定义
像 Kaggle 这样的竞赛网站, 它们会定义要解决或质疑的问题, 同时提供用于训练数据科学模型和根据测试数据集测试模型结果的数据集,（即, 训练集 和 测试集）.
针对《泰坦尼克号生存竞赛》的问题或定义在 [这里是 Kaggle 描述](https://www.kaggle.com/c/titanic) 中有描述.
> 从泰坦尼克号的灾难中幸存下来或没有幸存的乘客的样本训练集（train.csv）中，如果测试数据集（test.csv）中的这些乘客幸存下来，我们的模型是否可以基于给定的测试数据集（test.csv）来确定。
我们也可能希望对我们问题的领域有所了解.
这在 [Kaggle 竞赛描述](https://www.kaggle.com/c/titanic) 页面有详细的描述.
以下是要注意的事项.
- 1912年4月15日, 在首航期间, 泰坦尼克号撞上一座冰山后沉没, 2224 名乘客和机组人员中有 1502 人遇难. 生成率解释为 32%.
- 还难导致生命损失的原因之一是没有足够的救生艇给乘客和船员.
- 尽管幸存下来的运气有一些因素, 但一些人比其他人更有可能幸存下来，比如妇女, 儿童和上层阶级.
## 工作流目标
数据科学解决方案工作流程有以下七个主要的目标.
**Classifying（分类）.** 我们可能想对我们的样本进行分类或加以类别. 我们也可能想要了解不同类别与解决方案目标的含义或相关性.
**Correlating（相关）.** 可以根据训练数据集中的可用特征来处理这个问题. 数据集中的哪些特征对我们的解决方案目标有重大贡献？从统计学上讲, 特征和解决方案的目标中有一个[相关](https://en.wikiversity.org/wiki/Correlation)？随着特征值的改变, 解决方案的状态也会随之改变, 反之亦然？这可以针对给定数据集中的数字和分类特征进行测试. 我们也可能想要确定以后的目标和工作流程阶段的生存以外的特征之间的相关性. 关联某些特征可能有助于创建, 完善或纠正特征。
**Converting（转换）.** 对于建模阶段, 需要准备数据. 根据模型算法的选择, 可能需要将所有特征转换为数值等价值. 所以例如将文本分类值转换为数字的值.
**Completing（完整）.** 数据准备也可能要求我们估计一个特征中的任何缺失值. 当没有缺失值时，模型算法可能效果最好.
**Correcting（校正）.** 我们还可以分析给定的训练数据集以找出错误或者可能在特征内不准确的值, 并尝试对这些值进行校正或排除包含错误的样本. 一种方法是检测样本或特征中的任何异常值. 如果对分析没有贡献, 或者可能会显着扭曲结果, 我们也可能完全丢弃一个特征.
**Creating（创建）.** 我们可以根据现有特征或一组特征来创建新特征, 以便新特征遵循 correlation（相关）, conversion（转换）, completeness（完整）的目标.
**Charting（绘图）.** 如何根据数据的性质和解决方案的目标来选择正确的可视化图表工具以及绘图.
## 重构的发布日期 2017年1月29日
We are significantly refactoring the notebook based on (a) comments received by readers, (b) issues in porting notebook from Jupyter kernel (2.7) to Kaggle kernel (3.5), and (c) review of few more best practice kernels.
### 用户评论
- Combine training and test data for certain operations like converting titles across dataset to numerical values. (thanks @Sharan Naribole)
- Correct observation - nearly 30% of the passengers had siblings and/or spouses aboard. (thanks @Reinhard)
- Correctly interpreting logistic regresssion coefficients. (thanks @Reinhard)
### 移植问题
- Specify plot dimensions, bring legend into plot.
### 最佳实践
- 在项目早期进行特征相关分析.
- 为了可读性, 使用多个图而不是覆盖图.
```python
# 数据分析和整理
import pandas as pd
import numpy as np
import random as rnd
# 可视化
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# 机器学习
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
```
## 获取数据
Python 的 Pandas 包帮助我们处理我们的数据集.
我们首先将训练和测试数据集收集到 Pandas DataFrame 中.
我们还将这些数据集组合在一起, 在两个数据集上运行某些操作.
```python
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
```
## 通过 describing（描述）数据进行分析
在我们的项目早期, Pandas 还帮助描述回答数据集中的以下问题.
**数据集中哪些特征是可用的?**
注意: 直接操作或分析这些特征的名称.
这些特征名称在 [Kaggle 数据页面](https://www.kaggle.com/c/titanic/data) 页面上有描述.
```python
print(train_df.columns.values)
```
['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'
'Ticket' 'Fare' 'Cabin' 'Embarked']
**哪些特征是 categorical（分类的）?**
这些值将样本分成几组相似的样本.
在分类特征中的值是 nominal（标称的）, ordinal（顺序的）或 ratio（比例的）还是 interval based（基于区间的）值？
除此之外, 这有助于我们选择合适的图表进行可视化.
- Categorical（分类的）: Survived, Sex, and Embarked. Ordinal（顺序的）: Pclass.
**哪些特征是 numerical（数值的）?**
哪些特征是数值的？
这些值随样本而变化.
在数值特征中的值是 discrete（离散的）和 continuous（连续的） 还是 timeseries based（基于时间序列的）？
- Continous（连续的）: Age, Fare. Discrete（离散的）: SibSp, Parch.
```python
# 预览数据
train_df.head()
```
PassengerId
Survived
Pclass
Name
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
0
1
0
3
Braund, Mr. Owen Harris
male
22.0
1
0
A/5 21171
7.2500
NaN
S
1
2
1
1
Cumings, Mrs. John Bradley (Florence Briggs Th...
female
38.0
1
0
PC 17599
71.2833
C85
C
2
3
1
3
Heikkinen, Miss. Laina
female
26.0
0
0
STON/O2. 3101282
7.9250
NaN
S
3
4
1
1
Futrelle, Mrs. Jacques Heath (Lily May Peel)
female
35.0
1
0
113803
53.1000
C123
S
4
5
0
3
Allen, Mr. William Henry
male
35.0
0
0
373450
8.0500
NaN
S
**哪些特征是混合的数据类型?**
相同特征中的 numerical（数值的）, alphanumeric（字母数值的）.
这些是校正目标的候选特征.
- Ticket 是numerical（数值的）和 alphanumeric（字母数值的）数据类型的混合类型. Cabin 是 alphanumeric（字母数值的）.
**哪些特征也许包含错误或拼写错误?**
对于一个大型的数据集来说, 这是很难审查的, 但是从较小的数据集中查看一些样本可能会直接告诉我们, 哪些特征可能需要校正.
- Name 特征也许包含错误或拼写错误, 因为有几种方法可以用来描述名称, 包括头衔，圆括号和用于替代或短名称的引号.
```python
train_df.tail()
```
PassengerId
Survived
Pclass
Name
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
886
887
0
2
Montvila, Rev. Juozas
male
27.0
0
0
211536
13.00
NaN
S
887
888
1
1
Graham, Miss. Margaret Edith
female
19.0
0
0
112053
30.00
B42
S
888
889
0
3
Johnston, Miss. Catherine Helen "Carrie"
female
NaN
1
2
W./C. 6607
23.45
NaN
S
889
890
1
1
Behr, Mr. Karl Howell
male
26.0
0
0
111369
30.00
C148
C
890
891
0
3
Dooley, Mr. Patrick
male
32.0
0
0
370376
7.75
NaN
Q
**哪些特征包含 blank（空格）, null（无效的）或 empty values（空值）?**
这些将需要校正.
- Cabin > Age > Embarked features contain a number of null values in that order for the training dataset.
- Cabin > Age are incomplete in case of test dataset.
**各个特征的数据类型是什么样的?**
在转换的目标时可以帮助我们.
- 7 个特征是 integer 或 floats. 6 个在测试数据集中.
- 5 个特征是 strings (object).
```python
train_df.info()
print('_'*40)
test_df.info()
```
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId 891 non-null int64
Survived 891 non-null int64
Pclass 891 non-null int64
Name 891 non-null object
Sex 891 non-null object
Age 714 non-null float64
SibSp 891 non-null int64
Parch 891 non-null int64
Ticket 891 non-null object
Fare 891 non-null float64
Cabin 204 non-null object
Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB
________________________________________
RangeIndex: 418 entries, 0 to 417
Data columns (total 11 columns):
PassengerId 418 non-null int64
Pclass 418 non-null int64
Name 418 non-null object
Sex 418 non-null object
Age 332 non-null float64
SibSp 418 non-null int64
Parch 418 non-null int64
Ticket 418 non-null object
Fare 417 non-null float64
Cabin 91 non-null object
Embarked 418 non-null object
dtypes: float64(2), int64(4), object(5)
memory usage: 36.0+ KB
**样本中数值特征值的分布是什么?**
这有助于我们确定, 除了其他早期的思考, 在实际问题领域的训练数据集是如何具有代表性的.
- 总样本是 891 或者在泰坦尼克号（2,224）上实际旅客的 40%.
- Survived（生存）是一个具有 0 或 1 值的分类特征.
- 大约 38% 样本幸存了下来, 然而实际的幸存率是 32%.
- 大多数旅客 (> 75%) 没有和父母或孩子一起旅行.
- 近 30% 的旅客有兄弟姐妹 和/或 配偶.
- 少数旅客 Fares（票价）差异显著 (<1%), 最高达 $512.
- 很少有年长的旅客 (<1%) 在年龄范围 65-80.
```python
train_df.describe()
# Review survived rate using `percentiles=[.61, .62]` knowing our problem description mentions 38% survival rate.
# Review Parch distribution using `percentiles=[.75, .8]`
# SibSp distribution `[.68, .69]`
# Age and Fare `[.1, .2, .3, .4, .5, .6, .7, .8, .9, .99]`
```
PassengerId
Survived
Pclass
Age
SibSp
Parch
Fare
count
891.000000
891.000000
891.000000
714.000000
891.000000
891.000000
891.000000
mean
446.000000
0.383838
2.308642
29.699118
0.523008
0.381594
32.204208
std
257.353842
0.486592
0.836071
14.526497
1.102743
0.806057
49.693429
min
1.000000
0.000000
1.000000
0.420000
0.000000
0.000000
0.000000
25%
223.500000
0.000000
2.000000
20.125000
0.000000
0.000000
7.910400
50%
446.000000
0.000000
3.000000
28.000000
0.000000
0.000000
14.454200
75%
668.500000
1.000000
3.000000
38.000000
1.000000
0.000000
31.000000
max
891.000000
1.000000
3.000000
80.000000
8.000000
6.000000
512.329200
**分类特征的分布是什么样的?**
- Names（名称）特征在数据集中是唯一的 (count=unique=891)
- Sex（性别）变量有两个可能的值, 男性为 65% (top=male, freq=577/count=891).
- Cabin（房间号）值在样本中有重复. 或者几个旅客共享一个客舱.
- Embarked（出发港）有 3 个可能的值. 大多数乘客使用 S 港口(top=S)
- Ticket（船票号码）特征有很高 (22%) 的重复值 (unique=681).
```python
train_df.describe(include=['O'])
```
Name
Sex
Ticket
Cabin
Embarked
count
891
891
891
204
889
unique
891
2
681
147
3
top
Mitchell, Mr. Henry Michael
male
1601
C23 C25 C27
S
freq
1
577
7
4
644
### 基于数据分析的假设
到目前为止, 基于数据分析, 我们得出以下假设.
在采取适当的行动之前, 我们可能会进一步验证这些假设.
**Correlating（相关）.**
我们想知道每个特征与生存相关的程度.
我们希望在项目早期做到这一点, 并将这些快速相关性与项目后期的模型相关性相匹配.
**Completing（完整）.**
1. 我们可能想要去补全丢失的 Age（年龄）特征，因为它肯定与生存相关.
2. 我们也想要去补全丢失的 Embarked（出发港）特征, 因为它也可能与生存或者其它重要的特征相关联.
**Correcting（校正）.**
1. Ticket（船票号码）特征可能会从我们的分析中删除, 因为它包含了很高的重复比例 (22%), 并且票号和生存之间可能没有关联.
2. Cabin（房间号）特征可能因为高度不完整而丢失, 或者在 训练和测试数据集中都包含许多 null 值.
3. PassengerId（旅客ID）可能会从训练数据集中删除, 因为它对生存来说没有贡献.
4. Name（名称）特征是比较不规范的, 可能不直接影响生产, 所以也许会删除.
**Creating（创建）.**
1. 我们可能希望创建一个名为 Family 的基于 Parch 和 SibSp 的新特征，以获取船上家庭成员的总数.
2. 我们可能想要设计 Name 功能以将 Title 抽取为新特征.
3. 我们可能要为 Age（年龄）段创建新的特征. 这将一个连续的数字特征转变为一个顺序的分类特征.
4. 如果它有助于我们的分析, 我们也可能想要创建 Fare（票价）范围的特征。
**Classifying（分类）.**
根据前面提到的问题描述, 我们也可以增加我们的假设.
1. Women (Sex=female) 更有可能幸存下来.
2. Children (Age 0.5）. 我们决定在我们的模型中包含这个特征.
- **Sex** 在 Sex=female（性别=女性）的问题定义中确认了74％（分类＃1）的幸存率非常高的观察意见.
- **SibSp and Parch** 这些特征对于某些值具有零相关性. 从这些单独的特征（创建＃1）派生一个特征或一组特征可能是最好的
```python
train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)
```
Pclass
Survived
0
1
0.629630
1
2
0.472826
2
3
0.242363
```python
train_df[["Sex", "Survived"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)
```
Sex
Survived
0
female
0.742038
1
male
0.188908
```python
train_df[["SibSp", "Survived"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)
```
SibSp
Survived
1
1
0.535885
2
2
0.464286
0
0
0.345395
3
3
0.250000
4
4
0.166667
5
5
0.000000
6
8
0.000000
```python
train_df[["Parch", "Survived"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)
```
Parch
Survived
3
3
0.600000
1
1
0.550847
2
2
0.500000
0
0
0.343658
5
5
0.200000
4
4
0.000000
6
6
0.000000
## 通过可视化数据进行分析
现在我们可以继续使用可视化分析数据来确认我们的一些假设.
### 关联数值的特征
让我们从理解数值的特征和解决方案目标（生存）之间的相关性开始.
柱状图可用于分析连续的数字变量，如 Age（年龄），其中条带或范围将有助于识别有用的模式.
直方图可以使用自动定义的 bins 或等分范围的 bins 来说明样本的分布.
这有助于我们回答有关特定频段的问题（婴儿有更好的幸存率吗？）
请注意，直方图可视化中的 x 轴表示样本或旅客的数量.
**Observations（观察）.**
- 婴儿（4 岁以下）存活率高.
- 最老的乘客（年龄= 80）幸存下来.
- 大量的 15-25 岁的孩子没有幸.
- 大多数乘客在 15-35 年龄范围内.
**Decisions（决策）.**
这个简单的分析证实了我们的假设, 作为后续工作流程阶段的决策.
- 在我们的模型训练中, 我们应该考虑年龄（我们假设分类＃2）.
- 完成空值的年龄功能（完成＃1）.
- 我们应该 band（组合）年龄组（创建＃3）.
```python
g = sns.FacetGrid(train_df, col='Survived')
g.map(plt.hist, 'Age', bins=20)
```
### 关联数字和顺序的特征
我们可以结合多个特征使用一个图来确定其相关性.
这可以通过具有数字值的数字和分类特征来完成。
**Observations（观察）.**
- Pclass=3 拥有最多的乘客，但大多数没有生存. 确认我们的分类假设 ＃2.
- Pclass=2 和 Pclass = 3 的婴儿乘客大多存活. 进一步限定了我们的分类假设 ＃2.
- Pclass=1 的大多数乘客幸存下来。 确认我们的分类假设 ＃3。
- Pclass 在乘客的年龄分布方面有所不同.
**Decisions（决策）.**
- 考虑 Pclass 用于模型训练.
```python
# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')
grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=.5, bins=20)
grid.add_legend();
```
### 关联分类特征
现在我们可以将分类特征与我们的解决方案目标关联起来.
**Observations（观察）.**
- Female（女性）旅客的幸存率比 male（男性）好得多. 确认分类（＃1）。
- Embarked= C 的例外, 其中男性的成活率较高. 这可能是 Pclass 和 Embarked 之间的相关性, 反过来, Pclass 和 Survived 之间, 不一定是进入和生存直接相关。
- 与 C 和 Q 港口的 Pclass = 2 相比, Pclass = 3 时男性的生存率更高. 完成（＃2）。
- 出发港口的 Pclass=3 和男性乘客的生存率不同. 相关（＃1）。
**Decisions（决策）.**
- 增加 Sex 特征以用于模型训练.
- 补全丢失值并添加 Embarked 特征以用于模型训练.
```python
# grid = sns.FacetGrid(train_df, col='Embarked')
grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)
grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')
grid.add_legend()
```
### 关联分类和数值的特征
我们也可能想要关联分类特征（非数值的）和数值的特征.
我们可以考虑将 Embarked（类别非数字）, Sex（类别非数字）, Fare（数字连续）与生存（分类数字）相关联.
**Observations（观察）.**
- Higher fare paying passengers had better survival. Confirms our assumption for creating (#4) fare ranges.
- Port of embarkation correlates with survival rates. Confirms correlating (#1) and completing (#2).
- 更高的票价付费旅客有更好的生存. 证实我们对创造（＃4）票价范围的假设.
- 搭乘港口与生存率相关. 确认关联（＃1）和完成（＃2）.
**Decisions（决策）.**
- 考虑 banding（绑定）票价功能
```python
# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})
grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)
grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)
grid.add_legend()
```
## 整理数据
我们收集了关于我们的数据集和解决方案要求的一些假设和决策.
到目前为止, 我们没有必要改变一个单个的特征或值来达到目标.
让我们现在执行我们的决定和假设来 correcting(校正), creating（创建）和 completing（完整）目标.
### 通过删除特征进行校正
这是一个很好的开始执行目标. 通过丢弃特征, 我们正在处理更少的数据点. 加快我们的 notebook, 并简化分析.
根据我们的假设和决策, 我们要放弃 Cabin（房间号）（更正＃2）和 Ticket（票号）（更正＃1）的特征.
请注意, 如果适用, 我们将对训练和测试数据集进行操作, 以保持一致.
```python
print("Before", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)
train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)
test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)
combine = [train_df, test_df]
"After", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape
```
Before (891, 12) (418, 11) (891, 12) (418, 11)
('After', (891, 10), (418, 9), (891, 10), (418, 9))
### 从现在的提取以创建性特征
我们想要分析一下, Name 特征是否可以被设计来提取 titles（头衔）和 test（测试）头衔和 survival（生存）之间的相关性, 然后再删除Name 和 PassengerId 特征.
在下面的代码中, 我们使用正则表达式提取 Title 特征. 正则表达式`(\w+\.)`匹配 Name 特征中以点号字符结尾的第一个单词.
`expand = False` 标志返回一个 DataFrame.
**Observations（观察）.**
当我们绘制出 Title, Age 和 Survived 的图时, 我们可以发现以下观察.
- 大多数 titles band 年龄组准确. 例如: 硕士学位的年龄平均为 5 年。
- Title 中的生存年龄段略有不同.
- 某些 Title 大多存活（夫人, 女士, 先生）或不（Don, Rev, Jonkheer）.
**Decision（决策）.**
- 我们决定保留模型训练的新 Title 特征.
```python
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
```
Sex
female
male
Title
Capt
0
1
Col
0
2
Countess
1
0
Don
0
1
Dr
1
6
Jonkheer
0
1
Lady
1
0
Major
0
2
Master
0
40
Miss
182
0
Mlle
2
0
Mme
1
0
Mr
0
517
Mrs
125
0
Ms
1
0
Rev
0
6
Sir
0
1
我们可以用更常见的头衔来替换很多头衔, 或者将它们分类为 `Rare`.
```python
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\
'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()
```
Title
Survived
0
Master
0.575000
1
Miss
0.702703
2
Mr
0.156673
3
Mrs
0.793651
4
Rare
0.347826
我们可以将 titles（头衔）转换为顺序的.
```python
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df.head()
```
PassengerId
Survived
Pclass
Name
Sex
Age
SibSp
Parch
Fare
Embarked
Title
0
1
0
3
Braund, Mr. Owen Harris
male
22.0
1
0
7.2500
S
1
1
2
1
1
Cumings, Mrs. John Bradley (Florence Briggs Th...
female
38.0
1
0
71.2833
C
3
2
3
1
3
Heikkinen, Miss. Laina
female
26.0
0
0
7.9250
S
2
3
4
1
1
Futrelle, Mrs. Jacques Heath (Lily May Peel)
female
35.0
1
0
53.1000
S
3
4
5
0
3
Allen, Mr. William Henry
male
35.0
0
0
8.0500
S
1
现在我们可以放心地从训练和测试数据集中删除 Name 特征.
我们也不需要训练数据集中的 PassengerId 特征.
```python
train_df = train_df.drop(['Name', 'PassengerId'], axis=1)
test_df = test_df.drop(['Name'], axis=1)
combine = [train_df, test_df]
train_df.shape, test_df.shape
```
((891, 9), (418, 9))
### 转换分类的特征
现在我们可以将包含字符串的特征转换为数字值.
这是大多数模型算法所要求的.
这样做也将帮助我们实现特征完成目标.
让我们开始将 Sex（性别）特征转换为名为 Gender（性别）的新特征, 其中 female=1, male=0.
```python
for dataset in combine:
dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)
train_df.head()
```
Survived
Pclass
Sex
Age
SibSp
Parch
Fare
Embarked
Title
0
0
3
0
22.0
1
0
7.2500
S
1
1
1
1
1
38.0
1
0
71.2833
C
3
2
1
3
1
26.0
0
0
7.9250
S
2
3
1
1
1
35.0
1
0
53.1000
S
3
4
0
3
0
35.0
0
0
8.0500
S
1
### 完整化数值字连续特征
现在我们应该开始估计和完成缺少或空值的特征.
我们将首先为 Age（年龄）特征执行此操作.
我们可以考虑三种方法来完整化一个数值连续的特征.
1.简单的方法是在平均值和 [标准偏差](https://en.wikipedia.org/wiki/Standard_deviation) 之间生成随机数.
2.更准确地猜测缺失值的方法是使用其他相关特征. 在我们的例子中, 我们注意到 Age（年龄）, Sex（性别）和 Pclass 之间的相关性. 猜测年龄值使用 [中位数](https://en.wikipedia.org/wiki/Median) Age 中的各种 Pclass 和 Gender 特征组合的值. 因此, Pclass=1 和 Gender=0，Pclass=1 和 Gender=1 的年龄中位数等等...
3.结合方法 1 和 2. 因此. 不要根据中位数来猜测年龄值, 而应根据 Pclass 和 Sex 组合, 使用平均数和标准差之间的随机数.
方法 1 和 3 将在我们的模型中引入随机噪声. 多次执行的结果可能会有所不同. 我们更喜欢方法 2.
```python
# grid = sns.FacetGrid(train_df, col='Pclass', hue='Gender')
grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=.5, bins=20)
grid.add_legend()
```
让我们开始准备一个空数组, 以包含基于 Pclass x Gender 组合以猜测 Age 值.
```python
guess_ages = np.zeros((2,3))
guess_ages
```
array([[ 0., 0., 0.],
[ 0., 0., 0.]])
现在我们迭代 Sex（0 或 1）和 Pclass（1, 2, 3）来计算 6 个组合的 Age 的猜测值.
```python
for dataset in combine:
for i in range(0, 2):
for j in range(0, 3):
guess_df = dataset[(dataset['Sex'] == i) & \
(dataset['Pclass'] == j+1)]['Age'].dropna()
# age_mean = guess_df.mean()
# age_std = guess_df.std()
# age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)
age_guess = guess_df.median()
# Convert random age float to nearest .5 age
guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5
for i in range(0, 2):
for j in range(0, 3):
dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\
'Age'] = guess_ages[i,j]
dataset['Age'] = dataset['Age'].astype(int)
train_df.head()
```
Survived
Pclass
Sex
Age
SibSp
Parch
Fare
Embarked
Title
0
0
3
0
22
1
0
7.2500
S
1
1
1
1
1
38
1
0
71.2833
C
3
2
1
3
1
26
0
0
7.9250
S
2
3
1
1
1
35
1
0
53.1000
S
3
4
0
3
0
35
0
0
8.0500
S
1
让我们创建年龄段并确定与 Survived 的相关性.
```python
train_df['AgeBand'] = pd.cut(train_df['Age'], 5)
train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)
```
AgeBand
Survived
0
(-0.08, 16.0]
0.550000
1
(16.0, 32.0]
0.337374
2
(32.0, 48.0]
0.412037
3
(48.0, 64.0]
0.434783
4
(64.0, 80.0]
0.090909
让我们使用年龄段的顺序值来替换 Aage.
```python
for dataset in combine:
dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0
dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1
dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2
dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3
dataset.loc[ dataset['Age'] > 64, 'Age']
train_df.head()
```
Survived
Pclass
Sex
Age
SibSp
Parch
Fare
Embarked
Title
AgeBand
0
0
3
0
1
1
0
7.2500
S
1
(16.0, 32.0]
1
1
1
1
2
1
0
71.2833
C
3
(32.0, 48.0]
2
1
3
1
1
0
0
7.9250
S
2
(16.0, 32.0]
3
1
1
1
2
1
0
53.1000
S
3
(32.0, 48.0]
4
0
3
0
2
0
0
8.0500
S
1
(32.0, 48.0]
我们不能删除 AgeBand 特征.
```python
train_df = train_df.drop(['AgeBand'], axis=1)
combine = [train_df, test_df]
train_df.head()
```
Survived
Pclass
Sex
Age
SibSp
Parch
Fare
Embarked
Title
0
0
3
0
1
1
0
7.2500
S
1
1
1
1
1
2
1
0
71.2833
C
3
2
1
3
1
1
0
0
7.9250
S
2
3
1
1
1
2
1
0
53.1000
S
3
4
0
3
0
2
0
0
8.0500
S
1
### 结合现有特征创建新特征
我们可以为 Parch 和 SibSp 结合的 FamilySize 创建一个新的特征.
这将使我们能够从我们的数据集中删除 Parch 和 SibSp.
```python
for dataset in combine:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)
```
FamilySize
Survived
3
4
0.724138
2
3
0.578431
1
2
0.552795
6
7
0.333333
0
1
0.303538
4
5
0.200000
5
6
0.136364
7
8
0.000000
8
11
0.000000
我们可以创建另一个名为 IsAlone 特征.
```python
for dataset in combine:
dataset['IsAlone'] = 0
dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1
train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()
```
IsAlone
Survived
0
0
0.505650
1
1
0.303538
让我们放弃 Parch, SibSp 和 FamilySize 特征, 转而使用 IsAlone 特征.
```python
train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)
test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)
combine = [train_df, test_df]
train_df.head()
```
Survived
Pclass
Sex
Age
Fare
Embarked
Title
IsAlone
0
0
3
0
1
7.2500
S
1
0
1
1
1
1
2
71.2833
C
3
0
2
1
3
1
1
7.9250
S
2
1
3
1
1
1
2
53.1000
S
3
0
4
0
3
0
2
8.0500
S
1
1
我们还可以创建一个结合 Pclass 和 Age 的人造特征.
```python
for dataset in combine:
dataset['Age*Class'] = dataset.Age * dataset.Pclass
train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)
```
Age*Class
Age
Pclass
0
3
1
3
1
2
2
1
2
3
1
3
3
2
2
1
4
6
2
3
5
3
1
3
6
3
3
1
7
0
0
3
8
3
1
3
9
0
0
2
### 完整化分类特征
Embarked（出发港）特征有 S, Q, C 三个基于出发港口的值.
我们的训练集有两个丢失值.
我们简单的使用最常发生的情况来填充它.
```python
freq_port = train_df.Embarked.dropna().mode()[0]
freq_port
```
'S'
```python
for dataset in combine:
dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)
train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)
```
Embarked
Survived
0
C
0.553571
1
Q
0.389610
2
S
0.339009
### 转换分类特征为数值的
我们现在可以通过创建一个新的数字港特征来转换 EmbarkedFill 特征.
```python
for dataset in combine:
dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)
train_df.head()
```
Survived
Pclass
Sex
Age
Fare
Embarked
Title
IsAlone
Age*Class
0
0
3
0
1
7.2500
0
1
0
3
1
1
1
1
2
71.2833
1
3
0
2
2
1
3
1
1
7.9250
0
2
1
3
3
1
1
1
2
53.1000
0
3
0
2
4
0
3
0
2
8.0500
0
1
1
6
### 快速完整化兵转换数值的特征
现在，我们可以在测试数据集使用模式下为单个缺失值完整化票价特征, 以获取此特征最常出现的值. 我们用一行代码来完成.
请注意, 我们并没有创建中间用的新特征, 也没有对相关性进行任何进一步的分析以猜测丢失的特征, 因为我们只替换单个值. 完成目标达到了模型算法对非空值操作的期望要求.
我们可能还想把票价四舍五入到小数点后两位, 因为它代表货币.
```python
test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)
test_df.head()
```
PassengerId
Pclass
Sex
Age
Fare
Embarked
Title
IsAlone
Age*Class
0
892
3
0
2
7.8292
2
1
1
6
1
893
3
1
2
7.0000
0
3
0
6
2
894
2
0
3
9.6875
2
1
1
6
3
895
3
0
1
8.6625
0
1
1
3
4
896
3
1
1
12.2875
0
3
0
3
我们不创建 FareBand 特征.
```python
train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)
train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)
```
FareBand
Survived
0
(-0.001, 7.91]
0.197309
1
(7.91, 14.454]
0.303571
2
(14.454, 31.0]
0.454955
3
(31.0, 512.329]
0.581081
将 Fare 特征转换为基于 FareBand 的顺序值.
```python
for dataset in combine:
dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0
dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1
dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2
dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3
dataset['Fare'] = dataset['Fare'].astype(int)
train_df = train_df.drop(['FareBand'], axis=1)
combine = [train_df, test_df]
train_df.head(10)
```
Survived
Pclass
Sex
Age
Fare
Embarked
Title
IsAlone
Age*Class
0
0
3
0
1
0
0
1
0
3
1
1
1
1
2
3
1
3
0
2
2
1
3
1
1
1
0
2
1
3
3
1
1
1
2
3
0
3
0
2
4
0
3
0
2
1
0
1
1
6
5
0
3
0
1
1
2
1
1
3
6
0
1
0
3
3
0
1
1
3
7
0
3
0
0
2
0
4
0
0
8
1
3
1
1
1
0
3
0
3
9
1
2
1
0
2
1
3
0
0
并且测试数据集也一样.
```python
test_df.head(10)
```
PassengerId
Pclass
Sex
Age
Fare
Embarked
Title
IsAlone
Age*Class
0
892
3
0
2
0
2
1
1
6
1
893
3
1
2
0
0
3
0
6
2
894
2
0
3
1
2
1
1
6
3
895
3
0
1
1
0
1
1
3
4
896
3
1
1
1
0
3
0
3
5
897
3
0
0
1
0
1
1
0
6
898
3
1
1
0
2
2
1
3
7
899
2
0
1
2
0
1
0
2
8
900
3
1
1
0
1
3
1
3
9
901
3
0
1
2
0
1
0
3
## 模型, 预测和解决方案
现在我们准备训练模型并通过训练得到的模型预测结果。有60多种用于预测的模型可供选择。我们必须了解问题的类型和解决方案的要求，将模型数量缩小到少数几个。我们的问题是分类和回归问题，因为需要确定输出（生存与否）与其他变量或特征（性别，年龄，港口...）之间的关系。此外，我们的问题应该属于监督学习，因为我们用已知类别的数据集来训练我们的模型。有了监督学习、分类和回归这两个标准，我们可以将模型选择的范围缩小到几个。这些包括：
- Logistic回归
- KNN或K—近邻
- 支持向量机
- 朴素贝叶斯分类器
- 决策树
- 随机森林
- 感知器
- 人工神经网络
- 相关向量机
```python
X_train = train_df.drop("Survived", axis=1)
Y_train = train_df["Survived"]
X_test = test_df.drop("PassengerId", axis=1).copy()
X_train.shape, Y_train.shape, X_test.shape
```
((891, 8), (891,), (418, 8))
Logistic回归形式简单，易于建模，适合用于早期的工作流程。Logistics回归使用线性回归模型的预测结果去逼近真实标记的对数几率，形式为参数化的Logistics分布。参考维基百科[Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression).
注意模型产生的“置信度评分”是基于训练集的。
```python
# Logistic Regression
logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict(X_test)
acc_log = round(logreg.score(X_train, Y_train) * 100, 2)
acc_log
```
80.359999999999999
我们可以使用Logistic回归来验证我们之前对特征的创建所做的假设。这可以通过计算决策函数中的特征的系数来完成。
系数为正说明该特征增加了结果的对数几率（因而增加了概率），系数为负说明该特征降低了结果的对数几率（从而降低了概率）
- Sex特征有最高的正系数，意味着当Sex从男（0）变成女（1）时，Survived = 1的概率增加最多。
- 相反地，随着Pclass特征的增加，Survived = 1的概率减少的最多。
- Age * Class是一个很好的人造特征，因为它与Survived具有次高的负相关性。
- Title特征有第二高的正相关系数。
```python
coeff_df = pd.DataFrame(train_df.columns.delete(0))
coeff_df.columns = ['Feature']
coeff_df["Correlation"] = pd.Series(logreg.coef_[0])
coeff_df.sort_values(by='Correlation', ascending=False)
```
Feature
Correlation
1
Sex
2.201527
5
Title
0.398234
2
Age
0.287164
4
Embarked
0.261762
6
IsAlone
0.129140
3
Fare
-0.085150
7
Age*Class
-0.311199
0
Pclass
-0.749006
接下来，我们使用支持向量机（SVM）模型。支持向量机是一个监督学习模型，它使用相关学习算法来分析数据，可以用于分类和回归问题。在二元分类的情况下，SVM算法建立一个模型，去找两类训练样本“正中间”的划分超平面，因为该划分超平面对训练样本局部扰动的“容忍性”最好。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Support_vector_machine).
注意SVM模型生成的“置信度评分”高于Logistics回归模型。
```python
# Support Vector Machines
svc = SVC()
svc.fit(X_train, Y_train)
Y_pred = svc.predict(X_test)
acc_svc = round(svc.score(X_train, Y_train) * 100, 2)
acc_svc
```
83.840000000000003
在模式识别中，k-近邻算法（简称k-NN）是一种用于分类和回归的无参数方法。测试样本找出训练集中与其最靠近的k个训练样本，选择这k个样本中出现最多的类别标记作为预测结果（k是一个正整数，通常很小）。如果k = 1，则该对象的类别和最近邻样本的类别一致。 参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm).
KNN的“置信度评分”比Logistics回归好，但比SVM差。
```python
knn = KNeighborsClassifier(n_neighbors = 3)
knn.fit(X_train, Y_train)
Y_pred = knn.predict(X_test)
acc_knn = round(knn.score(X_train, Y_train) * 100, 2)
acc_knn
```
84.739999999999995
在机器学习中，朴素贝叶斯分类器是一个基于所有特征互相独立的贝叶斯理论的简单概率分类器。朴素贝叶斯分类器具有高度可扩展性，在学习过程中需要大量的线性特征作为参数。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier).
该模型生成的“置信度评分”是目前模型中最低的。
```python
# Gaussian Naive Bayes
gaussian = GaussianNB()
gaussian.fit(X_train, Y_train)
Y_pred = gaussian.predict(X_test)
acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)
acc_gaussian
```
72.280000000000001
感知器是用于二元分类器的监督学习的算法（可以决定包含一个向量的输入是否属于某个类别）。它是一种线性分类器，即一种分类算法，通过一个线性预测函数将一组权重与特征向量组合来进行预测。该算法允许在线学习，因为它在一次迭代中只处理一个训练集中的元素。 参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Perceptron).
```python
# Perceptron
perceptron = Perceptron()
perceptron.fit(X_train, Y_train)
Y_pred = perceptron.predict(X_test)
acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)
acc_perceptron
```
D:\Anaconda\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
"and default tol will be 1e-3." % type(self), FutureWarning)
78.0
```python
# Linear SVC
linear_svc = LinearSVC()
linear_svc.fit(X_train, Y_train)
Y_pred = linear_svc.predict(X_test)
acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)
acc_linear_svc
```
79.120000000000005
```python
# Stochastic Gradient Descent
sgd = SGDClassifier()
sgd.fit(X_train, Y_train)
Y_pred = sgd.predict(X_test)
acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)
acc_sgd
```
D:\Anaconda\Anaconda3\lib\site-packages\sklearn\linear_model\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
"and default tol will be 1e-3." % type(self), FutureWarning)
80.019999999999996
该模型使用决策树作为预测模型，将特征（树的分支）映射到决策结果（树的叶结点）。目标变量是有限的一组值的树称为分类树; 在这些树结构中，叶结点对应于决策结果，其他每个结点对应于一个属性测试，每个结点包含的样本集合根据属性测试的结果被划分到子结点中。目标变量可以取连续值（通常是实数）的决策树称为回归树。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning).
该模型的“置信度评分”是目前模型中最高的。
```python
# Decision Tree
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, Y_train)
Y_pred = decision_tree.predict(X_test)
acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)
acc_decision_tree
```
86.760000000000005
随机森林是最流行的模型之一。随机森林或随机决策树森林是一种用于分类，回归或其他任务的集成学习模型，它通过在训练时构造大量的决策树（n_estimators = 100），再使用某种策略将这些“个体学习器”结合起来。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Random_forest).
该模型的“置信度评分”是目前模型中最高的。我们决定使用这个模型的输出（Y_pred）来作为竞赛结果。
```python
# Random Forest
random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, Y_train)
Y_pred = random_forest.predict(X_test)
random_forest.score(X_train, Y_train)
acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)
acc_random_forest
```
86.760000000000005
### 模型评估
现在, 我们可以对所有模型进行评估, 为我们的问题选择最好的模型。
虽然决策树和随机森林评分相同, 但我们选择使用随机森林，因为随机森林会校正决策树“过拟合”的缺点。
```python
models = pd.DataFrame({
'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression',
'Random Forest', 'Naive Bayes', 'Perceptron',
'Stochastic Gradient Decent', 'Linear SVC',
'Decision Tree'],
'Score': [acc_svc, acc_knn, acc_log,
acc_random_forest, acc_gaussian, acc_perceptron,
acc_sgd, acc_linear_svc, acc_decision_tree]})
models.sort_values(by='Score', ascending=False)
```
Model
Score
3
Random Forest
86.76
8
Decision Tree
86.76
1
KNN
84.74
0
Support Vector Machines
83.84
2
Logistic Regression
80.36
6
Stochastic Gradient Decent
80.02
7
Linear SVC
79.12
5
Perceptron
78.00
4
Naive Bayes
72.28
```python
submission = pd.DataFrame({
"PassengerId": test_df["PassengerId"],
"Survived": Y_pred
})
# submission.to_csv('../output/submission.csv', index=False)
```
我们提交给竞赛网站 Kaggle 的比赛结果在 6,082 个参赛作品中获得 3883 名.
当竞赛正在进行时，这个结果是具有指导意义的.
这个结果只占提交数据集的一部分.
对我们的第一次尝试是不错的.
欢迎任何提高我们的分数的建议.
## 参考文献
该手册是基于完成解决《泰坦尼克号》竞赛和其它来源的伟大工作而创建的.
- [泰坦尼克号之旅](https://www.kaggle.com/omarelgabry/titanic/a-journey-through-titanic)
- [ Pandas 入门指南: Kaggle 的泰坦尼克号竞赛](https://www.kaggle.com/c/titanic/details/getting-started-with-random-forests)
- [泰坦尼克号的最佳处理分类器](https://www.kaggle.com/sinakhorami/titanic/titanic-best-working-classifier)
================================================
FILE: docs/Kaggle/competitions/getting-started/titanic/titanic_yy.md
================================================
# Titanic 第一次试水 | 瑶瑶亲卫队
* fork from https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python
* 开源组织: [ApacheCN ~ apachecn.org](http://www.apachecn.org)
* team：瑶瑶亲卫队
* 鸣谢：咸鱼大佬，帮忙调试错误，并上传
我们主要根据这个 [kernel](https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python) 写的，当然生成的 submission ，已经提交到 kaggle 上评分出来了。
## 目录
* 1、加载我们要用到的库
* 2、特征工程
* 3、可视化
* 4、整合模型
* 5、生成基本的模型
* 6、训练模型
* 7、生成 submission 文件
## 1、加载我们要用到的库
```python
# 加载我们的库
import pandas as pd
import numpy as np
# 使用 re 写正则匹配
import re
# 使用 sklearn 的模型来进行预测
import sklearn
# 使用 seaborn 进行可视化
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls
import warnings
warnings.filterwarnings('ignore')
# 我们使用 5 种模型来预测
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier
from sklearn.svm import SVC
from sklearn.cross_validation import KFold
```
## 2、特征工程
### 2.1、初步查看
```python
# 加载 train 和 test 数据集
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
# 存储 Passenger ID 方便后续操作
PassengerId = test['PassengerId']
train.head(3)
```
是下面这样(NaN 不是咱们想要的结果)：
### 2.2、加入一些特征，删除一些特征，映射一些特征
```python
# 整体的全部数据
full_data = [train, test]
# 一些从原始 features 中衍生出的 features ，我认为可以算是一个 feature
# Name_length 特征
train['Name_length'] = train['Name'].apply(len)
test['Name_length'] = test['Name'].apply(len)
# 在 Titanic 上是否具有一个 cabin
train['Has_Cabin'] = train["Cabin"].apply(lambda x: 0 if type(x) == float else 1)
test['Has_Cabin'] = test["Cabin"].apply(lambda x: 0 if type(x) == float else 1)
# 创建一个新的 feature FamilySize 作为 SibSp 和 Parch 的混合 features
for dataset in full_data:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
# 从 FamilySize 特征中 创建一个新的 feature IsAlone
for dataset in full_data:
dataset['IsAlone'] = 0
dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1
# 删除 Embarked 列中的所有的 NULLS值，使用 S 来填充（算是超级暴力的吧）
for dataset in full_data:
dataset['Embarked'] = dataset['Embarked'].fillna('S')
# 删除 Fare 中所有的 NULLS 值，并生成一个新的 feature 列 CategoricalFare
for dataset in full_data:
dataset['Fare'] = dataset['Fare'].fillna(train['Fare'].median())
train['CategoricalFare'] = pd.qcut(train['Fare'], 4)
# 创建一个新特征 CategoricalAge
for dataset in full_data:
age_avg = dataset['Age'].mean()
age_std = dataset['Age'].std()
age_null_count = dataset['Age'].isnull().sum()
age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)
dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list
dataset['Age'] = dataset['Age'].astype(int)
train['CategoricalAge'] = pd.cut(train['Age'], 5)
# 定义函数从 passenger 名字中获取 titles
def get_title(name):
title_search = re.search(' ([A-Za-z]+)\.', name)
# 如果 title 存在，返回
if title_search:
return title_search.group(1)
return ""
# 创建一个新的特征 Title, 包含乘客的名字的 titles
for dataset in full_data:
dataset['Title'] = dataset['Name'].apply(get_title)
# 将所有的非常见的 title 分类到一个 “Rare” 特征
for dataset in full_data:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
# 将数据集中的数据映射成离散型数据
for dataset in full_data:
# 映射 Sex
dataset['Sex'] = dataset['Sex'].map( {'female': 0, 'male': 1} ).astype(int)
# 映射 titles
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
# 映射 Embarked
dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)
# 映射 Fare
dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0
dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1
dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2
dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3
dataset['Fare'] = dataset['Fare'].astype(int)
# 映射 Age
dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0
dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1
dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2
dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3
dataset.loc[ dataset['Age'] > 64, 'Age'] = 4
```
选择一些特征：
```python
# Feature selection
drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp']
train = train.drop(drop_elements, axis = 1)
train = train.drop(['CategoricalAge', 'CategoricalFare'], axis = 1)
test = test.drop(drop_elements, axis = 1)
```
### 2.3、将新的数据特征存储起来（没有必要，我这儿闲的蛋疼）
我将这些新的数据特征存储起来了，其实完全没有必要。
```python
# 生成一个临时文件，专门存储我们已经转化/映射完成之后的 train 和 test 文件，我写的很烂，，，
def saveTmpTrainFile(tmpFile,csvName):
with open(csvName, 'w', newline='') as myFile:
myWriter=csv.writer(myFile)
myWriter.writerow(["Survived","Pclass","Sex","Age","Parch","Fare","Embarked","Name_length","Has_cabin","FamilySize","IsAlone","Title"])
for lines in tmpFile.index:
tmp=[]
tmp.append(tmpFile.loc[lines].values[1])
tmp.append(tmpFile.loc[lines].values[2])
tmp.append(tmpFile.loc[lines].values[4])
tmp.append(tmpFile.loc[lines].values[5])
tmp.append(tmpFile.loc[lines].values[7])
tmp.append(tmpFile.loc[lines].values[9])
tmp.append(tmpFile.loc[lines].values[11])
tmp.append(tmpFile.loc[lines].values[12])
tmp.append(tmpFile.loc[lines].values[13])
tmp.append(tmpFile.loc[lines].values[14])
tmp.append(tmpFile.loc[lines].values[15])
tmp.append(tmpFile.loc[lines].values[-1])
myWriter.writerow(tmp)
saveTmpTrainFile(train,'D:/titanic/titanic_dataset/train_later.csv')
# 生成一个临时文件，专门存储我们已经转化/映射完成之后的 train 和 test 文件
def saveTmpTestFile(tmpFile,csvName):
with open(csvName, 'w', newline='') as myFile:
myWriter=csv.writer(myFile)
myWriter.writerow(["Pclass","Sex","Age","Parch","Fare","Embarked","Name_length","Has_cabin","FamilySize","IsAlone","Title"])
for lines in tmpFile.index:
tmp=[]
tmp.append(tmpFile.loc[lines].values[1])
tmp.append(tmpFile.loc[lines].values[3])
tmp.append(tmpFile.loc[lines].values[4])
tmp.append(tmpFile.loc[lines].values[6])
tmp.append(tmpFile.loc[lines].values[8])
tmp.append(tmpFile.loc[lines].values[10])
tmp.append(tmpFile.loc[lines].values[11])
tmp.append(tmpFile.loc[lines].values[12])
tmp.append(tmpFile.loc[lines].values[13])
tmp.append(tmpFile.loc[lines].values[14])
tmp.append(tmpFile.loc[lines].values[15])
myWriter.writerow(tmp)
# 存储 test 文件
saveTmpFile(test,'D:/titanic/titanic_dataset/test_later.csv')
```
## 3、可视化
我们处理完成之后的 train 数据集为：
让我们生成一些特征的关联图，以查看一个特征与下一个特征之间的相关性。 为此，我们将利用 Seaborn 绘图软件包，使我们能够非常方便地绘制热图，如下所示：
```python
```
皮尔逊相关图可以告诉我们的一件事是，没有太多的特征与彼此强相关。 从将这些特征提供给您的学习模型的角度来看，这是很好的，因为这意味着我们的训练集中没有太多冗余或多余的数据，我们很高兴每个特征都带有一些独特的信息。 这里有两个最相关的功能是 FamilySize 和 Parch（家长和儿童）。但是这儿没有删除，依然留着。
## 4、整合模型
我们这儿使用一个 SklearnHelper 类，来方便我们的面向对象编程，它允许扩展所有 Sklearn 分类器共有的内置方法（如 train, predict 和 fit）。因此，如果我们想要调用五个不同的分类器，就可以省去五次冗余。
```python
# 加载我们生成的 train 和 test 文件
# # -----------注意一下哈，这个地方，需要调整一下生成的文件-------------------------------------------------------------
train_later = pd.read_csv('D:/titanic/titanic_dataset/train_later.csv')
test_later = pd.read_csv('D:/titanic/titanic_dataset/test_later.csv')
# train.head(3)
# test.head(3)
# # 使用 seaborn 将 特征的 Person 系数画出来
# colormap = plt.cm.RdBu
# plt.figure(figsize=(14,12))
# plt.title('Pearson Correlation of Features', y=1.05, size=15)
# sns.heatmap(train.astype(float).corr(),linewidths=0.1,vmax=1.0,
# square=True, cmap=colormap, linecolor='white', annot=True)
# 一些比较有用参数，后边会派上用场
ntrain = train_later.shape[0]
ntest = test_later.shape[0]
SEED = 0 # 重现性
NFOLDS = 5 # 设置交叉验证的折数，以便后面的 K 折交叉验证
kf = KFold(ntrain, n_folds= NFOLDS, random_state=SEED)
# sklearn 的分类器
class SklearnHelper(object):
def __init__(self, clf, seed=0, params=None):
params['random_state'] = seed
self.clf = clf(**params)
def train(self, x_train, y_train):
self.clf.fit(x_train, y_train)
def predict(self, x):
return self.clf.predict(x)
def fit(self,x,y):
return self.clf.fit(x,y)
def feature_importances(self,x,y):
print(self.clf.fit(x,y).feature_importances_)
# 获取最后的 预测结果
def get_oof(clf, x_train, y_train, x_test):
oof_train = np.zeros((ntrain,))
oof_test = np.zeros((ntest,))
oof_test_skf = np.empty((NFOLDS, ntest))
for i, (train_index, test_index) in enumerate(kf):
x_tr = x_train[train_index]
y_tr = y_train[train_index]
x_te = x_train[test_index]
clf.train(x_tr, y_tr)
oof_train[test_index] = clf.predict(x_te)
oof_test_skf[i, :] = clf.predict(x_test)
oof_test[:] = oof_test_skf.mean(axis=0)
return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)
```
## 5、生成基本模型
以下是 sklearn 中的 5 个模型，都可以很方便的调用
* Random Forest classifier --- Score 0.78947
* Extra Trees classifier --- Score 0.79425
* AdaBoost classifer --- Score 0.74162
* Gradient Boosting classifer --- Score 0.77033
* Support Vector Machine --- Score 0.77990
参数：
* n_jobs：用于训练过程的 core 的数量。如果设置为-1，则使用所有内核。
* n_estimators：学习模型中分类树的数量（默认设置为10）
* max_depth：树的最大深度，或者应该展开多少节点。要小心，如果设置得太高，就会有过度配合的风险，因为会使树太深
* verbose：控制在学习过程中是否要输出任何文本。值为0会抑制所有的文本，而值3会在每次迭代时输出树学习过程。
详细的参数介绍，请参考 [sklearn 官方文档](http://sklearn.apachecn.org/cn/0.19.0/)。
```python
# 将参数加载到调用的分类器中，进行调试
# Random Forest 的参数
rf_params = {
'n_jobs': -1,
'n_estimators': 500,
'warm_start': True,
#'max_features': 0.2,
'max_depth': 6,
'min_samples_leaf': 2,
'max_features' : 'sqrt',
'verbose': 0
}
# Extra Trees 的参数
et_params = {
'n_jobs': -1,
'n_estimators':500,
#'max_features': 0.5,
'max_depth': 8,
'min_samples_leaf': 2,
'verbose': 0
}
# AdaBoost 的参数
ada_params = {
'n_estimators': 500,
'learning_rate' : 0.75
}
# Gradient Boosting 的参数
gb_params = {
'n_estimators': 500,
#'max_features': 0.2,
'max_depth': 5,
'min_samples_leaf': 2,
'verbose': 0
}
# Support Vector Classifier 的参数
svc_params = {
'kernel' : 'linear',
'C' : 0.025
}
```
## 6、训练模型
```python
# 创建代表模型的 5 个对象
rf = SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=rf_params)
et = SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)
ada = SklearnHelper(clf=AdaBoostClassifier, seed=SEED, params=ada_params)
gb = SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=gb_params)
svc = SklearnHelper(clf=SVC, seed=SEED, params=svc_params)
# 创建 Numpy arrays 来代表 train, test 和 target
y_train = train['Survived'].ravel()
train = train.drop(['Survived'], axis=1)
x_train = train.values # 创建 train 的 array
x_test = test.values # 创建 test 的 array
# 创建代表模型的 5 个对象
rf = SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=rf_params)
et = SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)
ada = SklearnHelper(clf=AdaBoostClassifier, seed=SEED, params=ada_params)
gb = SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=gb_params)
svc = SklearnHelper(clf=SVC, seed=SEED, params=svc_params)
# 创建 Numpy arrays 来代表 train, test 和 target
y_train = train['Survived'].ravel()
train = train.drop(['Survived'], axis=1)
x_train = train.values # 创建 train 的 array
x_test = test.values # 创建 test 的 array
# 创建您的 OOF 的 train 和 test 预测
et_oof_train, et_oof_test = get_oof(et, x_train, y_train, x_test) # Extra Trees
rf_oof_train, rf_oof_test = get_oof(rf,x_train, y_train, x_test) # Random Forest
ada_oof_train, ada_oof_test = get_oof(ada, x_train, y_train, x_test) # AdaBoost
gb_oof_train, gb_oof_test = get_oof(gb,x_train, y_train, x_test) # Gradient Boost
svc_oof_train, svc_oof_test = get_oof(svc,x_train, y_train, x_test) # Support Vector Classifier
print("Training is complete")
```
## 7、生成 submission 结果
生成文件的位置都需要自定义以下：
```python
# 创建 submission 文件，接下来就是提交到 kaggle 页面看一下得分和排名啦
def saveResult(result,csvName):
with open(csvName,'w',newline='') as myFile:
myWriter=csv.writer(myFile)
myWriter.writerow(["PassengerId","Survived"])
index=891
for i in result:
tmp=[]
index=index+1
tmp.append(index)
tmp.append(int(i))
myWriter.writerow(tmp)
saveResult(et_oof_test,'D:/titanic/titanic_dataset/result/et.csv')
saveResult(rf_oof_test,'D:/titanic/titanic_dataset/result/rf.csv')
saveResult(ada_oof_test,'D:/titanic/titanic_dataset/result/ada.csv')
saveResult(gb_oof_test,'D:/titanic/titanic_dataset/result/gb.csv')
saveResult(svc_oof_test,'D:/titanic/titanic_dataset/result/svc.csv')
```
将生成的文件在 [这里](https://www.kaggle.com/c/titanic/submit) 提交，看一下评分。
================================================
FILE: docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/NLP电影预测.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **word2vec nlp tutorial**\n",
"\n",
"> 比赛说明\n",
"\n",
"标记的数据集由50,000条IMDB电影评论组成，专门用于情感分析。评论的情感是二元的，这意味着IMDB评分<5导致情绪分数为0，并且评分≥7的情绪评分为1.没有单独的电影具有超过30个评论。25,000个带有复审标签的训练集不包含与25,000个复习测试集相同的电影。此外，还有另外50,000个IMDB评论没有提供任何评级标签。\n",
"\n",
"> 文件说明\n",
"\n",
"| 文件 | 说明 |\n",
"| :--- | :--- |\n",
"| labeledTrainData | 标记的训练集。该文件是制表符分隔的，并且有一个标题行，后面跟着25,000行，其中包含每个审阅的ID，情绪和文本。 |\n",
"| testData | 测试集。制表符分隔的文件有一个标题行，后面跟着25,000行，其中包含每个评论的标识和文本。你的任务是预测每个人的情绪。 |\n",
"| unlabeledTrainData | 一个没有标签的额外训练集。制表符分隔的文件有一个标题行，后跟50,000行，每行包含一个标识和文本。 |\n",
"| sampleSubmission | 以正确格式的逗号分隔的示例提交文件。 |\n",
"\n",
"> 数据字段\n",
"\n",
"| 字段 | 说明 |\n",
"| :--- | :--- |\n",
"| id | 每个评论的唯一ID |\n",
"| sentiment | 审查的情绪; 1为正面评论，0为负面评论 |\n",
"| review | 审查的文本 |\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 比赛操作流程\n",
"\n",
"分类问题：预测的是好与坏的问题\n",
"常用算法： K紧邻(knn)、逻辑回归(LogisticRegression)、随机森林(RandomForest)、支持向量机(SVM)、xgboost、GBDT\n",
"\n",
"> 步骤:\n",
"\n",
"```\n",
"一. 数据分析\n",
"1. 下载并加载数据\n",
"2. 总体预览:了解每列数据的含义,数据的格式等\n",
"3. 数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备\n",
"\n",
"二. 特征工程\n",
"1.根据业务,常识,以及第二步的数据分析构造特征工程.\n",
"2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)\n",
"\n",
"三. 模型选择\n",
"1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.\n",
"2.比较各个模型的分数,然后取效果较好的模型作为基础模型.\n",
"\n",
"四. 模型融合\n",
"\n",
"五. 修改特征和模型参数\n",
"1.可以通过添加或者修改特征,提高模型的上限.\n",
"2.通过修改模型的参数,是模型逼近上限\n",
"```\n",
"\n",
"* * * \n",
"\n",
"* 比赛地址: https://www.kaggle.com/c/word2vec-nlp-tutorial\n",
"* 参考地址: https://www.cnblogs.com/zhao441354231/p/6056914.html\n",
"* 参考地址: https://blog.csdn.net/lijingpengchina/article/details/52250765\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 一.数据分析\n",
"\n",
"### 数据下载和加载\n",
"\n",
"* 数据集下载地址: https://www.kaggle.com/c/word2vec-nlp-tutorial/data\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# 导入相关数据包\n",
"import pandas as pd\n",
"import numpy as np\n",
"from bs4 import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 读取数据"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"data_dir = \"/opt/data/kaggle/getting-started/word2vec-nlp-tutorial\"\n",
"# 载入数据集 \n",
"train = pd.read_csv(os.path.join(data_dir, 'labeledTrainData.tsv'), header=0, delimiter=\"\\t\", quoting=3)\n",
"pre = pd.read_csv(os.path.join(data_dir, 'testData.tsv'), header=0, delimiter=\"\\t\", quoting=3)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(25000, 3) \t (25000, 2) \t\n",
"\n",
"RangeIndex: 25000 entries, 0 to 24999\n",
"Data columns (total 3 columns):\n",
"id 25000 non-null object\n",
"sentiment 25000 non-null int64\n",
"review 25000 non-null object\n",
"dtypes: int64(1), object(2)\n",
"memory usage: 586.0+ KB\n",
"None \n",
"\n",
"\n",
" ['id' 'sentiment' 'review']\n",
"\n",
" id sentiment review\n",
"0 \"5814_8\" 1 \"With all this stuff going down at the moment ...\n",
"1 \"2381_9\" 1 \"\\\"The Classic War of the Worlds\\\" by Timothy ...\n",
"2 \"7759_3\" 0 \"The film starts with a manager (Nicholas Bell...\n"
]
}
],
"source": [
"print(train.shape, '\\t', pre.shape, '\\t')\n",
"print(train.info(), '\\n')\n",
"print('\\n', train.columns.values)\n",
"print('\\n', train.head(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 数据预处理\n",
"\n",
"* 1.去掉html标签\n",
"* 2.移除标点\n",
"* 3.切分成词/token\n",
"* 4.去掉停用词\n",
"* 5.重组为新的句子"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# def review_to_wordlist(review):\n",
"# '''\n",
"# 把IMDB的评论转成词序列\n",
"# 参考：http://blog.csdn.net/longxinchen_ml/article/details/50629613\n",
"# '''\n",
"# # 去掉HTML标签，拿到内容\n",
"# review_text = BeautifulSoup(review, \"html.parser\").get_text()\n",
"# # 用正则表达式取出符合规范的部分\n",
"# review_text = re.sub(\"[^a-zA-Z]\", \" \", review_text)\n",
"# # 小写化所有的词，并转成词list\n",
"# words = review_text.lower().split()\n",
"# # 返回words\n",
"# return words\n",
"\n",
"\n",
"# 预处理数据\n",
"label = train['sentiment']\n",
"train_data = []\n",
"pre_data = []\n",
"for i in range(len(train['review'])):\n",
" train_data.append(BeautifulSoup(train['review'][i], \"html.parser\").get_text())\n",
"test_data = []\n",
"for i in range(len(pre['review'])):\n",
" pre_data.append(BeautifulSoup(pre['review'][i], \"html.parser\").get_text())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\"With all this stuff going down at the moment with MJ i've started listening to his music, watching the odd documentary here and there, watched The Wiz and watched Moonwalker again. Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent. Moonwalker is part biography, part feature film which i remember going to see at the cinema when it was originally released. Some of it has subtle messages about MJ's feeling towards the press and also the obvious message of drugs are bad m'kay.Visually impressive but of course this is all about Michael Jackson so unless you remotely like MJ in anyway then you are going to hate this and find it boring. Some may call MJ an egotist for consenting to the making of this movie BUT MJ and most of his fans would say that he made it for the fans which if true is really nice of him.The actual feature film bit when it finally starts is only on for 20 minutes or so excluding the Smooth Criminal sequence and Joe Pesci is convincing as a psychopathic all powerful drug lord. Why he wants MJ dead so bad is beyond me. Because MJ overheard his plans? Nah, Joe Pesci's character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno, maybe he just hates MJ's music.Lots of cool things in this like MJ turning into a car and a robot and the whole Speed Demon sequence. Also, the director must have had the patience of a saint when it came to filming the kiddy Bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene.Bottom line, this movie is for people who like MJ on one level or another (which i think is most people). If not, then stay away. It does try and give off a wholesome message and ironically MJ's bestest buddy in this movie is a girl! Michael Jackson is truly one of the most talented people ever to grace this planet but is he guilty? Well, with all the attention i've gave this subject....hmmm well i don't know because people can be different behind closed doors, i know this for a fact. He is either an extremely nice but stupid guy or one of the most sickest liars. I hope he is not the latter.\" \n",
"\n",
"\"Naturally in a film who's main themes are of mortality, nostalgia, and loss of innocence it is perhaps not surprising that it is rated more highly by older viewers than younger ones. However there is a craftsmanship and completeness to the film which anyone can enjoy. The pace is steady and constant, the characters full and engaging, the relationships and interactions natural showing that you do not need floods of tears to show emotion, screams to show fear, shouting to show dispute or violence to show anger. Naturally Joyce's short story lends the film a ready made structure as perfect as a polished diamond, but the small changes Huston makes such as the inclusion of the poem fit in neatly. It is truly a masterpiece of tact, subtlety and overwhelming beauty.\"\n"
]
}
],
"source": [
"# 预览数据\n",
"print(train_data[0], '\\n')\n",
"print(pre_data[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 特征处理"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# 合并训练和测试集以便进行TFIDF向量化操作\n",
"data_all = train_data + pre_data\n",
"len_train = len(train_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"直接丢给计算机这些词文本，计算机是无法计算的，因此我们需要把文本转换为向量，有几种常见的文本向量处理方法，比如： \n",
"\n",
"1. Bow-of-Words计数 \n",
"2. TF-IDF向量 \n",
"3. Word2vec向量 "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['have', 'yourselves', 'itself', \"haven't\", 'y', 'shan', 'because', 'didn', 'it', \"she's\", 'nor', 'once', 'hadn', 'an', 'will', 'in', 'than', 'just', \"doesn't\", 'down', \"mightn't\", 've', 'shouldn', 'before', 'when', 'and', 'won', 'which', \"wouldn't\", 'other', 'are', 'doesn', 'here', 'him', 'why', \"mustn't\", 'theirs', 'ours', 'himself', 'now', 'at', 'but', 'its', 'were', 'whom', 'how', 'again', 'under', 'myself', 'me', 'your', 'then', 'he', 'the', 'who', 'herself', 'off', 'aren', 'each', 'same', 'all', \"that'll\", 'so', 'having', 'that', 'couldn', 'she', 'wasn', 'own', \"shouldn't\", 'by', 'there', 'this', 'we', 'if', 'no', 'doing', 'don', 'ain', \"you've\", 'had', 't', 'into', 'too', 'hasn', 'they', 'few', 'their', 'being', 'mightn', \"you'd\", 'a', 'her', \"couldn't\", 'did', \"you'll\", 'd', 'can', 'been', 'm', 'yours', 'very', 'wouldn', 'i', 'his', 'during', 'through', 'you', 'against', 'be', 'themselves', 'not', 'out', \"don't\", 'is', \"it's\", 'was', 'does', 'ma', 'needn', 'these', 'some', 'on', \"isn't\", 'for', 'further', \"hadn't\", 'isn', 'below', 'more', \"didn't\", 'has', 'up', 'with', 'about', \"weren't\", 'am', 'those', 'where', 'what', 'any', 's', \"you're\", 'do', 'or', 'over', 'weren', 'my', 'until', 'as', 'most', 'only', \"should've\", 'ourselves', \"needn't\", 'haven', 'above', 'such', 'hers', \"shan't\", 'after', 'while', \"wasn't\", 'them', 'between', 'our', 'from', 'yourself', \"aren't\", 'should', 'mustn', \"hasn't\", \"won't\", 'to', 're', 'of', 'both', 'o', 'll']\n"
]
}
],
"source": [
"from nltk.corpus import stopwords\n",
"#英文停止词，set()集合函数消除重复项\n",
"list_stopWords = list(set(stopwords.words('english')))\n",
"print(list_stopWords)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"from gensim import corpora\n",
"\n",
"# bow 模型 \n",
"import re\n",
"texts = [[word for word in re.sub(\"[^a-zA-Z]\", \" \", doc.lower()) if word != \"\" and word not in list_stopWords] for doc in data_all]\n",
"dictionary = corpora.Dictionary(texts)\n",
"# 对每一行的单词，进行统计出现次数\n",
"corpus = [dictionary.doc2bow(text) for text in texts]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 \n",
"1 b\n",
"2 c\n",
"3 e\n",
"4 f\n",
"[(0, 471), (1, 29), (2, 56), (3, 206), (4, 37), (5, 42), (6, 102), (7, 19), (8, 18), (9, 82), (10, 115), (11, 31), (12, 3), (13, 72), (14, 53), (15, 15), (16, 41), (17, 3), (18, 1)]\n"
]
}
],
"source": [
"for key in dictionary.keys()[0:5]:\n",
" print (key, dictionary[key])\n",
"\n",
"print(corpus[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.TF-IDF + Lsi主题模型"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from gensim import models\n",
"tfidf_model = models.TfidfModel(corpus=corpus, id2word=dictionary, normalize=True) \n",
"# 将整个corpus转为tf-idf格式\n",
"corpus_tfidf = tfidf_model[corpus]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## lsi 主题模型，作为特征向量\n",
"lsi_model = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=200)\n",
"corpus_lsi = lsi_model[corpus_tfidf]\n",
"\n",
"# 提取数字，转化为numpy的矩阵\n",
"all_x = [[v for k,v in doc] for doc in corpus_lsi]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0, '0.162*\"movie\" + 0.140*\"film\" + 0.102*\"-\" + 0.099*\"good\" + 0.099*\"like\" + 0.098*\"really\" + 0.098*\"bad\" + 0.092*\"one\" + 0.089*\"would\" + 0.088*\"story\"'), (1, '0.276*\"bad\" + 0.238*\"movie\" + 0.180*\"worst\" + -0.156*\"-\" + 0.153*\"movies\" + 0.113*\"waste\" + 0.108*\"ever\" + 0.106*\"acting\" + 0.106*\"terrible\" + -0.101*\"film\"'), (2, '-0.667*\"show\" + -0.212*\"episode\" + -0.203*\"series\" + 0.160*\"-\" + 0.153*\"film\" + -0.146*\"season\" + -0.145*\"episodes\" + -0.135*\"tv\" + -0.130*\"shows\" + -0.125*\"funny\"')]\n",
"(50000, 200)\n"
]
}
],
"source": [
"# print(np.shape(corpus_lsi))\n",
"# (50000, 200, 2)\n",
"print(lsi_model.print_topics(3))\n",
"# print(corpus_lsi[0])\n",
"\n",
"import numpy as np\n",
"print(np.shape(all_x))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.Word2vec向量\n",
"\n",
"神经网络语言模型 L = SUM[log(p(w|contect(w))]，即在w的上下文下计算当前词w的概率，由公式可以看到，我们的核心是计算p(w|contect(w)， Word2vec给出了构造这个概率的一个方法。"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"from bs4 import BeautifulSoup\n",
"from nltk.corpus import stopwords\n",
"\n",
"# def show_diff(origin, html, text):\n",
"# print(origin)\n",
"# print(\"\\n-----------show diff-----------\\n\")\n",
"# print(html)\n",
"# print(\"\\n-----------show diff-----------\\n\")\n",
"# print(text)\n",
"\n",
"# origin = train['review'][0]\n",
"# html = BeautifulSoup(origin, \"html.parser\").get_text()\n",
"# text = re.sub('[^a-zA-Z]', ' ', html).strip()\n",
"# show_diff(origin, html, text)\n",
"\n",
"stopwords = set(stopwords.words(\"english\"))\n",
"\n",
"def review_to_sentence(review):\n",
" html = BeautifulSoup(review, \"html.parser\").get_text()\n",
" text = re.sub('[^a-zA-Z]', ' ', html).strip()\n",
" words = [word for word in text.lower().split() if word not in stopwords]\n",
" return words\n",
"\n",
"unlabeled_train = pd.read_csv(os.path.join(data_dir, 'unlabeledTrainData.tsv'), header=0, delimiter=\"\\t\", quoting=3 )\n",
"train_texts = pd.concat([train['review'], unlabeled_train['review']])\n",
"sentences = list(map(review_to_sentence, train_texts))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(75000,)\n"
]
}
],
"source": [
"print(np.shape(sentences))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"from gensim.models import Word2Vec\n",
"# 模型参数\n",
"num_features = 784 # Word vector dimensionality(原来默认用300维，为了计算CNN, 设置 784维 = 28*28) \n",
"min_word_count = 10 # Minimum word count \n",
"num_workers = 4 # Number of threads to run in parallel\n",
"context = 10 # Context window size \n",
"downsampling = 1e-3 # Downsample setting for frequent words"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"训练模型中...\n",
"训练完成\n",
"CPU times: user 7min 17s, sys: 6.85 s, total: 7min 24s\n",
"Wall time: 3min 1s\n"
]
}
],
"source": [
"%%time\n",
"# 训练模型\n",
"print(\"训练模型中...\")\n",
"# model = Word2Vec(sentences, workers=num_workers, \\\n",
"# size=num_features, min_count=min_word_count, \\\n",
"# window=5, sample=downsampling)\n",
"model = Word2Vec(sentences, size=num_features, window=5)\n",
"print(\"训练完成\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
},
{
"data": {
"text/plain": [
"(784,)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.shape(model[\"film\"])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'kitchen'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.wv.doesnt_match(\"man woman child kitchen\".split())"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'berlin'"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.wv.doesnt_match(\"france england germany berlin\".split())"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'london'"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.wv.doesnt_match(\"paris berlin london austria\".split())"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('men', 0.5552874803543091),\n",
" ('lady', 0.5526503920555115),\n",
" ('woman', 0.49917668104171753),\n",
" ('mans', 0.47213518619537354),\n",
" ('guy', 0.4668915569782257)]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.wv.most_similar(\"man\", topn=5)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('princess', 0.6967809200286865),\n",
" ('bride', 0.6197341084480286),\n",
" ('latifah', 0.6163896918296814),\n",
" ('goddess', 0.6069524884223938),\n",
" ('showgirl', 0.5752988457679749)]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.wv.most_similar(\"queen\", topn=5)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('terrible', 0.8102608919143677),\n",
" ('horrible', 0.7840115427970886),\n",
" ('dreadful', 0.7728089690208435),\n",
" ('horrid', 0.7526298761367798),\n",
" ('atrocious', 0.7394574284553528)]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.wv.most_similar(\"awful\", topn=5)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('princess', 0.4752192795276642)]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.wv.most_similar(positive=['woman', 'king'], negative=['man'], topn=1)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"def makeFeatureVec(words, model, num_features):\n",
" '''\n",
" 对段落中的所有词向量进行取平均操作\n",
" '''\n",
" featureVec = np.zeros((num_features,), dtype=\"float32\")\n",
" nwords = 0.\n",
"\n",
" # Index2word包含了词表中的所有词，为了检索速度，保存到set中\n",
" index2word_set = set(model.wv.index2word)\n",
" for word in words:\n",
" if word in index2word_set:\n",
" nwords = nwords + 1.\n",
" featureVec = np.add(featureVec, model[word])\n",
"\n",
" # 取平均\n",
" featureVec = np.divide(featureVec, nwords)\n",
" return featureVec\n",
"\n",
"\n",
"def getAvgFeatureVecs(reviews, model, num_features):\n",
" '''\n",
" 给定一个文本列表，每个文本由一个词列表组成，返回每个文本的词向量加和的平均值\n",
" '''\n",
" counter = 0\n",
" reviewFeatureVecs = np.zeros((len(reviews), num_features), dtype=\"float32\")\n",
"\n",
" for review in reviews:\n",
" if counter % 5000 == 0:\n",
" print(\"Review %d of %d\" % (counter, len(reviews)))\n",
"\n",
" reviewFeatureVecs[counter] = makeFeatureVec(review, model, num_features)\n",
" counter = counter + 1\n",
"\n",
" return reviewFeatureVecs"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6 µs, sys: 1e+03 ns, total: 7 µs\n",
"Wall time: 11.2 µs\n",
"Review 0 of 25000\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:13: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
" del sys.path[0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Review 5000 of 25000\n",
"Review 10000 of 25000\n",
"Review 15000 of 25000\n",
"Review 20000 of 25000\n",
"(25000, 784)\n"
]
}
],
"source": [
"%time \n",
"trainDataVecs = getAvgFeatureVecs(texts[:len_train], model, num_features)\n",
"print(np.shape(trainDataVecs))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6 µs, sys: 2 µs, total: 8 µs\n",
"Wall time: 97 µs\n",
"Review 0 of 25000\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:13: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
" del sys.path[0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Review 5000 of 25000\n",
"Review 10000 of 25000\n",
"Review 15000 of 25000\n",
"Review 20000 of 25000\n",
"(25000, 784)\n"
]
}
],
"source": [
"%time \n",
"testDataVecs = getAvgFeatureVecs(texts[len_train:], model, num_features)\n",
"print(np.shape(testDataVecs))"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"高斯贝叶斯分类器 10折交叉验证得分: \n",
" [0.62715936 0.6181632 0.62577952 0.62458144 0.63289088 0.59956992\n",
" 0.61033216 0.62668192 0.610296 0.60734944]\n",
"\n",
"高斯贝叶斯分类器 10折交叉验证平均得分: \n",
" 0.618280384\n"
]
}
],
"source": [
"from sklearn.naive_bayes import GaussianNB as GNB\n",
"from sklearn.cross_validation import cross_val_score\n",
"\n",
"gnb_model = GNB()\n",
"gnb_model.fit(trainDataVecs, label)\n",
"\n",
"scores = cross_val_score(gnb_model, trainDataVecs, label, cv=10, scoring='roc_auc')\n",
"print(\"\\n高斯贝叶斯分类器 10折交叉验证得分: \\n\", scores)\n",
"print(\"\\n高斯贝叶斯分类器 10折交叉验证平均得分: \\n\", np.mean(scores))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 机器学习 - 模型调参"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### KNN 模型训练"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"knn算法 10折交叉验证得分: \n",
" [0.82005056 0.81503776 0.83006976 0.8199152 0.82069568 0.827304\n",
" 0.81693088 0.8250944 0.80150176 0.821496 ]\n",
"\n",
"knn算法 10折交叉验证平均得分: \n",
" 0.8198095999999999\n"
]
}
],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"knn_model = KNeighborsClassifier(n_neighbors=5)\n",
"knn_model.fit(all_x[:len_train], label)\n",
"\n",
"scores = cross_val_score(knn_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\n",
"print(\"\\nknn算法 10折交叉验证得分: \\n\", scores)\n",
"print(\"\\nknn算法 10折交叉验证平均得分: \\n\", np.mean(scores))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 决策树 模型训练"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"决策树 10折交叉验证得分: \n",
" [0.7392 0.7232 0.7292 0.7236 0.7412 0.7164 0.718 0.724 0.7124 0.7156]\n",
"\n",
"决策树 10折交叉验证平均得分: \n",
" 0.72428\n"
]
}
],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"tree_model = DecisionTreeClassifier()\n",
"tree_model.fit(all_x[:len_train], label)\n",
"\n",
"scores = cross_val_score(tree_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\n",
"print(\"\\n决策树 10折交叉验证得分: \\n\", scores)\n",
"print(\"\\n决策树 10折交叉验证平均得分: \\n\", np.mean(scores))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 逻辑回归 模型训练"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"逻辑回归 10折交叉验证得分: \n",
" [0.94440064 0.94031744 0.95128192 0.94374784 0.9410656 0.94308864\n",
" 0.94733184 0.948768 0.93660352 0.94612288]\n",
"\n",
"逻辑回归 10折交叉验证平均得分: \n",
" 0.944272832\n"
]
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"lr_model = LogisticRegression(C=0.1, max_iter=100)\n",
"lr_model.fit(all_x[:len_train], label)\n",
"\n",
"scores = cross_val_score(lr_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\n",
"print(\"\\n逻辑回归 10折交叉验证得分: \\n\", scores)\n",
"print(\"\\n逻辑回归 10折交叉验证平均得分: \\n\", np.mean(scores))\n",
"\n",
"\n",
"# from sklearn.model_selection import GridSearchCV\n",
"\n",
"# # 设定grid search的参数\n",
"# grid_values = {'C': [1, 15, 30, 50]} \n",
"# \"\"\"\n",
"# penalty: l1 or l2, 用于指定惩罚中使用的标准。\n",
"# \"\"\"\n",
"# model_LR = GridSearchCV(estimator=LR(penalty='l2', dual=True, random_state=0), grid_values, scoring='roc_auc', cv=20)\n",
"# model_LR.fit(train_x, label)\n",
"\n",
"# 输出结果\n",
"# print(model_LR.cv_results_, '\\n', model_LR.best_params_, model_LR.best_score_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SVM 模型训练"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"SVM 10折交叉验证得分: \n",
" [0.94539328 0.9421344 0.95242176 0.94563328 0.94189504 0.94408704\n",
" 0.94839424 0.94898688 0.93809024 0.9473792 ]\n",
"\n",
"SVM 10折交叉验证平均得分: \n",
" 0.945441536\n"
]
}
],
"source": [
"from sklearn.svm import SVC\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"# model = SVC(C=4, kernel='rbf')\n",
"svm_model = SVC(kernel='linear', probability=True)\n",
"svm_model.fit(all_x[:len_train], label)\n",
"\n",
"scores = cross_val_score(svm_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\n",
"print(\"\\nSVM 10折交叉验证得分: \\n\", scores)\n",
"print(\"\\nSVM 10折交叉验证平均得分: \\n\", np.mean(scores))"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [],
"source": [
"svm_model = SVC(kernel='linear', probability=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### XGBoost 模型训练"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"SVM 10折交叉验证得分: \n",
" [0.93396416 0.93105024 0.93927616 0.9332768 0.9338336 0.93682432\n",
" 0.93343296 0.93623296 0.92564352 0.93381504]\n",
"\n",
"SVM 10折交叉验证平均得分: \n",
" 0.933734976\n"
]
}
],
"source": [
"from sklearn.model_selection import cross_val_score\n",
"from xgboost import XGBClassifier\n",
"import numpy as np\n",
"\n",
"xgb_model = XGBClassifier(n_estimators=150, min_samples_leaf=3, max_depth=6)\n",
"\"\"\"\n",
"AttributeError: 'list' object has no attribute 'shape'\n",
"list => np.array\n",
"\"\"\"\n",
"xgb_model.fit(np.array(all_x[:len_train]), label)\n",
"\n",
"scores = cross_val_score(xgb_model, np.array(all_x[:len_train]), label, cv=10, scoring='roc_auc')\n",
"print(\"\\nXGB 10折交叉验证得分: \\n\", scores)\n",
"print(\"\\nXGB 10折交叉验证平均得分: \\n\", np.mean(scores))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 模型融合"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### bagging: 随机森林 \n",
"\n",
"随机森林效果不好，去掉所有的树模型"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"随机森林 10折交叉验证得分: \n",
" [0.94539328 0.9421344 0.95242176 0.94563328 0.94189504 0.94408704\n",
" 0.94839424 0.94898688 0.93809024 0.9473792 ]\n",
"\n",
"随机森林 10折交叉验证平均得分: \n",
" 0.945441536\n"
]
}
],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"# parameters= {'n_estimators': range(10, 101, 10)} \n",
"# gsearch_rf = GridSearchCV(\n",
"# estimator=RandomForestClassifier(max_depth=8, random_state=0),\n",
"# param_grid=parameters, scoring='roc_auc', cv=10)\n",
"\n",
"# gsearch_rf = gsearch_rf.fit(all_x[:len_train], label)\n",
"\n",
"# print(gsearch_rf.cv_results_, '\\n', gsearch_rf.best_params_, '\\t', gsearch_rf.best_score_)\n",
"\"\"\"\n",
"[mean: 0.87486, std: 0.00576, params: {'n_estimators': 10}, mean: 0.88505, std: 0.00611, params: {'n_estimators': 20}, mean: 0.89032, std: 0.00609, params: {'n_estimators': 30}, mean: 0.89246, std: 0.00537, params: {'n_estimators': 40}, mean: 0.89439, std: 0.00528, params: {'n_estimators': 50}, \n",
" mean: 0.89507, std: 0.00607, params: {'n_estimators': 60}, mean: 0.89591, std: 0.00618, params: {'n_estimators': 70}, mean: 0.89634, std: 0.00634, params: {'n_estimators': 80}, mean: 0.89671, std: 0.00607, params: {'n_estimators': 90}, mean: 0.89753, std: 0.00588, params: {'n_estimators': 100}] \n",
"\n",
"{'n_estimators': 100} 0.89753344\n",
"\"\"\"\n",
"\n",
"rf_model = RandomForestClassifier(n_estimators=100, max_depth=8, random_state=0)\n",
"rf_model.fit(all_x[:len_train], label)\n",
"\n",
"scores = cross_val_score(svm_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\n",
"print(\"\\n随机森林 10折交叉验证得分: \\n\", scores)\n",
"print(\"\\n随机森林 10折交叉验证平均得分: \\n\", np.mean(scores))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### boosting: AdaBoost"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"AdaBoost 10折交叉验证得分: \n",
" [0.9207616 0.91974976 0.92787136 0.91906176 0.92305856 0.92228416\n",
" 0.9224832 0.92169856 0.91816256 0.92096768]\n",
"\n",
"AdaBoost 10折交叉验证平均得分: \n",
" 0.9216099200000001\n"
]
}
],
"source": [
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"ab_model = AdaBoostClassifier(\n",
" DecisionTreeClassifier(max_depth=2),\n",
" n_estimators=600,\n",
" learning_rate=1)\n",
"\n",
"ab_model.fit(all_x[:len_train], label)\n",
"\n",
"scores = cross_val_score(ab_model, all_x[:len_train], label, cv=10, scoring='roc_auc')\n",
"print(\"\\nAdaBoost 10折交叉验证得分: \\n\", scores)\n",
"print(\"\\nAdaBoost 10折交叉验证平均得分: \\n\", np.mean(scores))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### voting: 多模型投票"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.ensemble import VotingClassifier\n",
"\n",
"\"\"\"\n",
"soft报错是因为这种投票方式使用的是每个分类器的概率输出值进行投票的。\n",
"所以要求每个分类器的输出是概率值，而不是一个类别。\n",
"对于svc来说，默认的输出是类别，所以会有问题，其他分类器不会有这样的问题。\n",
"\"\"\"\n",
"\n",
"vot_model = VotingClassifier(\n",
"# estimators=[('lr', lr_model), ('svm', svm_model), ('xgb', xgb_model), ('rf', rf_model), ('ab', ab_model)]\n",
" estimators=[('xgb', xgb_model), ('rf', rf_model)],\n",
" voting='hard')\n",
"vot_model.fit(np.array(all_x[:len_train]), np.array(label))\n",
"\n",
"scores = cross_val_score(vot_model, np.array(all_x[:len_train]), np.array(label), cv=10, scoring='roc_auc')\n",
"print(\"\\nAdaBoost 10折交叉验证得分: \\n\", scores)\n",
"print(\"\\nAdaBoost 10折交叉验证平均得分: \\n\", np.mean(scores))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### stacking: 模型"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y = np.array([0, 0, 1, 1])\n",
"skf = StratifiedKFold(y, 2)\n",
"len(skf)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''模型融合中使用到的各个单模型'''\n",
"import numpy as np\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.cross_validation import StratifiedKFold\n",
"\n",
"# 划分train数据集,调用代码,把数据集名字转成和代码一样\n",
"X = np.array(all_x[:len_train])\n",
"X_predict = np.array(all_x[len_train:])\n",
"label.astype(np.integer)\n",
"y = label.values\n",
"\n",
"# clfs = [LogisticRegression(C=0.1, max_iter=100),\n",
"# xgb.XGBClassifier(max_depth=6, n_estimators=100, num_round = 5),\n",
"# RandomForestClassifier(n_estimators=100, max_depth=6, oob_score=True),\n",
"# GradientBoostingClassifier(learning_rate=0.3, max_depth=6, n_estimators=100)]\n",
"\n",
"clfs = [knn_model, tree_model, lr_model, svm_model, xgb_model, rf_model, ab_model]\n",
"\n",
"\n",
"# 创建n_folds\n",
"n_folds = 10\n",
"skf = StratifiedKFold(y, n_folds)\n",
"\n",
"\n",
"# 创建零矩阵\n",
"dataset_blend_train = np.zeros((X.shape[0], len(clfs)))\n",
"dataset_blend_test = np.zeros((X_predict.shape[0], len(clfs)))\n",
"\n",
"# 建立模型\n",
"for j, clf in enumerate(clfs):\n",
" '''依次训练各个单模型'''\n",
" # print(j, clf)\n",
" dataset_blend_test_j = np.zeros((X_predict.shape[0], len(skf)))\n",
" for i, (train, test) in enumerate(skf):\n",
" '''使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。'''\n",
" # print(\"Fold\", i)\n",
" X_train, y_train, X_test, y_test = X[train], y[train], X[test], y[test]\n",
" clf.fit(X_train, y_train)\n",
" y_submission = clf.predict_proba(X_test)[:, 1]\n",
" dataset_blend_train[test, j] = y_submission\n",
" dataset_blend_test_j[:, i] = clf.predict_proba(X_predict)[:, 1]\n",
" '''对于测试集，直接用这k个模型的预测值均值作为新的特征。'''\n",
" dataset_blend_test[:, j] = dataset_blend_test_j.mean(1)\n",
"\n",
"# 用建立第二层模型\n",
"stacking_model = LogisticRegression(C=0.1, max_iter=100)\n",
"stacking_model.fit(dataset_blend_train, y_train)\n",
"\n",
"scores = cross_val_score(ab_model, dataset_blend_train, label, cv=10, scoring='roc_auc')\n",
"print(\"\\nAdaBoost 10折交叉验证得分: \\n\", scores)\n",
"print(\"\\nAdaBoost 10折交叉验证平均得分: \\n\", np.mean(scores))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 数据导出"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"保存结果...\n",
" id sentiment\n",
"0 \"12311_10\" 1\n",
"1 \"8348_2\" 0\n",
"2 \"5828_4\" 1\n",
"3 \"7186_2\" 1\n",
"4 \"12128_7\" 1\n",
"5 \"2913_8\" 1\n",
"6 \"4396_1\" 0\n",
"7 \"395_2\" 0\n",
"8 \"10616_1\" 0\n",
"9 \"9074_9\" 1\n",
"结束.\n"
]
},
{
"data": {
"text/plain": [
"'\\n1.提交最终的结果到kaggle，AUC为：0.85728，排名300左右，50%的水平\\n2. ngram_range = 3, 三元文法，AUC为0.85924\\n'"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_predicted = np.array(model_NB.predict(corpus_tfidf[len_train:]))\n",
"print('保存结果...')\n",
"\n",
"import os\n",
"root_dir = \"/opt/data/kaggle/getting-started/word2vec-nlp-tutorial\"\n",
" \n",
"submission_df = pd.DataFrame(data ={'id': test['id'], 'sentiment': test_predicted})\n",
"print(submission_df.head(10))\n",
"submission_df.to_csv(os.path.join(root_dir, 'submission_br.csv'), index = False)\n",
"\n",
"print('结束.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CNN 来处理 文本问题： https://zhuanlan.zhihu.com/p/26729228"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 分词，获取分割后的所有文章"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"from bs4 import BeautifulSoup\n",
"from nltk.corpus import stopwords\n",
"\n",
"stopwords = set(stopwords.words(\"english\"))\n",
"\n",
"def review_to_sentence(review):\n",
" html = BeautifulSoup(review, \"html.parser\").get_text()\n",
" text = re.sub('[^a-zA-Z]', ' ', html).strip()\n",
" words = [word for word in text.lower().split() if word not in stopwords]\n",
" return words\n",
"\n",
"unlabeled_train = pd.read_csv(os.path.join(data_dir, 'unlabeledTrainData.tsv'), header=0, delimiter=\"\\t\", quoting=3 )\n",
"train_texts = pd.concat([train['review'], unlabeled_train['review']], axis=0, ignore_index=True)\n",
"sentences = list(map(review_to_sentence, train_texts))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((75000,), 219, 84, 25000, 50000)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.shape(sentences), len(sentences[0]), len(sentences[1]), len(train['review']), len( unlabeled_train['review'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 对文章简历词典"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from gensim import corpora\n",
"dictionary = corpora.Dictionary(sentences)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"dictionary.add_documents([[\" \"]])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 actual\n",
"1 alone\n",
"2 also\n",
"3 another\n",
"4 anyway\n"
]
}
],
"source": [
"for key in dictionary.keys()[0:5]:\n",
" print (key, dictionary[key])\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(123350, 123350, dict, dict)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dictionary.token2id), len(dictionary.id2token), type(dictionary.token2id), type(dictionary.id2token)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3, 'another', 123349)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dictionary.token2id[\"another\"], dictionary.id2token[3], dictionary.token2id[\" \"]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F # 激励函数都在这\n",
"from torch.autograd import Variable\n",
"import torch.utils.data as Data\n",
"import torchvision # 数据库模块"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1416 \n",
" 1416\n"
]
}
],
"source": [
"max_len = max([len(i) for i in sentences])\n",
"max_index = dictionary.token2id[\" \"]\n",
"max_list = [max_index for x in range(max_len)]\n",
"print(max_len, \"\\n\", len(max_list))"
]
},
{
"cell_type": "code",
"execution_count": 362,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variable containing:\n",
"1.00000e-02 *\n",
" 5.6823 -5.1981\n",
"[torch.FloatTensor of size 1x2]\n",
"\n"
]
}
],
"source": [
"# # prepare_sequence 是将文本的索引转化为 Variable 对象\n",
"# def prepare_sequence(seq):\n",
"# idxs = [dictionary.token2id[w] for w in seq]\n",
"# if len(idxs) < max_len:\n",
"# idxs = idxs + max_list[len(idxs):]\n",
"# # print('文本词典的索引序列：', idxs)\n",
"# tensor = torch.LongTensor(idxs)\n",
"# return Variable(tensor)\n",
"\n",
"# sentence_in = prepare_sequence(sentences[1383])\n",
"# # word_embeddings = nn.Embedding(len(dictionary.token2id), 5)\n",
"# # word_embeddings(sentence_in)\n",
"# x = cnn(sentence_in)\n",
"# print(x)"
]
},
{
"cell_type": "code",
"execution_count": 319,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1416"
]
},
"execution_count": 319,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sentence_in.data.size(0)"
]
},
{
"cell_type": "code",
"execution_count": 320,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(['stuff', 'going', 'moment'], '\\n\\n', 1416)"
]
},
"execution_count": 320,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sentences[0][:3], \"\\n\\n\", len(sentence_in)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 拆分数据集"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# prepare_sequence 是将文本的索引转化为 Variable 对象\n",
"def prepare_sequence(seq):\n",
" idxs = [dictionary.token2id[w] for w in seq]\n",
" if len(idxs) < max_len:\n",
" idxs = idxs + max_list[len(idxs):]\n",
"# print('文本词典的索引序列：', idxs)\n",
" return idxs\n",
"\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"X_train = list(map(prepare_sequence, sentences[:len(train)]))\n",
"X_train_d, X_test_d, y_train_d, y_test_d = train_test_split(X_train, label.tolist(), test_size=0.2, shuffle=True, random_state=42)\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(list, list)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(X_train_d), type(y_train_d)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((20000, 1416), (5000, 1416), (20000,), (5000,))"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.shape(X_train_d), np.shape(X_test_d), np.shape(y_train_d), np.shape(y_test_d)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"([100, 1380, 2330], [509, 58, 14209], [0, 0, 0], [0, 1, 0])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train_d[0][:3], X_test_d[0][:3], y_train_d[:3], y_test_d[:3]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"class textCNN(nn.Module):\n",
" \n",
" def __init__(self, vocab_size, embedding_dim, max_len, n_classes):\n",
" super(textCNN, self).__init__()\n",
" \n",
" self.model_name = 'alexnet'\n",
" self.vocab_size = vocab_size\n",
" self.embedding_dim = embedding_dim\n",
" self.max_len = max_len\n",
" \n",
" self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\n",
" \n",
" self.features = nn.Sequential(\n",
"# nn.Conv2d(1, 64, kernel_size=11, stride=4, padding=2),\n",
"# nn.ReLU(inplace=True),\n",
"# nn.MaxPool2d(kernel_size=3, stride=2),\n",
"\n",
"# nn.Conv2d(64, 192, kernel_size=5, padding=2),\n",
"# nn.ReLU(inplace=True),\n",
"# nn.MaxPool2d(kernel_size=3, stride=2),\n",
"\n",
"# nn.Conv2d(192, 384, kernel_size=3, padding=1),\n",
"# nn.ReLU(inplace=True),\n",
"\n",
"# nn.Conv2d(384, 256, kernel_size=3, padding=1),\n",
"# nn.ReLU(inplace=True),\n",
"\n",
"# nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
"# nn.ReLU(inplace=True),\n",
"# nn.MaxPool2d(kernel_size=3, stride=2),\n",
" \n",
" \n",
" nn.Conv2d(1, 16, 5, 1, 2),\n",
" nn.ReLU(), # activation\n",
" nn.MaxPool2d(kernel_size=2, stride=2), # 在 2x2 空间里向下采样, output shape (16, 14, 14), 默认步长为2\n",
" \n",
" nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)\n",
" nn.ReLU(), # activation\n",
" nn.MaxPool2d(2), # output shape (32, 7, 7)\n",
"\n",
" nn.Conv2d(32, 64, 5, 1, 2), \n",
" nn.ReLU(), \n",
" nn.MaxPool2d(2), \n",
" \n",
" nn.Conv2d(64, 128, 5, 1, 2), \n",
" nn.ReLU(), \n",
" nn.MaxPool2d(2)\n",
" )\n",
" \n",
" self.classifier = nn.Sequential(\n",
"# nn.Dropout(),\n",
"# nn.Linear(256 * 6 * 6, 4096),\n",
"# nn.ReLU(inplace=True),\n",
"\n",
"# nn.Dropout(),\n",
"# nn.Linear(4096, 4096),\n",
"# nn.ReLU(inplace=True),\n",
"\n",
"# nn.Linear(4096, n_classes),\n",
" \n",
" nn.Dropout(),\n",
" nn.Linear(45056, n_classes), \n",
" )\n",
" \n",
" def forward(self, x):\n",
" x = self.word_embeddings(x)\n",
" x = x.view(len(x), 1, self.max_len, self.embedding_dim)\n",
" x = self.features(x)\n",
" x = x.view(x.size(0), -1)\n",
" output = self.classifier(x)\n",
" return output # return x for visualization\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"textCNN(\n",
" (word_embeddings): Embedding(123350, 64)\n",
" (features): Sequential(\n",
" (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
" (1): ReLU()\n",
" (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)\n",
" (3): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
" (4): ReLU()\n",
" (5): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)\n",
" (6): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
" (7): ReLU()\n",
" (8): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)\n",
" (9): Conv2d(64, 128, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
" (10): ReLU()\n",
" (11): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)\n",
" )\n",
" (classifier): Sequential(\n",
" (0): Dropout(p=0.5)\n",
" (1): Linear(in_features=45056, out_features=2, bias=True)\n",
" )\n",
")\n"
]
}
],
"source": [
"cnn = textCNN(len(dictionary.token2id), 64, max_len, 2)\n",
"print(cnn) # net architecture\n",
"\n",
"# optimizer = torch.optim.SGD(cnn.parameters(), lr=0.02) # 传入 net 的所有参数, 学习率\n",
"# lr 优化步长\n",
"# weight_decay(权重衰减): 也叫 L2 regularization (1e-5就是 1*(10的-5次方)即0.00001)\n",
"optimizer = torch.optim.Adam(cnn.parameters(), lr=1e-5, weight_decay=1e-7)\n",
"# 算误差的时候, 注意真实值!不是! one-hot 形式的, 而是1D Tensor, (batch,)\n",
"# 但是预测值是2D tensor (batch, n_classes)\n",
"loss_func = nn.CrossEntropyLoss()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import math\n",
"\n",
"def timeSince(since):\n",
" now = time.time()\n",
" s = now - since\n",
" m = math.floor(s / 60)\n",
" s -= m * 60\n",
" return '%dm %ds' % (m, s)\n",
"\n",
"start = time.time()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"tr_x = torch.LongTensor(X_train_d)\n",
"tr_y = torch.LongTensor(y_train_d)\n",
"te_x = torch.LongTensor(X_test_d)\n",
"te_y = torch.LongTensor(y_test_d)\n",
"\n",
"torch_train_dataset = Data.TensorDataset(data_tensor=tr_x, target_tensor=tr_y)\n",
"torch_test_dataset = Data.TensorDataset(data_tensor=te_x, target_tensor=te_y)\n",
"\n",
"BATCH_SIZE = 20 # 批训练的数据个数\n",
"\n",
"# 把 dataset 放入 DataLoader\n",
"train_loader = Data.DataLoader(\n",
" dataset=torch_train_dataset, # torch TensorDataset format\n",
" batch_size=BATCH_SIZE, # 每个 batch 加载多少个样本\n",
" shuffle=True, # 要不要打乱数据 (打乱比较好)\n",
" num_workers=2, # 多线程来读数据\n",
")\n",
"test_loader = Data.DataLoader(\n",
" dataset=torch_test_dataset, # torch TensorDataset format\n",
" batch_size=BATCH_SIZE, # 每个 batch 加载多少个样本\n",
" shuffle=True, # 要不要打乱数据 (打乱比较好)\n",
" num_workers=2, # 多线程来读数据\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pred_y:\t [1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1]\n",
"target_y:\t [0 1 1 1 0 1 0 0 1 0 0 1 1 1 1 0 1 0 1 0]\n",
"0-19 7.60% (101m 42s) logloss=12.09 \t accuracy=0.65 \t loss=0.6844480037689209\n",
"pred_y:\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
"target_y:\t [1 1 0 1 1 0 1 1 0 0 0 0 0 1 1 0 1 1 1 0]\n",
"0-39 15.60% (103m 24s) logloss=15.54 \t accuracy=0.55 \t loss=0.6886069774627686\n",
"pred_y:\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
"target_y:\t [0 1 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 0 0 1]\n",
"0-59 23.60% (106m 31s) logloss=12.09 \t accuracy=0.65 \t loss=0.6793051958084106\n",
"pred_y:\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
"target_y:\t [0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 1 0 0 1]\n",
"0-79 31.60% (114m 37s) logloss=22.45 \t accuracy=0.35 \t loss=0.6999103426933289\n",
"pred_y:\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
"target_y:\t [0 0 0 0 0 1 1 0 1 0 1 1 0 1 1 0 0 0 1 0]\n",
"0-99 39.60% (120m 29s) logloss=20.72 \t accuracy=0.40 \t loss=0.7069584131240845\n",
"pred_y:\t [1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
"target_y:\t [1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1]\n",
"0-119 47.60% (125m 5s) logloss=19.00 \t accuracy=0.45 \t loss=0.7006260752677917\n",
"pred_y:\t [0 0 0 1 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1]\n",
"target_y:\t [0 1 1 0 0 1 0 0 1 1 0 0 0 0 1 1 1 1 1 0]\n",
"0-139 55.60% (132m 49s) logloss=25.90 \t accuracy=0.25 \t loss=0.7005825638771057\n",
"pred_y:\t [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [0 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 0 1 1 0]\n",
"0-159 63.60% (140m 33s) logloss=24.18 \t accuracy=0.30 \t loss=0.699988067150116\n",
"pred_y:\t [1 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0]\n",
"target_y:\t [1 1 1 1 0 1 0 0 0 0 1 0 0 0 1 1 1 0 1 1]\n",
"0-179 71.60% (148m 14s) logloss=15.54 \t accuracy=0.55 \t loss=0.689541220664978\n",
"pred_y:\t [1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 0 1 0 1 1]\n",
"target_y:\t [0 1 0 0 1 0 0 0 0 1 1 1 0 0 1 1 1 1 0 1]\n",
"0-199 79.60% (154m 37s) logloss=19.00 \t accuracy=0.45 \t loss=0.6907564401626587\n",
"pred_y:\t [1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1]\n",
"target_y:\t [0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 1 1 0 0]\n",
"0-219 87.60% (160m 44s) logloss=24.18 \t accuracy=0.30 \t loss=0.6987239122390747\n",
"pred_y:\t [1 0 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1]\n",
"target_y:\t [0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 1]\n",
"0-239 95.60% (167m 26s) logloss=19.00 \t accuracy=0.45 \t loss=0.691254734992981\n",
"pred_y:\t [0 1 1 0 1 0 0 1 1 0 1 0 1 0 1 0 1 1 1 1]\n",
"target_y:\t [1 0 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 0 0 0]\n",
"0-259 103.60% (174m 38s) logloss=13.82 \t accuracy=0.60 \t loss=0.6896542310714722\n",
"pred_y:\t [0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0]\n",
"target_y:\t [1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 1]\n",
"0-279 111.60% (181m 56s) logloss=12.09 \t accuracy=0.65 \t loss=0.6853312253952026\n",
"pred_y:\t [0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0]\n",
"target_y:\t [1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1]\n",
"0-299 119.60% (188m 49s) logloss=25.90 \t accuracy=0.25 \t loss=0.7102211713790894\n",
"pred_y:\t [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 1 1 1]\n",
"0-319 127.60% (196m 8s) logloss=12.09 \t accuracy=0.65 \t loss=0.6825012564659119\n",
"pred_y:\t [0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 0 0]\n",
"target_y:\t [0 0 0 1 0 1 1 0 0 1 1 0 1 1 0 0 0 0 1 0]\n",
"0-339 135.60% (203m 21s) logloss=17.27 \t accuracy=0.50 \t loss=0.693179190158844\n",
"pred_y:\t [0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0]\n",
"target_y:\t [0 1 0 1 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 1]\n",
"0-359 143.60% (210m 48s) logloss=13.82 \t accuracy=0.60 \t loss=0.6911899447441101\n",
"pred_y:\t [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [0 0 1 0 0 1 0 0 0 1 1 1 0 0 0 0 0 1 0 1]\n",
"0-379 151.60% (218m 11s) logloss=10.36 \t accuracy=0.70 \t loss=0.6836111545562744\n",
"pred_y:\t [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [0 1 1 1 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0]\n",
"0-399 159.60% (225m 11s) logloss=17.27 \t accuracy=0.50 \t loss=0.6962472200393677\n",
"pred_y:\t [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]\n",
"target_y:\t [0 1 1 0 0 1 0 0 0 1 1 0 0 0 1 1 0 0 1 1]\n",
"0-419 167.60% (232m 36s) logloss=17.27 \t accuracy=0.50 \t loss=0.6901986598968506\n",
"pred_y:\t [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1]\n",
"target_y:\t [1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1]\n",
"0-439 175.60% (240m 1s) logloss=8.63 \t accuracy=0.75 \t loss=0.6787502765655518\n",
"pred_y:\t [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [0 1 0 1 1 0 0 0 0 0 1 0 1 1 0 0 0 1 0 1]\n",
"0-459 183.60% (247m 31s) logloss=13.82 \t accuracy=0.60 \t loss=0.694130003452301\n",
"pred_y:\t [0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [1 0 1 1 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0]\n",
"0-479 191.60% (254m 5s) logloss=15.54 \t accuracy=0.55 \t loss=0.6915131211280823\n",
"pred_y:\t [0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0]\n",
"target_y:\t [0 0 1 1 1 0 0 0 1 1 1 1 1 1 0 1 0 1 0 1]\n",
"0-499 199.60% (260m 32s) logloss=20.72 \t accuracy=0.40 \t loss=0.696361243724823\n",
"pred_y:\t [0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 1 1 0 1]\n",
"target_y:\t [0 0 0 0 0 1 0 0 1 0 0 1 1 0 1 0 0 0 1 1]\n",
"0-519 207.60% (267m 39s) logloss=15.54 \t accuracy=0.55 \t loss=0.6885088086128235\n",
"pred_y:\t [0 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1]\n",
"target_y:\t [1 0 0 0 1 0 1 1 1 0 1 0 0 0 0 1 1 0 0 0]\n",
"0-539 215.60% (275m 2s) logloss=19.00 \t accuracy=0.45 \t loss=0.6945030093193054\n",
"pred_y:\t [1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0]\n",
"target_y:\t [1 0 0 0 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 0]\n",
"0-559 223.60% (281m 12s) logloss=13.82 \t accuracy=0.60 \t loss=0.6861685514450073\n",
"pred_y:\t [1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1]\n",
"target_y:\t [0 1 1 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0]\n",
"0-579 231.60% (287m 12s) logloss=22.45 \t accuracy=0.35 \t loss=0.6945004463195801\n",
"pred_y:\t [0 1 0 0 1 1 1 1 0 1 1 1 0 0 0 1 0 1 0 1]\n",
"target_y:\t [1 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 0 1]\n",
"0-599 239.60% (293m 44s) logloss=19.00 \t accuracy=0.45 \t loss=0.6927602887153625\n",
"pred_y:\t [1 1 1 1 1 0 1 1 1 1 1 0 0 0 1 0 1 0 1 0]\n",
"target_y:\t [0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 0 0 1]\n",
"0-619 247.60% (300m 38s) logloss=12.09 \t accuracy=0.65 \t loss=0.6841057538986206\n",
"pred_y:\t [1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1]\n",
"target_y:\t [1 1 0 1 0 1 1 1 1 0 0 1 0 0 0 1 0 1 0 0]\n",
"0-639 255.60% (307m 59s) logloss=17.27 \t accuracy=0.50 \t loss=0.6867891550064087\n",
"pred_y:\t [1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1 1 0 1 0]\n",
"target_y:\t [1 0 1 0 0 1 1 1 0 1 0 0 1 0 0 0 1 1 0 0]\n",
"0-659 263.60% (315m 15s) logloss=20.72 \t accuracy=0.40 \t loss=0.697279691696167\n",
"pred_y:\t [0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 0 0]\n",
"target_y:\t [1 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 1 1 0]\n",
"0-679 271.60% (322m 12s) logloss=24.18 \t accuracy=0.30 \t loss=0.6973448991775513\n",
"pred_y:\t [1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1]\n",
"target_y:\t [1 1 1 1 0 1 1 0 0 0 1 1 1 1 1 0 0 0 0 0]\n",
"0-699 279.60% (329m 20s) logloss=17.27 \t accuracy=0.50 \t loss=0.693761944770813\n",
"pred_y:\t [0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0]\n",
"target_y:\t [1 0 1 1 0 1 0 1 0 1 1 0 0 0 1 0 1 1 1 1]\n",
"0-719 287.60% (335m 57s) logloss=17.27 \t accuracy=0.50 \t loss=0.6938427686691284\n",
"pred_y:\t [0 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0]\n",
"target_y:\t [1 0 1 0 0 1 1 0 0 1 1 0 0 1 0 0 1 0 1 0]\n",
"0-739 295.60% (342m 53s) logloss=17.27 \t accuracy=0.50 \t loss=0.6820307970046997\n",
"pred_y:\t [0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [0 1 1 1 1 1 1 1 0 0 1 0 1 0 0 1 1 0 0 0]\n",
"0-759 303.60% (350m 7s) logloss=15.54 \t accuracy=0.55 \t loss=0.6916964650154114\n",
"pred_y:\t [0 1 0 1 0 0 0 1 0 1 1 1 0 0 0 0 1 0 0 0]\n",
"target_y:\t [1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0 0 0 0]\n",
"0-779 311.60% (357m 5s) logloss=20.72 \t accuracy=0.40 \t loss=0.6954394578933716\n",
"pred_y:\t [0 1 0 1 1 1 0 1 1 1 0 1 0 0 1 0 1 0 1 0]\n",
"target_y:\t [0 0 1 1 1 1 0 0 0 0 1 0 1 1 0 1 1 0 0 0]\n",
"0-799 319.60% (364m 23s) logloss=20.72 \t accuracy=0.40 \t loss=0.7011473178863525\n",
"pred_y:\t [0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 1 1 0 1 0]\n",
"target_y:\t [0 1 1 1 1 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1]\n",
"0-819 327.60% (371m 51s) logloss=19.00 \t accuracy=0.45 \t loss=0.6923590898513794\n",
"pred_y:\t [0 1 1 0 1 0 1 1 1 0 1 1 0 0 1 1 0 1 1 1]\n",
"target_y:\t [0 0 1 1 0 0 0 1 1 1 0 0 0 1 0 0 1 0 1 1]\n",
"0-839 335.60% (379m 18s) logloss=20.72 \t accuracy=0.40 \t loss=0.7093911170959473\n",
"pred_y:\t [1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0]\n",
"target_y:\t [0 0 0 0 0 1 1 1 0 0 0 1 1 1 1 1 0 1 0 1]\n",
"0-859 343.60% (386m 37s) logloss=17.27 \t accuracy=0.50 \t loss=0.6935914158821106\n",
"pred_y:\t [1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1]\n",
"target_y:\t [1 1 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0]\n",
"0-879 351.60% (394m 6s) logloss=17.27 \t accuracy=0.50 \t loss=0.6971246004104614\n",
"pred_y:\t [1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1]\n",
"target_y:\t [1 1 0 1 0 1 1 1 0 0 1 0 0 0 0 1 0 1 0 1]\n",
"0-899 359.60% (401m 57s) logloss=13.82 \t accuracy=0.60 \t loss=0.7006368637084961\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"pred_y:\t [0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0]\n",
"target_y:\t [1 0 0 0 0 0 1 1 0 1 1 1 0 0 1 0 0 0 0 1]\n",
"0-919 367.60% (409m 9s) logloss=13.82 \t accuracy=0.60 \t loss=0.684188961982727\n",
"pred_y:\t [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [1 0 1 1 1 0 0 1 1 0 0 0 1 1 1 0 1 0 0 0]\n",
"0-939 375.60% (416m 38s) logloss=17.27 \t accuracy=0.50 \t loss=0.7021096348762512\n",
"pred_y:\t [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [0 0 0 0 1 1 0 1 0 0 1 1 1 1 1 1 1 0 0 1]\n",
"0-959 383.60% (424m 14s) logloss=20.72 \t accuracy=0.40 \t loss=0.6950109004974365\n",
"pred_y:\t [0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 0 0]\n",
"0-979 391.60% (431m 21s) logloss=17.27 \t accuracy=0.50 \t loss=0.6897827982902527\n",
"pred_y:\t [0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 1 1]\n",
"target_y:\t [0 0 0 1 1 1 1 0 1 1 1 0 0 1 0 1 0 1 1 0]\n",
"0-999 399.60% (438m 31s) logloss=15.54 \t accuracy=0.55 \t loss=0.6863324642181396\n",
"pred_y:\t [0 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0]\n",
"target_y:\t [0 1 0 1 1 0 1 1 0 0 0 1 1 1 0 1 1 1 0 0]\n",
"1-69 27.60% (447m 6s) logloss=17.27 \t accuracy=0.50 \t loss=0.6887694597244263\n",
"pred_y:\t [1 0 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0]\n",
"target_y:\t [1 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0]\n",
"1-89 35.60% (455m 24s) logloss=6.91 \t accuracy=0.80 \t loss=0.6829289197921753\n",
"pred_y:\t [0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0]\n",
"target_y:\t [0 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1]\n",
"1-109 43.60% (463m 55s) logloss=13.82 \t accuracy=0.60 \t loss=0.6840181350708008\n",
"pred_y:\t [0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0]\n",
"target_y:\t [0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1]\n",
"1-129 51.60% (472m 25s) logloss=3.45 \t accuracy=0.90 \t loss=0.6634591817855835\n",
"pred_y:\t [0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [0 1 1 1 0 0 0 0 0 1 1 0 0 0 1 0 0 1 1 1]\n",
"1-149 59.60% (481m 20s) logloss=15.54 \t accuracy=0.55 \t loss=0.6897100210189819\n",
"pred_y:\t [0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [1 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 0]\n",
"1-169 67.60% (490m 7s) logloss=12.09 \t accuracy=0.65 \t loss=0.6846562623977661\n",
"pred_y:\t [0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0]\n",
"target_y:\t [1 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 0 0 0 1]\n",
"1-189 75.60% (498m 56s) logloss=12.09 \t accuracy=0.65 \t loss=0.6853525638580322\n",
"pred_y:\t [0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0]\n",
"target_y:\t [1 0 1 1 1 0 1 1 0 0 0 1 0 0 0 1 0 0 0 1]\n",
"1-209 83.60% (507m 31s) logloss=19.00 \t accuracy=0.45 \t loss=0.7002253532409668\n",
"pred_y:\t [1 0 1 0 1 0 0 1 0 1 1 0 1 1 1 0 1 0 1 0]\n",
"target_y:\t [1 1 0 1 0 0 0 0 1 1 1 0 0 0 1 0 1 0 1 0]\n",
"1-229 91.60% (516m 28s) logloss=13.82 \t accuracy=0.60 \t loss=0.6863614320755005\n",
"pred_y:\t [0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
"target_y:\t [0 1 0 0 1 0 0 1 0 1 0 1 0 1 0 0 1 0 1 1]\n",
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{
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]
},
{
"name": "stdout",
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"pred_y:\t [1 1 1 0 0 1 1 1 1 0 1 0 1 0 1 0 0 1 1 1]\n",
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"pred_y:\t [1 0 0 0 1 0 1 0 0 1 0 1 1 1 1 0 0 1 0 0]\n",
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"pred_y:\t [0 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0]\n",
"target_y:\t [1 1 1 1 0 1 1 1 0 0 1 0 1 1 0 0 0 1 0 0]\n",
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"pred_y:\t [1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
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"pred_y:\t [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0]\n",
"target_y:\t [1 0 0 1 0 1 0 0 0 1 1 0 0 1 1 0 0 0 1 1]\n",
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"pred_y:\t [0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0]\n",
"3-289 115.60% (844m 43s) logloss=25.90 \t accuracy=0.25 \t loss=0.7268241047859192\n",
"pred_y:\t [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0]\n",
"target_y:\t [0 0 1 0 1 1 1 0 0 1 0 1 0 1 1 1 1 0 0 0]\n",
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"pred_y:\t [0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [1 0 0 0 1 1 1 1 0 1 1 0 0 0 1 0 0 1 1 0]\n",
"3-329 131.60% (849m 35s) logloss=15.54 \t accuracy=0.55 \t loss=0.6976009607315063\n",
"pred_y:\t [0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0]\n",
"target_y:\t [0 0 1 1 0 0 1 1 0 1 0 1 0 0 0 0 1 0 0 0]\n",
"3-349 139.60% (852m 0s) logloss=15.54 \t accuracy=0.55 \t loss=0.6879168152809143\n",
"pred_y:\t [1 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1 1]\n",
"target_y:\t [0 0 1 0 1 1 0 0 1 0 0 1 1 0 1 0 0 0 0 0]\n",
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"pred_y:\t [0 0 0 1 1 0 1 1 1 1 0 0 0 0 1 1 0 1 1 0]\n",
"target_y:\t [0 0 1 0 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0]\n",
"3-389 155.60% (856m 19s) logloss=15.54 \t accuracy=0.55 \t loss=0.677700400352478\n",
"pred_y:\t [0 0 1 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 1]\n",
"target_y:\t [1 1 0 0 1 0 0 1 1 0 1 0 1 1 1 0 1 1 0 0]\n",
"3-409 163.60% (858m 10s) logloss=25.90 \t accuracy=0.25 \t loss=0.7202922105789185\n",
"pred_y:\t [1 1 1 1 0 1 1 0 1 0 0 0 0 1 1 0 0 0 0 0]\n",
"target_y:\t [1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 0 1 1 0]\n",
"3-429 171.60% (860m 38s) logloss=20.72 \t accuracy=0.40 \t loss=0.6937626600265503\n",
"pred_y:\t [1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 1 1 1 1]\n",
"target_y:\t [0 1 0 1 0 0 1 1 1 1 0 1 1 0 0 0 0 1 0 0]\n",
"3-449 179.60% (863m 8s) logloss=17.27 \t accuracy=0.50 \t loss=0.6776196956634521\n",
"pred_y:\t [0 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1]\n",
"target_y:\t [0 1 0 0 1 0 1 1 0 1 0 0 1 1 1 1 1 0 1 0]\n",
"3-469 187.60% (865m 31s) logloss=10.36 \t accuracy=0.70 \t loss=0.6834084987640381\n",
"pred_y:\t [1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 0 0 1 1]\n",
"target_y:\t [0 1 0 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 0]\n",
"3-489 195.60% (867m 51s) logloss=22.45 \t accuracy=0.35 \t loss=0.6945611238479614\n",
"pred_y:\t [1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0]\n",
"target_y:\t [0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 1 0 1 0 0]\n",
"3-509 203.60% (870m 3s) logloss=17.27 \t accuracy=0.50 \t loss=0.6905189752578735\n",
"pred_y:\t [1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0]\n",
"target_y:\t [1 1 1 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 0 1]\n",
"3-529 211.60% (872m 42s) logloss=13.82 \t accuracy=0.60 \t loss=0.6931790113449097\n",
"pred_y:\t [0 1 0 1 1 0 1 1 1 1 0 1 0 1 0 1 0 0 0 0]\n",
"target_y:\t [0 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 0]\n",
"3-549 219.60% (874m 53s) logloss=15.54 \t accuracy=0.55 \t loss=0.6936815977096558\n",
"pred_y:\t [0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0]\n",
"target_y:\t [0 0 0 0 1 1 1 0 1 1 1 0 0 1 1 1 1 0 0 1]\n",
"3-569 227.60% (877m 4s) logloss=24.18 \t accuracy=0.30 \t loss=0.7091041803359985\n",
"pred_y:\t [0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0]\n",
"target_y:\t [0 0 0 1 0 1 1 0 1 0 0 0 1 1 0 0 0 0 0 1]\n",
"3-589 235.60% (880m 4s) logloss=8.63 \t accuracy=0.75 \t loss=0.6674402356147766\n",
"pred_y:\t [0 0 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1]\n",
"target_y:\t [0 1 0 0 0 0 1 0 1 1 0 1 1 1 1 0 1 0 1 0]\n",
"3-609 243.60% (882m 57s) logloss=17.27 \t accuracy=0.50 \t loss=0.6928107142448425\n",
"pred_y:\t [0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 1 1 0 0 0]\n",
"target_y:\t [0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 1 1 1 1]\n",
"3-629 251.60% (885m 15s) logloss=19.00 \t accuracy=0.45 \t loss=0.7027646899223328\n",
"pred_y:\t [0 0 0 0 1 1 0 0 1 1 0 1 1 0 0 0 0 0 1 1]\n",
"target_y:\t [0 0 0 0 1 1 1 1 0 0 0 1 1 1 0 0 1 1 1 1]\n",
"3-649 259.60% (887m 35s) logloss=12.09 \t accuracy=0.65 \t loss=0.6845759153366089\n",
"pred_y:\t [0 1 0 0 1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0]\n",
"target_y:\t [1 1 1 1 1 1 0 0 0 1 0 1 1 0 0 1 1 1 1 1]\n",
"3-669 267.60% (890m 7s) logloss=17.27 \t accuracy=0.50 \t loss=0.6864821314811707\n",
"pred_y:\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
"target_y:\t [0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0]\n",
"3-689 275.60% (892m 46s) logloss=25.90 \t accuracy=0.25 \t loss=0.7397301197052002\n",
"pred_y:\t [1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 0]\n",
"target_y:\t [1 0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1 0]\n",
"3-709 283.60% (895m 49s) logloss=17.27 \t accuracy=0.50 \t loss=0.6964901685714722\n",
"pred_y:\t [1 0 0 1 0 0 0 1 1 0 0 1 0 1 1 1 0 1 1 0]\n",
"target_y:\t [1 0 1 0 1 1 0 0 1 0 0 1 0 0 1 1 0 1 1 1]\n",
"3-729 291.60% (898m 44s) logloss=12.09 \t accuracy=0.65 \t loss=0.6655923128128052\n",
"pred_y:\t [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
"target_y:\t [0 1 0 1 1 1 1 0 1 0 1 0 0 0 1 0 1 1 1 0]\n",
"3-749 299.60% (901m 31s) logloss=15.54 \t accuracy=0.55 \t loss=0.7032931447029114\n"
]
},
{
"name": "stdout",
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"pred_y:\t [1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1]\n",
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"3-769 307.60% (904m 20s) logloss=17.27 \t accuracy=0.50 \t loss=0.6945109963417053\n",
"pred_y:\t [0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 1 1]\n",
"target_y:\t [0 1 1 0 1 1 1 1 1 0 0 0 1 0 0 1 0 0 1 0]\n",
"3-789 315.60% (907m 19s) logloss=13.82 \t accuracy=0.60 \t loss=0.6908108592033386\n",
"pred_y:\t [1 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1]\n",
"target_y:\t [1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 0 1 1 1 1]\n",
"3-809 323.60% (909m 46s) logloss=15.54 \t accuracy=0.55 \t loss=0.6973456144332886\n",
"pred_y:\t [0 0 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 1 0 1]\n",
"target_y:\t [1 0 0 1 1 1 1 0 0 0 0 0 1 1 1 0 1 0 1 1]\n",
"3-829 331.60% (912m 25s) logloss=19.00 \t accuracy=0.45 \t loss=0.6773894429206848\n",
"pred_y:\t [0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\n",
"target_y:\t [1 0 0 0 1 0 1 1 0 1 0 1 1 0 0 1 1 0 0 1]\n",
"3-849 339.60% (914m 47s) logloss=15.54 \t accuracy=0.55 \t loss=0.6820051670074463\n",
"pred_y:\t [0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0]\n",
"target_y:\t [0 1 0 0 1 1 0 1 1 0 0 0 0 1 1 0 1 1 0 1]\n",
"3-869 347.60% (920m 18s) logloss=13.82 \t accuracy=0.60 \t loss=0.6797436475753784\n",
"pred_y:\t [0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0]\n",
"target_y:\t [0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1]\n",
"3-889 355.60% (923m 7s) logloss=10.36 \t accuracy=0.70 \t loss=0.6741222143173218\n",
"pred_y:\t [0 0 1 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0]\n",
"target_y:\t [0 1 1 0 0 0 1 0 0 0 1 0 1 1 0 1 0 1 1 1]\n",
"3-909 363.60% (925m 22s) logloss=13.82 \t accuracy=0.60 \t loss=0.6847935914993286\n",
"pred_y:\t [1 1 0 1 1 0 1 0 0 0 1 0 1 0 1 0 0 1 1 1]\n",
"target_y:\t [0 0 0 0 1 1 1 0 0 1 0 1 1 0 1 1 0 0 1 1]\n",
"3-929 371.60% (927m 38s) logloss=15.54 \t accuracy=0.55 \t loss=0.7047096490859985\n",
"pred_y:\t [0 0 1 1 0 0 0 0 1 0 0 1 0 1 0 0 1 0 1 0]\n",
"target_y:\t [0 0 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0]\n",
"3-949 379.60% (935m 52s) logloss=17.27 \t accuracy=0.50 \t loss=0.6819921731948853\n",
"pred_y:\t [1 0 0 0 1 1 1 1 1 0 0 0 1 1 0 1 0 0 0 0]\n",
"target_y:\t [1 0 0 1 1 1 0 1 1 0 1 1 0 1 0 1 1 0 1 0]\n",
"3-969 387.60% (944m 46s) logloss=12.09 \t accuracy=0.65 \t loss=0.6759116053581238\n",
"pred_y:\t [1 0 0 1 1 0 0 1 1 1 1 0 1 0 0 0 1 1 0 1]\n",
"target_y:\t [1 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0]\n",
"3-989 395.60% (953m 30s) logloss=19.00 \t accuracy=0.45 \t loss=0.6978853344917297\n",
"pred_y:\t [1 0 1 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1 0]\n",
"target_y:\t [0 1 0 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 0]\n",
"3-1009 403.60% (1268m 54s) logloss=22.45 \t accuracy=0.35 \t loss=0.6969307065010071\n",
"pred_y:\t [0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0]\n",
"target_y:\t [0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1]\n",
"3-1029 411.60% (1271m 31s) logloss=22.45 \t accuracy=0.35 \t loss=0.7028256058692932\n",
"pred_y:\t [1 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1]\n",
"target_y:\t [0 1 1 0 1 1 0 1 0 1 0 1 1 1 1 1 0 1 1 0]\n",
"3-1049 419.60% (1273m 47s) logloss=22.45 \t accuracy=0.35 \t loss=0.7021511793136597\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Process Process-20:\n",
"Process Process-19:\n",
"Traceback (most recent call last):\n",
"Traceback (most recent call last):\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
" self.run()\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
" self.run()\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
" self._target(*self._args, **self._kwargs)\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
" self._target(*self._args, **self._kwargs)\n",
" File \"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 50, in _worker_loop\n",
" r = index_queue.get()\n",
" File \"/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 50, in _worker_loop\n",
" r = index_queue.get()\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n",
" res = self._reader.recv_bytes()\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
" with self._rlock:\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
" buf = self._recv_bytes(maxlength)\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
" buf = self._recv(4)\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
" return self._semlock.__enter__()\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
" chunk = read(handle, remaining)\n",
"KeyboardInterrupt\n",
"KeyboardInterrupt\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m~/.virtualenvs/python3.6/lib/python3.6/site-packages/torch/nn/modules/activation.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"from sklearn.metrics import log_loss\n",
"\n",
"Epoch = 5\n",
"print_every = 20\n",
"max_step = len(X_train_d)/BATCH_SIZE\n",
"\n",
"# 跟踪绘图的损失\n",
"current_loss = 0\n",
"all_losses = []\n",
"\n",
"for epoch in range(Epoch):\n",
" for step, (batch_x, batch_y) in enumerate(train_loader): # 每一步 loader 释放一小批数据用来学习\n",
" b_x = Variable(batch_x) # batch x\n",
" b_y = Variable(batch_y) # batch y\n",
" \n",
" out = cnn(b_x) # 喂给 net 训练数据 x, 输出分析值\n",
" loss = loss_func(out, b_y) # 计算两者的误差\n",
"\n",
" optimizer.zero_grad() # 清空上一步的残余更新参数值\n",
" loss.backward() # 误差反向传播, 计算参数更新值\n",
" optimizer.step() # 将参数更新值施加到 net 的 parameters 上\n",
"\n",
" current_loss += loss.data[0]\n",
" # print(F.softmax(out), '---', torch.max(F.softmax(out), 1), 'xxx', torch.max(F.softmax(out), 1)[1])\n",
" if step % print_every == print_every-1:\n",
" # softmax 用来计算输出分类的概率，然后max是选出最大的一组：(概率值，分类值)\n",
" prediction = torch.max(F.softmax(out, dim=1), 1)[1]\n",
" pred_y = prediction.data.numpy().squeeze()\n",
" target_y = b_y.data.numpy()\n",
" print(\"pred_y:\\t\", pred_y)\n",
" print(\"target_y:\\t\", target_y)\n",
" logloss = log_loss(target_y, pred_y, eps=1e-15)\n",
" accuracy = sum(pred_y == target_y)/len(target_y) # 预测中有多少和真实值一样\n",
"\n",
" # 总次数\n",
" loop_step = epoch*max_step + step\n",
" total_step = Epoch*max_step\n",
" print('%d-%d %.2f%% (%s) logloss=%.2f \\t accuracy=%.2f \\t loss=%s' % (epoch, loop_step, loop_step/total_step*100, timeSince(start), logloss, accuracy, loss.data[0]))\n",
"\n",
" all_losses.append(current_loss/print_every)\n",
" current_loss = 0\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import log_loss\n",
"\n",
"Epoch = 1\n",
"print_every = 100\n",
"max_step = len(X_train_d)\n",
"\n",
"# 跟踪绘图的损失\n",
"current_loss = 0\n",
"all_losses = []\n",
"pre_result = []\n",
"rea_result = []\n",
"\n",
"for epoch in range(Epoch):\n",
" # 优化为批处理\n",
" \n",
" # step = len(X_train_d)/BATCH_SIZE (train_loader 为 BATCH_SIZE 大小的集合)\n",
" for step, (x, y) in enumerate(zip(X_train_d[1384:], y_train_d[1384:])):\n",
" b_x = prepare_sequence(x)\n",
" b_y = Variable(torch.LongTensor([y])) # batch y\n",
" \n",
" out = cnn(b_x) # 喂给 net 训练数据 x, 输出分析值\n",
" loss = loss_func(out, b_y) # 计算两者的误差\n",
"\n",
" optimizer.zero_grad() # 清空上一步的残余更新参数值\n",
" loss.backward() # 误差反向传播, 计算参数更新值\n",
" optimizer.step() # 将参数更新值施加到 net 的 parameters 上\n",
"\n",
" current_loss += loss.data[0]\n",
"\n",
" prediction = torch.max(F.softmax(out, dim=1), 1)[1]\n",
" pred_y = prediction.data.numpy()\n",
" target_y = b_y.data.numpy()\n",
" \n",
"# print('预测---', pred_y)\n",
"# print('目标---', target_y)\n",
" \n",
"# if step>2:\n",
"# break\n",
"\n",
" # softmax 用来计算输出分类的概率，然后max是选出最大的一组：(概率值，分类值)\n",
" prediction = torch.max(F.softmax(out, dim=1), 1)[1]\n",
" pre_result.append(prediction.data.numpy()[0])\n",
" rea_result.append(b_y.data.numpy()[0])\n",
" \n",
" # print(F.softmax(out), '---', torch.max(F.softmax(out), 1), 'xxx', torch.max(F.softmax(out), 1)[1])\n",
" if step % print_every == print_every-1:\n",
"# print('预测---', pred_y)\n",
"# print('目标---', target_y)\n",
" logloss = log_loss(rea_result, pre_result, eps=1e-15)\n",
"# print(\"pre_result: \\t\", pre_result)\n",
"# print(\"rea_result: \\t\", rea_result)\n",
" accuracy = sum(np.array(pre_result) == np.array(rea_result))/len(rea_result) # 预测中有多少和真实值一样\n",
" \n",
" # 总次数\n",
" loop_step = epoch*max_step + step\n",
" total_step = Epoch*max_step\n",
" \n",
" print('%d-%d %.2f%% (%s) logloss=%.2f \\t accuracy=%.2f \\t loss=%s' % (epoch, loop_step, loop_step/total_step*100, timeSince(start), logloss, accuracy, loss.data[0]))\n",
"\n",
" all_losses.append(current_loss/print_every)\n",
" current_loss = 0\n",
" pre_result = []\n",
" rea_result = []\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"一，train loss与test loss结果分析\n",
"\n",
"* train loss 不断下降，test loss不断下降，说明网络仍在学习;\n",
"* train loss 不断下降，test loss趋于不变，说明网络过拟合;\n",
"* train loss 趋于不变，test loss不断下降，说明数据集100%有问题;\n",
"* train loss 趋于不变，test loss趋于不变，说明学习遇到瓶颈，需要减小学习率或批量数目;\n",
"* train loss 不断上升，test loss不断上升，说明网络结构设计不当，训练超参数设置不当，数据集经过清洗等问题。\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as ticker\n",
"\n",
"plt.figure()\n",
"plt.plot(all_losses)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 234,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as ticker\n",
"\n",
"plt.figure()\n",
"plt.plot(all_losses)"
]
},
{
"cell_type": "code",
"execution_count": 235,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3m 30s) logloss=14.94 \t accuracy=0.57\n"
]
}
],
"source": [
"# 看看测试集合的正确率多高, 发现效果很差，说明一点。。（数据不均匀）\n",
"\n",
"out_t = cnn(Variable(te_x))\n",
"\n",
"# softmax 用来计算输出分类的概率，然后max是选出最大的一组：(概率值，分类值)\n",
"prediction_t = torch.max(F.softmax(out_t, dim=1), 1)[1]\n",
"pred_t_y = prediction_t.data.numpy().squeeze()\n",
"target_t_y = Variable(te_y).data.numpy()\n",
"logloss_t = log_loss(target_t_y, pred_t_y, eps=1e-15)\n",
"accuracy_t = sum(pred_t_y == target_t_y)/len(target_t_y) # 预测中有多少和真实值一样\n",
"print('(%s) logloss=%.2f \\t accuracy=%.2f' % (timeSince(start), logloss_t, accuracy_t))"
]
},
{
"cell_type": "code",
"execution_count": 236,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3m 49s) logloss=14.25 \t accuracy=0.59\n"
]
}
],
"source": [
"# 又回头看看训练集合的正确率多高，严重说明：数据在采样的时候不均匀，倒是数据有丢失没学习到\n",
"\n",
"out_t = cnn(Variable(tr_x[:10000]))\n",
"\n",
"# softmax 用来计算输出分类的概率，然后max是选出最大的一组：(概率值，分类值)\n",
"prediction_t = torch.max(F.softmax(out_t, dim=1), 1)[1]\n",
"pred_t_y = prediction_t.data.numpy().squeeze()\n",
"target_t_y = Variable(tr_y[:10000]).data.numpy()\n",
"logloss_t = log_loss(target_t_y, pred_t_y, eps=1e-15)\n",
"accuracy_t = sum(pred_t_y == target_t_y)/len(target_t_y) # 预测中有多少和真实值一样\n",
"print('(%s) logloss=%.2f \\t accuracy=%.2f' % (timeSince(start), logloss_t, accuracy_t))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* * * \n",
"\n",
"其他的信息"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 加载训练好的词向量\n",
"\n",
"from gensim.models.word2vec import Word2Vec\n",
"\n",
"model = Word2Vec.load_word2vec_format(\"vector.txt\", binary=False) # C text format\n",
"# model = Word2Vec.load_word2vec_format(\"vector.bin\", binary=True) # C"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 加载 google 的词向量，查看单词之间关系\n",
"\n",
"from gensim.models.word2vec import Word2Vec \n",
"model = Word2Vec.load_word2vec_format(\"GoogleNews-vectors-negative300.bin\", binary=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 测试预测效果\n",
"\n",
"print(model.most_similar(positive=[\"woman\", \"king\"], negative=[\"man\"], topn=5))\n",
"print(model.most_similar(positive=[\"biggest\", \"small\"], negative=[\"big\"], topn=5))\n",
"print(model.most_similar(positive=[\"ate\", \"speak\"], negative=[\"eat\"], topn=5))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"with open(\"food_words.txt\", \"r\") as infile:\n",
" food_words = infile.readlines()\n",
" \n",
"with open(\"sports_words.txt\", \"r\") as infile:\n",
" food_words = infile.readlines()\n",
" \n",
"with open(\"weather_words.txt\", \"r\") as infile:\n",
" food_words = infile.readlines()\n",
" \n",
"def getWordVecs(words):\n",
" vec = []\n",
" for word in words:\n",
" word = word.replace(\"\\n\", \"\")\n",
" try:\n",
" vecs.append(model[word].reshape((1, 300)))\n",
" except KeyError:\n",
" continue\n",
" \n",
" # numpy提供了numpy.concatenate((a1,a2,...), axis=0)函数。能够一次完成多个数组的拼接\n",
" \"\"\"\n",
" >>> a=np.array([1,2,3])\n",
" >>> b=np.array([11,22,33])\n",
" >>> c=np.array([44,55,66])\n",
" >>> np.concatenate((a,b,c),axis=0) # 默认情况下，axis=0可以不写\n",
" array([ 1, 2, 3, 11, 22, 33, 44, 55, 66]) #对于一维数组拼接，axis的值不影响最后的结果\n",
" \"\"\"\n",
" vecs = np.concatenate(vecs)\n",
" return np.array(vecs, dtype=\"float\")\n",
"\n",
"food_vecs = getWordVecs(food_words)\n",
"sports_vecs = getWordVecs(sports_words)\n",
"weather_vecs = getWordVecs(weather_words)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 利用 TSNE 和 matplotlib 对分类结果进行可视化处理\n",
"\n",
"from sklearn.manifold import TSEN\n",
"import matplotlib.pyplot as plt\n",
"\n",
"ts = TSEN(2)\n",
"reduced_vecs = ts.fit_transform(np.concatenate((food_vecs, sports_vecs, weather_vecs)))\n",
"\n",
"for i in range(len(reduced_vecs)):\n",
" if i < len(food_vecs):\n",
" color = \"b\"\n",
" elif i >= len(food_vecs) and i <(len(food_vecs)+len(sports_vecs)):\n",
" color = \"r\"\n",
" else:\n",
" color = \"g\"\n",
" \n",
" plt.plot(reduced_vecs[i, 0], reduced_vecs[i, 1], marker=\"0\", color=color, marksize=8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 首先，我们导入数据并构建 Word2Vec 模型：\n",
"\n",
"from sklearn.cross_validation import train_ _test_ _split\n",
"from gensim.models.word2vec import Word2Vec\n",
"\n",
"with open('twitter.data/pos_ tweets.txt', 'r') as infile:\n",
" pos_tweets= infile.readlines()\n",
"\n",
"with open(' twitter_ data/neg_ tweets.txt', 'r') as infile:\n",
" neg_ _tweets = infile.readlines()\n",
"\n",
"# use 1for positive sentiment,0 for negative\n",
"Y= np.concatenate((np.ones( len (pos_tweets )) ，np.zeros(len(neg_tweets))))\n",
"\n",
"x_train,x_test,y_train,y_test = train_test_split(np.concatenate((pos_tweets, neg_tweets)), y, test_size=0.2)\n",
"# Do some very minor text preprocessing\n",
"\n",
"def cleanText(corpus):\n",
" corpus= [z.lower( ).replace(' \\n' , '').split() for z in corpus]\n",
" return corpus\n",
"\n",
"x_ train= cleanText(x_ train)\n",
"x_ test= cleanText (x_ _test)\n",
"\n",
"n _dim= 300\n",
"#Initialize model and build vocab\n",
"imdb_w2v= Word2Vec(size=n dim, min_count=10)\n",
"imdb_w2v.build_vocab(x_ _train)\n",
"#Train the model over train_ _reviews (this may take several minutes)\n",
"imdb_w2v.train( x_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 接下来，为了利用下面的函数获得推文中所有词向量的平均值，我们必须构建作为输入文本的词向量。\n",
"\n",
"def buildWordVector(text, size):\n",
" vec = np.zeros(size).reshape((1，size))\n",
" count= 0.\n",
"\n",
" for word in text :\n",
" try:\n",
" vec += imdb_w2v[word].reshape( (1，size) )\n",
" count += 1.\n",
" except KeyError:\n",
" continue\n",
" if count != 0:\n",
" vec 1'= count\n",
" return vec"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 调整数据集的量纲是数据标准化处理的一部分，我们通常将数据集转化成服从均值为零的高斯分布，这说明数值大于均值表示乐观，反之则表示悲观。为了使模型更有效，许多机器学习模型需要预先处理数据集的量纲，特别是文本分类器这类具有许多变量的模型。\n",
"\n",
"from sklearn.preprocessing import scale\n",
"\n",
"train_vecs = np.concatenate([buildWordVector(z ，n_dim) for z in x_train])\n",
"train_vecs= scale(train_vecs)\n",
"\n",
"# Train word2vec on test tweets\n",
"imdb_w2v.train(x_test)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 最后我们需要建立测试集向量并对其标准化处理：\n",
"\n",
"#Build test tweet vectors then scale\n",
"test_vecs = np.concatenate( [buildWordVector( Z，n _dim) for z in x _test ])\n",
"test_vecs = scale(test_vecs)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"接下来我们想要通过计算测试集的预测精度和 ROC 曲线来验证分类器的有效性。 ROC 曲线衡量当模型参数调整的时候，其真阳性率和假阳性率的变化情况。在我们的案例中，我们调整的是分类器模型截断阈值的概率。一般来说，ROC 曲线下的面积（AUC）越大，该模型的表现越好。你可以在这里找到更多关于 ROC 曲线的资料\n",
"\n",
"（https://en.wikipedia.org/wiki/Receiver_operating_characteristic）\n",
"\n",
"在这个案例中我们使用罗吉斯回归的随机梯度下降法作为分类器算法。\n",
"\"\"\"\n",
"\n",
"#Use classification algorithm (i.e.Stochastic Logistic Regression) on training set, then assess model performance on test set\n",
"\n",
"from sklearn.linear model import SGDClassifier\n",
"lr = SGDClassifier(loss='log' ，penalty='11' )\n",
"lr.fit(train_vecs, y_train)\n",
"print' Test Accuracy: %.2f' % r.score(test vecs, y_test )\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 随后我们利用 matplotlib 和 metric 库来构建 ROC 曲线\n",
"\n",
"#Crea t e ROC curve\n",
"from sklearn.metrics import roc_curve, auc\n",
"import matplotlib.pyplot as plt\n",
"\n",
"pred_probas = lr.predict_proba(test_vecs)[:, 1]\n",
"\n",
"fpr, tpr, _ = roc_curve(y_test, pred_probas )\n",
"roc_auc = auc(fpr, tpr)\n",
"\n",
"plt.plot(fpr,tpr,label='area = %.2f' % roc_ auc)\n",
"plt.plot([0,1]，[0，1],'k--')\n",
"plt. xlim( [0. 0 ，1. 0 ])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.legend(loc='lower right')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/README.md
================================================
# 比赛分析
步骤:
```
一. 数据分析
1. 下载并加载数据
2. 总体预览:了解每列数据的含义,数据的格式等
3. 数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备
二. 特征工程
1.根据业务,常识,以及第二步的数据分析构造特征工程.
2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)
三. 模型选择
1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.
2.比较各个模型的分数,然后取效果较好的模型作为基础模型.
四. 模型融合
五. 修改特征和模型参数
1.可以通过添加或者修改特征,提高模型的上限.
2.通过修改模型的参数,是模型逼近上限
```
* * *
* 比赛地址: https://www.kaggle.com/c/word2vec-nlp-tutorial
* 参考地址: https://www.cnblogs.com/zhao441354231/p/6056914.html
* 参考地址: https://blog.csdn.net/lijingpengchina/article/details/52250765
# 加载包
```python
import pandas as pd
import numpy as np
import re
from bs4 import BeautifulSoup
```
# 读取数据
```python
root_dir = "/opt/data/kaggle/getting-started/word2vec-nlp-tutorial"
# 载入数据集
train = pd.read_csv('%s/%s' % (root_dir, 'labeledTrainData.tsv'), header=0, delimiter="\t", quoting=3)
test = pd.read_csv('%s/%s' % (root_dir, 'testData.tsv'), header=0, delimiter="\t", quoting=3)
print(train.shape)
print(train.columns.values)
print(train.head(3))
print(test.head(3))
```
(25000, 3)
['id' 'sentiment' 'review']
id sentiment review
0 "5814_8" 1 "With all this stuff going down at the moment ...
1 "2381_9" 1 "\"The Classic War of the Worlds\" by Timothy ...
2 "7759_3" 0 "The film starts with a manager (Nicholas Bell...
id review
0 "12311_10" "Naturally in a film who's main themes are of ...
1 "8348_2" "This movie is a disaster within a disaster fi...
2 "5828_4" "All in all, this is a movie for kids. We saw ...
```python
# 去除评论中的HTML标签
print('\n处理前: \n', train['review'][0])
example1 = BeautifulSoup(train['review'][0], "html.parser")
import re
# Use regular expressions to do a find-and-replace
letters_only = re.sub('[^a-zA-Z]', # 搜寻的pattern
' ', # 用来替代的pattern(空格)
example1.get_text()) # 待搜索的text
print(letters_only)
lower_case = letters_only.lower() # Convert to lower case
words = lower_case.split() # Split into word
print('\n处理后: \n', words)
```
处理前:
"With all this stuff going down at the moment with MJ i've started listening to his music, watching the odd documentary here and there, watched The Wiz and watched Moonwalker again. Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent. Moonwalker is part biography, part feature film which i remember going to see at the cinema when it was originally released. Some of it has subtle messages about MJ's feeling towards the press and also the obvious message of drugs are bad m'kay.
Visually impressive but of course this is all about Michael Jackson so unless you remotely like MJ in anyway then you are going to hate this and find it boring. Some may call MJ an egotist for consenting to the making of this movie BUT MJ and most of his fans would say that he made it for the fans which if true is really nice of him.
The actual feature film bit when it finally starts is only on for 20 minutes or so excluding the Smooth Criminal sequence and Joe Pesci is convincing as a psychopathic all powerful drug lord. Why he wants MJ dead so bad is beyond me. Because MJ overheard his plans? Nah, Joe Pesci's character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno, maybe he just hates MJ's music.
Lots of cool things in this like MJ turning into a car and a robot and the whole Speed Demon sequence. Also, the director must have had the patience of a saint when it came to filming the kiddy Bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene.
Bottom line, this movie is for people who like MJ on one level or another (which i think is most people). If not, then stay away. It does try and give off a wholesome message and ironically MJ's bestest buddy in this movie is a girl! Michael Jackson is truly one of the most talented people ever to grace this planet but is he guilty? Well, with all the attention i've gave this subject....hmmm well i don't know because people can be different behind closed doors, i know this for a fact. He is either an extremely nice but stupid guy or one of the most sickest liars. I hope he is not the latter."
With all this stuff going down at the moment with MJ i ve started listening to his music watching the odd documentary here and there watched The Wiz and watched Moonwalker again Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent Moonwalker is part biography part feature film which i remember going to see at the cinema when it was originally released Some of it has subtle messages about MJ s feeling towards the press and also the obvious message of drugs are bad m kay Visually impressive but of course this is all about Michael Jackson so unless you remotely like MJ in anyway then you are going to hate this and find it boring Some may call MJ an egotist for consenting to the making of this movie BUT MJ and most of his fans would say that he made it for the fans which if true is really nice of him The actual feature film bit when it finally starts is only on for minutes or so excluding the Smooth Criminal sequence and Joe Pesci is convincing as a psychopathic all powerful drug lord Why he wants MJ dead so bad is beyond me Because MJ overheard his plans Nah Joe Pesci s character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno maybe he just hates MJ s music Lots of cool things in this like MJ turning into a car and a robot and the whole Speed Demon sequence Also the director must have had the patience of a saint when it came to filming the kiddy Bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene Bottom line this movie is for people who like MJ on one level or another which i think is most people If not then stay away It does try and give off a wholesome message and ironically MJ s bestest buddy in this movie is a girl Michael Jackson is truly one of the most talented people ever to grace this planet but is he guilty Well with all the attention i ve gave this subject hmmm well i don t know because people can be different behind closed doors i know this for a fact He is either an extremely nice but stupid guy or one of the most sickest liars I hope he is not the latter
处理后:
['with', 'all', 'this', 'stuff', 'going', 'down', 'at', 'the', 'moment', 'with', 'mj', 'i', 've', 'started', 'listening', 'to', 'his', 'music', 'watching', 'the', 'odd', 'documentary', 'here', 'and', 'there', 'watched', 'the', 'wiz', 'and', 'watched', 'moonwalker', 'again', 'maybe', 'i', 'just', 'want', 'to', 'get', 'a', 'certain', 'insight', 'into', 'this', 'guy', 'who', 'i', 'thought', 'was', 'really', 'cool', 'in', 'the', 'eighties', 'just', 'to', 'maybe', 'make', 'up', 'my', 'mind', 'whether', 'he', 'is', 'guilty', 'or', 'innocent', 'moonwalker', 'is', 'part', 'biography', 'part', 'feature', 'film', 'which', 'i', 'remember', 'going', 'to', 'see', 'at', 'the', 'cinema', 'when', 'it', 'was', 'originally', 'released', 'some', 'of', 'it', 'has', 'subtle', 'messages', 'about', 'mj', 's', 'feeling', 'towards', 'the', 'press', 'and', 'also', 'the', 'obvious', 'message', 'of', 'drugs', 'are', 'bad', 'm', 'kay', 'visually', 'impressive', 'but', 'of', 'course', 'this', 'is', 'all', 'about', 'michael', 'jackson', 'so', 'unless', 'you', 'remotely', 'like', 'mj', 'in', 'anyway', 'then', 'you', 'are', 'going', 'to', 'hate', 'this', 'and', 'find', 'it', 'boring', 'some', 'may', 'call', 'mj', 'an', 'egotist', 'for', 'consenting', 'to', 'the', 'making', 'of', 'this', 'movie', 'but', 'mj', 'and', 'most', 'of', 'his', 'fans', 'would', 'say', 'that', 'he', 'made', 'it', 'for', 'the', 'fans', 'which', 'if', 'true', 'is', 'really', 'nice', 'of', 'him', 'the', 'actual', 'feature', 'film', 'bit', 'when', 'it', 'finally', 'starts', 'is', 'only', 'on', 'for', 'minutes', 'or', 'so', 'excluding', 'the', 'smooth', 'criminal', 'sequence', 'and', 'joe', 'pesci', 'is', 'convincing', 'as', 'a', 'psychopathic', 'all', 'powerful', 'drug', 'lord', 'why', 'he', 'wants', 'mj', 'dead', 'so', 'bad', 'is', 'beyond', 'me', 'because', 'mj', 'overheard', 'his', 'plans', 'nah', 'joe', 'pesci', 's', 'character', 'ranted', 'that', 'he', 'wanted', 'people', 'to', 'know', 'it', 'is', 'he', 'who', 'is', 'supplying', 'drugs', 'etc', 'so', 'i', 'dunno', 'maybe', 'he', 'just', 'hates', 'mj', 's', 'music', 'lots', 'of', 'cool', 'things', 'in', 'this', 'like', 'mj', 'turning', 'into', 'a', 'car', 'and', 'a', 'robot', 'and', 'the', 'whole', 'speed', 'demon', 'sequence', 'also', 'the', 'director', 'must', 'have', 'had', 'the', 'patience', 'of', 'a', 'saint', 'when', 'it', 'came', 'to', 'filming', 'the', 'kiddy', 'bad', 'sequence', 'as', 'usually', 'directors', 'hate', 'working', 'with', 'one', 'kid', 'let', 'alone', 'a', 'whole', 'bunch', 'of', 'them', 'performing', 'a', 'complex', 'dance', 'scene', 'bottom', 'line', 'this', 'movie', 'is', 'for', 'people', 'who', 'like', 'mj', 'on', 'one', 'level', 'or', 'another', 'which', 'i', 'think', 'is', 'most', 'people', 'if', 'not', 'then', 'stay', 'away', 'it', 'does', 'try', 'and', 'give', 'off', 'a', 'wholesome', 'message', 'and', 'ironically', 'mj', 's', 'bestest', 'buddy', 'in', 'this', 'movie', 'is', 'a', 'girl', 'michael', 'jackson', 'is', 'truly', 'one', 'of', 'the', 'most', 'talented', 'people', 'ever', 'to', 'grace', 'this', 'planet', 'but', 'is', 'he', 'guilty', 'well', 'with', 'all', 'the', 'attention', 'i', 've', 'gave', 'this', 'subject', 'hmmm', 'well', 'i', 'don', 't', 'know', 'because', 'people', 'can', 'be', 'different', 'behind', 'closed', 'doors', 'i', 'know', 'this', 'for', 'a', 'fact', 'he', 'is', 'either', 'an', 'extremely', 'nice', 'but', 'stupid', 'guy', 'or', 'one', 'of', 'the', 'most', 'sickest', 'liars', 'i', 'hope', 'he', 'is', 'not', 'the', 'latter']
```python
def review_to_wordlist(review):
'''
把IMDB的评论转成词序列
参考：http://blog.csdn.net/longxinchen_ml/article/details/50629613
'''
# 去掉HTML标签，拿到内容
review_text = BeautifulSoup(review, "html.parser").get_text()
# 用正则表达式取出符合规范的部分
review_text = re.sub("[^a-zA-Z]", " ", review_text)
# 小写化所有的词，并转成词list
words = review_text.lower().split()
# 返回words
return words
# 预处理数据
label = train['sentiment']
train_data = []
for i in range(len(train['review'])):
train_data.append(' '.join(review_to_wordlist(train['review'][i])))
test_data = []
for i in range(len(test['review'])):
test_data.append(' '.join(review_to_wordlist(test['review'][i])))
# 预览数据
print(train_data[0], '\n')
print(test_data[0])
```
with all this stuff going down at the moment with mj i ve started listening to his music watching the odd documentary here and there watched the wiz and watched moonwalker again maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent moonwalker is part biography part feature film which i remember going to see at the cinema when it was originally released some of it has subtle messages about mj s feeling towards the press and also the obvious message of drugs are bad m kay visually impressive but of course this is all about michael jackson so unless you remotely like mj in anyway then you are going to hate this and find it boring some may call mj an egotist for consenting to the making of this movie but mj and most of his fans would say that he made it for the fans which if true is really nice of him the actual feature film bit when it finally starts is only on for minutes or so excluding the smooth criminal sequence and joe pesci is convincing as a psychopathic all powerful drug lord why he wants mj dead so bad is beyond me because mj overheard his plans nah joe pesci s character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno maybe he just hates mj s music lots of cool things in this like mj turning into a car and a robot and the whole speed demon sequence also the director must have had the patience of a saint when it came to filming the kiddy bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene bottom line this movie is for people who like mj on one level or another which i think is most people if not then stay away it does try and give off a wholesome message and ironically mj s bestest buddy in this movie is a girl michael jackson is truly one of the most talented people ever to grace this planet but is he guilty well with all the attention i ve gave this subject hmmm well i don t know because people can be different behind closed doors i know this for a fact he is either an extremely nice but stupid guy or one of the most sickest liars i hope he is not the latter
naturally in a film who s main themes are of mortality nostalgia and loss of innocence it is perhaps not surprising that it is rated more highly by older viewers than younger ones however there is a craftsmanship and completeness to the film which anyone can enjoy the pace is steady and constant the characters full and engaging the relationships and interactions natural showing that you do not need floods of tears to show emotion screams to show fear shouting to show dispute or violence to show anger naturally joyce s short story lends the film a ready made structure as perfect as a polished diamond but the small changes huston makes such as the inclusion of the poem fit in neatly it is truly a masterpiece of tact subtlety and overwhelming beauty
## 特征处理
直接丢给计算机这些词文本，计算机是无法计算的，因此我们需要把文本转换为向量，有几种常见的文本向量处理方法，比如：
1. 单词计数
2. TF-IDF向量
3. Word2vec向量
我们先使用TF-IDF来试一下。
### 2.TF-IDF向量
```python
from sklearn.feature_extraction.text import TfidfVectorizer as TFIDF
# 参考：http://blog.csdn.net/longxinchen_ml/article/details/50629613
"""
min_df: 最小支持度为2（词汇出现的最小次数）
max_features: 默认为None，可设为int，对所有关键词的term frequency进行降序排序，只取前max_features个作为关键词集
strip_accents: 将使用ascii或unicode编码在预处理步骤去除raw document中的重音符号
analyzer: 设置返回类型
token_pattern: 表示token的正则表达式，需要设置analyzer == 'word'，默认的正则表达式选择2个及以上的字母或数字作为token，标点符号默认当作token分隔符，而不会被当作token
ngram_range: 词组切分的长度范围
use_idf: 启用逆文档频率重新加权
use_idf：默认为True，权值是tf*idf，如果设为False，将不使用idf，就是只使用tf，相当于CountVectorizer了。
smooth_idf: idf平滑参数，默认为True，idf=ln((文档总数+1)/(包含该词的文档数+1))+1，如果设为False，idf=ln(文档总数/包含该词的文档数)+1
sublinear_tf: 默认为False，如果设为True，则替换tf为1 + log(tf)
stop_words: 设置停用词，设为english将使用内置的英语停用词，设为一个list可自定义停用词，设为None不使用停用词，设为None且max_df∈[0.7, 1.0)将自动根据当前的语料库建立停用词表
"""
tfidf = TFIDF(min_df=2,
max_features=None,
strip_accents='unicode',
analyzer='word',
token_pattern=r'\w{1,}',
ngram_range=(1, 3), # 二元文法模型
use_idf=1,
smooth_idf=1,
sublinear_tf=1,
stop_words = 'english') # 去掉英文停用词
# 合并训练和测试集以便进行TFIDF向量化操作
data_all = train_data + test_data
len_train = len(train_data)
tfidf.fit(data_all)
data_all = tfidf.transform(data_all)
# 恢复成训练集和测试集部分
train_x = data_all[:len_train]
test_x = data_all[len_train:]
print('TF-IDF处理结束.')
print("train: \n", np.shape(train_x[0]))
print("test: \n", np.shape(test_x[0]))
```
TF-IDF处理结束.
train:
(1, 810866)
test:
(1, 810866)
### 朴素贝叶斯训练
```python
# 朴素贝叶斯训练
from sklearn.naive_bayes import MultinomialNB as MNB
model_NB = MNB() # (alpha=1.0, class_prior=None, fit_prior=True)
# 为了在预测的时候使用
model_NB.fit(train_x, label)
from sklearn.model_selection import cross_val_score
import numpy as np
print("多项式贝叶斯分类器10折交叉验证得分: \n", cross_val_score(model_NB, train_x, label, cv=10, scoring='roc_auc'))
print("\n多项式贝叶斯分类器10折交叉验证得分: ", np.mean(cross_val_score(model_NB, train_x, label, cv=10, scoring='roc_auc')))
```
多项式贝叶斯分类器10折交叉验证得分:
[0.95134592 0.94728448 0.951648 0.94707712 0.95122816 0.94939968
0.95240704 0.95434432 0.94438528 0.94930816]
多项式贝叶斯分类器10折交叉验证得分: 0.949842816
```python
test_predicted = np.array(model_NB.predict(test_x))
print('保存结果...')
submission_df = pd.DataFrame(data ={'id': test['id'], 'sentiment': test_predicted})
print(submission_df.head(10))
submission_df.to_csv('/Users/jiangzl/Desktop/submission_br.csv',columns = ['id','sentiment'], index = False)
# nb_output = pd.DataFrame(data=test_predicted, columns=['sentiment'])
# nb_output['id'] = test['id']
# nb_output = nb_output[['id', 'sentiment']]
# nb_output.to_csv('nb_output.csv', index=False)
print('结束.')
'''
1.提交最终的结果到kaggle，AUC为：0.85728，排名300左右，50%的水平
2. ngram_range = 3, 三元文法，AUC为0.85924
'''
```
保存结果...
id sentiment
0 "12311_10" 1
1 "8348_2" 0
2 "5828_4" 1
3 "7186_2" 1
4 "12128_7" 1
5 "2913_8" 1
6 "4396_1" 0
7 "395_2" 0
8 "10616_1" 0
9 "9074_9" 1
结束.
'\n1.提交最终的结果到kaggle，AUC为：0.85728，排名300左右，50%的水平\n2. ngram_range = 3, 三元文法，AUC为0.85924\n'
### 逻辑回归
```python
from sklearn.linear_model import LogisticRegression as LR
from sklearn.model_selection import GridSearchCV
# 设定grid search的参数
grid_values = {'C': [1, 15, 30, 50]}
# grid_values = {'C': [30]}
# 设定打分为roc_auc
"""
penalty: l1 or l2, 用于指定惩罚中使用的标准。
"""
model_LR = GridSearchCV(LR(penalty='l2', dual=True, random_state=0), grid_values, scoring='roc_auc', cv=20)
model_LR.fit(train_x, label)
# 20折交叉验证
# GridSearchCV(cv=20,
# estimator=LR(C=1.0,
# class_weight=None,
# dual=True,
# fit_intercept=True,
# intercept_scaling=1,
# penalty='l2',
# random_state=0,
# tol=0.0001),
# fit_params={},
# iid=True,
# n_jobs=1,
# param_grid={'C': [30]},
# pre_dispatch='2*n_jobs',
# refit=True,
# scoring='roc_auc',
# verbose=0)
# 输出结果
# print(model_LR.grid_scores_, '\n', model_LR.best_params_, model_LR.best_params_)
print(model_LR.cv_results_, '\n', model_LR.best_params_, model_LR.best_score_)
```
{'mean_fit_time': array([0.77368994, 1.95680232, 2.88316183, 3.50976259]), 'std_fit_time': array([0.05099312, 0.19345662, 0.39457327, 0.50422455]), 'mean_score_time': array([0.00273149, 0.0025926 , 0.00262785, 0.00249476]), 'std_score_time': array([0.0001698 , 0.00014623, 0.00014215, 0.00024111]), 'param_C': masked_array(data=[1, 15, 30, 50],
mask=[False, False, False, False],
fill_value='?',
dtype=object), 'params': [{'C': 1}, {'C': 15}, {'C': 30}, {'C': 50}], 'split0_test_score': array([0.95273728, 0.95990784, 0.960192 , 0.9602816 ]), 'split1_test_score': array([0.96081408, 0.96953856, 0.96975104, 0.96994816]), 'split2_test_score': array([0.9583616 , 0.96794112, 0.96825856, 0.96836352]), 'split3_test_score': array([0.95249152, 0.96079104, 0.96123136, 0.96137984]), 'split4_test_score': array([0.96460288, 0.9721088 , 0.9724672 , 0.97263104]), 'split5_test_score': array([0.95881216, 0.96733184, 0.96779008, 0.96797184]), 'split6_test_score': array([0.95679232, 0.96563968, 0.96596736, 0.96606976]), 'split7_test_score': array([0.95171072, 0.96053248, 0.96105216, 0.96125952]), 'split8_test_score': array([0.95526656, 0.9604096 , 0.96051712, 0.96053248]), 'split9_test_score': array([0.94979328, 0.95777024, 0.95817472, 0.95834368]), 'split10_test_score': array([0.95965952, 0.9672192 , 0.9675264 , 0.96764672]), 'split11_test_score': array([0.95329024, 0.96009472, 0.96019712, 0.96021504]), 'split12_test_score': array([0.96268544, 0.97140224, 0.97184256, 0.97202944]), 'split13_test_score': array([0.9571968 , 0.96615936, 0.9666048 , 0.96676864]), 'split14_test_score': array([0.95916544, 0.96551936, 0.96583168, 0.96596992]), 'split15_test_score': array([0.96279296, 0.96956928, 0.96978176, 0.96979968]), 'split16_test_score': array([0.95332096, 0.96132352, 0.96161792, 0.96173568]), 'split17_test_score': array([0.94883328, 0.9570816 , 0.95749632, 0.95771136]), 'split18_test_score': array([0.9528448 , 0.96074496, 0.96114176, 0.9612672 ]), 'split19_test_score': array([0.96429824, 0.97186048, 0.972032 , 0.97212416]), 'mean_test_score': array([0.9567735 , 0.9646473 , 0.9649737 , 0.96510246]), 'std_test_score': array([0.0046911 , 0.00476416, 0.00475249, 0.00475557]), 'rank_test_score': array([4, 3, 2, 1], dtype=int32), 'split0_train_score': array([0.99254593, 1. , 1. , 1. ]), 'split1_train_score': array([0.99230078, 1. , 1. , 1. ]), 'split2_train_score': array([0.9923811, 1. , 1. , 1. ]), 'split3_train_score': array([0.9924227, 1. , 1. , 1. ]), 'split4_train_score': array([0.9923401, 1. , 1. , 1. ]), 'split5_train_score': array([0.9924475, 1. , 1. , 1. ]), 'split6_train_score': array([0.99238184, 1. , 1. , 1. ]), 'split7_train_score': array([0.99249388, 1. , 1. , 1. ]), 'split8_train_score': array([0.99257082, 1. , 1. , 1. ]), 'split9_train_score': array([0.99253744, 1. , 1. , 1. ]), 'split10_train_score': array([0.99235201, 1. , 1. , 1. ]), 'split11_train_score': array([0.99243953, 1. , 1. , 1. ]), 'split12_train_score': array([0.99236668, 1. , 1. , 1. ]), 'split13_train_score': array([0.99248181, 1. , 1. , 1. ]), 'split14_train_score': array([0.99254685, 1. , 1. , 1. ]), 'split15_train_score': array([0.99240575, 1. , 1. , 1. ]), 'split16_train_score': array([0.99240521, 1. , 1. , 1. ]), 'split17_train_score': array([0.99248037, 1. , 1. , 1. ]), 'split18_train_score': array([0.99243375, 1. , 1. , 1. ]), 'split19_train_score': array([0.99242053, 1. , 1. , 1. ]), 'mean_train_score': array([0.99243773, 1. , 1. , 1. ]), 'std_train_score': array([7.34564551e-05, 0.00000000e+00, 2.48253415e-17, 2.48253415e-17])}
{'C': 50} 0.965102464
```python
model_LR = LR(penalty='l2', dual=True, random_state=0)
model_LR.fit(train_x, label)
test_predicted = np.array(model_LR.predict(test_x))
print('保存结果...')
submission_df = pd.DataFrame(data ={'id': test['id'], 'sentiment': test_predicted})
print(submission_df.head(10))
submission_df.to_csv('/Users/jiangzl/Desktop/submission_br.csv',columns = ['id','sentiment'], index = False)
print('结束.')
'''
1. 提交最终的结果到kaggle，AUC为：0.88956，排名260左右，比之前贝叶斯模型有所提高
2. 三元文法，AUC为0.89076
'''
```
保存结果...
id sentiment
0 "12311_10" 1
1 "8348_2" 0
2 "5828_4" 1
3 "7186_2" 1
4 "12128_7" 1
5 "2913_8" 1
6 "4396_1" 0
7 "395_2" 0
8 "10616_1" 0
9 "9074_9" 1
结束.
'\n1. 提交最终的结果到kaggle，AUC为：0.88956，排名260左右，比之前贝叶斯模型有所提高\n2. 三元文法，AUC为0.89076\n'
## 3.Word2vec向量
神经网络语言模型L = SUM[log(p(w|contect(w))]，即在w的上下文下计算当前词w的概率，由公式可以看到，我们的核心是计算p(w|contect(w)， Word2vec给出了构造这个概率的一个方法。
```python
import gensim
import nltk
from nltk.corpus import stopwords
tokenizer = nltk.data.load('/opt/data/nlp/nltk_data/tokenizers/punkt/english.pickle')
def review_to_wordlist(review, remove_stopwords=False):
# review = BeautifulSoup(review, "html.parser").get_text()
review_text = re.sub("[^a-zA-Z]"," ", review)
words = review_text.lower().split()
if remove_stopwords:
stops = set(stopwords.words("english"))
words = [w for w in words if not w in stops]
# print(words)
return(words)
def review_to_sentences(review, tokenizer, remove_stopwords=False):
'''
1. 将评论文章，按照句子段落来切分(所以会比文章的数量多很多)
2. 返回句子列表，每个句子由一堆词组成
'''
review = BeautifulSoup(review, "html.parser").get_text()
# raw_sentences 句子段落集合
raw_sentences = tokenizer.tokenize(review)
# print(raw_sentences)
sentences = []
for raw_sentence in raw_sentences:
if len(raw_sentence) > 0:
# 获取句子中的词列表
sentences.append(review_to_wordlist(raw_sentence, remove_stopwords))
return sentences
sentences = []
for i, review in enumerate(train["review"]):
# print(i, review)
sentences += review_to_sentences(review, tokenizer, True)
```
```python
print(np.shape(train["review"]))
print(np.shape(sentences))
```
(25000,)
(267192,)
```python
unlabeled_train = pd.read_csv("%s/%s" % (root_dir, "unlabeledTrainData.tsv"), header=0, delimiter="\t", quoting=3 )
for review in unlabeled_train["review"]:
sentences += review_to_sentences(review, tokenizer)
print('预处理 unlabeled_train data...')
```
预处理 unlabeled_train data...
```python
print(np.shape(train_data))
print(np.shape(sentences))
```
(25000,)
(1035107,)
> 构建word2vec模型
```python
import time
from gensim.models import Word2Vec
# 模型参数
num_features = 300 # Word vector dimensionality
min_word_count = 40 # Minimum word count
num_workers = 4 # Number of threads to run in parallel
context = 10 # Context window size
downsampling = 1e-3 # Downsample setting for frequent words
```
```python
%%time
# 训练模型
print("训练模型中...")
model = Word2Vec(sentences, workers=num_workers, \
size=num_features, min_count=min_word_count, \
window=context, sample=downsampling)
print("训练完成")
```
训练模型中...
训练完成
CPU times: user 7min 12s, sys: 6.7 s, total: 7min 19s
Wall time: 2min 29s
```python
print('保存模型...')
model.init_sims(replace=True)
model_name = "%s/%s" % (root_dir, "300features_40minwords_10context")
model.save(model_name)
print('保存结束')
```
保存模型...
保存结束
> 预处理
```python
model.wv.doesnt_match("man woman child kitchen".split())
```
'kitchen'
```python
model.wv.doesnt_match("france england germany berlin".split())
```
'berlin'
```python
model.wv.doesnt_match("paris berlin london austria".split())
```
'paris'
```python
# help(model.wv.most_similar)
model.wv.most_similar("man", topn=5)
```
[('woman', 0.5622519850730896),
('lady', 0.5539723634719849),
('lad', 0.5375600457191467),
('men', 0.4897556006908417),
('monk', 0.48445409536361694)]
```python
model.wv.most_similar("queen", topn=5)
```
[('princess', 0.6005384922027588),
('bride', 0.5296590328216553),
('queens', 0.5233569145202637),
('eva', 0.5130444765090942),
('brunette', 0.505348265171051)]
```python
model.wv.most_similar("awful", topn=5)
```
[('terrible', 0.7551479935646057),
('abysmal', 0.7124387621879578),
('horrible', 0.7055309414863586),
('atrocious', 0.6951155066490173),
('horrendous', 0.6731454730033875)]
```python
model.wv.most_similar(positive=['woman', 'king'], negative=['man'], topn=1)
```
[('princess', 0.4474681615829468)]
> 使用Word2vec特征
```python
def makeFeatureVec(words, model, num_features):
'''
对段落中的所有词向量进行取平均操作
'''
featureVec = np.zeros((num_features,), dtype="float32")
nwords = 0.
# Index2word包含了词表中的所有词，为了检索速度，保存到set中
index2word_set = set(model.wv.index2word)
for word in words:
if word in index2word_set:
nwords = nwords + 1.
featureVec = np.add(featureVec, model[word])
# 取平均
featureVec = np.divide(featureVec, nwords)
return featureVec
def getAvgFeatureVecs(reviews, model, num_features):
'''
给定一个文本列表，每个文本由一个词列表组成，返回每个文本的词向量平均值
'''
counter = 0
reviewFeatureVecs = np.zeros((len(reviews), num_features), dtype="float32")
for review in reviews:
if counter % 5000 == 0:
print("Review %d of %d" % (counter, len(reviews)))
reviewFeatureVecs[counter] = makeFeatureVec(review, model, num_features)
counter = counter + 1
return reviewFeatureVecs
```
```python
%time trainDataVecs = getAvgFeatureVecs(train_data, model, num_features)
print(np.shape(trainDataVecs))
```
Review 0 of 25000
/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:13: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).
del sys.path[0]
Review 5000 of 25000
Review 10000 of 25000
Review 15000 of 25000
Review 20000 of 25000
CPU times: user 5min 27s, sys: 4.14 s, total: 5min 31s
Wall time: 5min 58s
(25000, 300)
```python
%time testDataVecs = getAvgFeatureVecs(test_data, model, num_features)
print(np.shape(testDataVecs))
```
Review 0 of 25000
/Users/jiangzl/.virtualenvs/python3.6/lib/python3.6/site-packages/ipykernel_launcher.py:13: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).
del sys.path[0]
Review 5000 of 25000
Review 10000 of 25000
Review 15000 of 25000
Review 20000 of 25000
CPU times: user 5min 10s, sys: 3.6 s, total: 5min 14s
Wall time: 5min 30s
(25000, 300)
### 高斯贝叶斯+Word2vec训练
```python
from sklearn.naive_bayes import GaussianNB as GNB
model_GNB = GNB()
model_GNB.fit(trainDataVecs, label)
from sklearn.cross_validation import cross_val_score
import numpy as np
print("高斯贝叶斯分类器10折交叉验证得分: ", np.mean(cross_val_score(model_GNB, trainDataVecs, label, cv=10, scoring='roc_auc')))
print('保存结果...')
result = model_GNB.predict( testDataVecs )
submission_df = pd.DataFrame(data ={'id': test['id'], 'sentiment': result})
print(submission_df.head(10))
submission_df.to_csv('/Users/jiangzl/Desktop/gnb_word2vec.csv',columns = ['id','sentiment'], index = False)
print('结束.')
"""
从验证结果来看，没有超过基于TF-IDF多项式贝叶斯模型
"""
```
高斯贝叶斯分类器10折交叉验证得分: 0.6163932159999999
保存结果...
id sentiment
0 "12311_10" 0
1 "8348_2" 0
2 "5828_4" 1
3 "7186_2" 0
4 "12128_7" 0
5 "2913_8" 0
6 "4396_1" 0
7 "395_2" 1
8 "10616_1" 1
9 "9074_9" 1
结束.
'\n从验证结果来看，没有超过基于TF-IDF多项式贝叶斯模型\n'
### 随机森林+Word2vec训练
```python
from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier( n_estimators = 100, n_jobs=2)
print("Fitting a random forest to labeled training data...")
%time forest = forest.fit( trainDataVecs, label )
print("随机森林分类器10折交叉验证得分: ", np.mean(cross_val_score(forest, trainDataVecs, label, cv=10, scoring='roc_auc')))
# 测试集
result = forest.predict( testDataVecs )
print('保存结果...')
submission_df = pd.DataFrame(data ={'id': test['id'], 'sentiment': result})
print(submission_df.head(10))
submission_df.to_csv('/Users/jiangzl/Desktop/rf_word2vec.csv',columns = ['id','sentiment'], index = False)
print('结束.')
"""
改用随机森林之后，效果有提升，但是依然没有超过基于TF-IDF多项式贝叶斯模型
"""
```
Fitting a random forest to labeled training data...
CPU times: user 43.8 s, sys: 347 ms, total: 44.2 s
Wall time: 23.1 s
随机森林分类器10折交叉验证得分: 0.6428176640000001
保存结果...
id sentiment
0 "12311_10" 1
1 "8348_2" 1
2 "5828_4" 0
3 "7186_2" 1
4 "12128_7" 0
5 "2913_8" 0
6 "4396_1" 0
7 "395_2" 0
8 "10616_1" 1
9 "9074_9" 1
结束.
'\n改用随机森林之后，效果有提升，但是依然没有超过基于TF-IDF多项式贝叶斯模型\n'
```python
# 加载训练好的词向量
from gensim.models.word2vec import Word2Vec
model = Word2Vec.load_word2vec_format("vector.txt", binary=False) # C text format
# model = Word2Vec.load_word2vec_format("vector.bin", binary=True) # C
```
```python
# 加载 google 的词向量，查看单词之间关系
from gensim.models.word2vec import Word2Vec
model = Word2Vec.load_word2vec_format("GoogleNews-vectors-negative300.bin", binary=True)
```
```python
# 测试预测效果
print(model.most_similar(positive=["woman", "king"], negative=["man"], topn=5))
print(model.most_similar(positive=["biggest", "small"], negative=["big"], topn=5))
print(model.most_similar(positive=["ate", "speak"], negative=["eat"], topn=5))
```
```python
import numpy as np
with open("food_words.txt", "r") as infile:
food_words = infile.readlines()
with open("sports_words.txt", "r") as infile:
food_words = infile.readlines()
with open("weather_words.txt", "r") as infile:
food_words = infile.readlines()
def getWordVecs(words):
vec = []
for word in words:
word = word.replace("\n", "")
try:
vecs.append(model[word].reshape((1, 300)))
except KeyError:
continue
# numpy提供了numpy.concatenate((a1,a2,...), axis=0)函数。能够一次完成多个数组的拼接
"""
>>> a=np.array([1,2,3])
>>> b=np.array([11,22,33])
>>> c=np.array([44,55,66])
>>> np.concatenate((a,b,c),axis=0) # 默认情况下，axis=0可以不写
array([ 1, 2, 3, 11, 22, 33, 44, 55, 66]) #对于一维数组拼接，axis的值不影响最后的结果
"""
vecs = np.concatenate(vecs)
return np.array(vecs, dtype="float")
food_vecs = getWordVecs(food_words)
sports_vecs = getWordVecs(sports_words)
weather_vecs = getWordVecs(weather_words)
```
```python
# 利用 TSNE 和 matplotlib 对分类结果进行可视化处理
from sklearn.manifold import TSEN
import matplotlib.pyplot as plt
ts = TSEN(2)
reduced_vecs = ts.fit_transform(np.concatenate((food_vecs, sports_vecs, weather_vecs)))
for i in range(len(reduced_vecs)):
if i < len(food_vecs):
color = "b"
elif i >= len(food_vecs) and i <(len(food_vecs)+len(sports_vecs)):
color = "r"
else:
color = "g"
plt.plot(reduced_vecs[i, 0], reduced_vecs[i, 1], marker="0", color=color, marksize=8)
```
```python
# 首先，我们导入数据并构建 Word2Vec 模型：
from sklearn.cross_validation import train_ _test_ _split
from gensim.models.word2vec import Word2Vec
with open('twitter.data/pos_ tweets.txt', 'r') as infile:
pos_tweets= infile.readlines()
with open(' twitter_ data/neg_ tweets.txt', 'r') as infile:
neg_ _tweets = infile.readlines()
# use 1for positive sentiment,0 for negative
Y= np.concatenate((np.ones( len (pos_tweets )) ，np.zeros(len(neg_tweets))))
x_train,x_test,y_train,y_test = train_test_split(np.concatenate((pos_tweets, neg_tweets)), y, test_size=0.2)
# Do some very minor text preprocessing
def cleanText(corpus):
corpus= [z.lower( ).replace(' \n' , '').split() for z in corpus]
return corpus
x_ train= cleanText(x_ train)
x_ test= cleanText (x_ _test)
n _dim= 300
#Initialize model and build vocab
imdb_w2v= Word2Vec(size=n dim, min_count=10)
imdb_w2v.build_vocab(x_ _train)
#Train the model over train_ _reviews (this may take several minutes)
imdb_w2v.train( x_train)
```
```python
# 接下来，为了利用下面的函数获得推文中所有词向量的平均值，我们必须构建作为输入文本的词向量。
def buildWordVector(text, size):
vec = np.zeros(size).reshape((1，size))
count= 0.
for word in text :
try:
vec += imdb_w2v[word].reshape( (1，size) )
count += 1.
except KeyError:
continue
if count != 0:
vec 1'= count
return vec
```
```python
# 调整数据集的量纲是数据标准化处理的一部分，我们通常将数据集转化成服从均值为零的高斯分布，这说明数值大于均值表示乐观，反之则表示悲观。为了使模型更有效，许多机器学习模型需要预先处理数据集的量纲，特别是文本分类器这类具有许多变量的模型。
from sklearn.preprocessing import scale
train_vecs = np.concatenate([buildWordVector(z ，n_dim) for z in x_train])
train_vecs= scale(train_vecs)
# Train word2vec on test tweets
imdb_w2v.train(x_test)
```
```python
# 最后我们需要建立测试集向量并对其标准化处理：
#Build test tweet vectors then scale
test_vecs = np.concatenate( [buildWordVector( Z，n _dim) for z in x _test ])
test_vecs = scale(test_vecs)
```
```python
"""
接下来我们想要通过计算测试集的预测精度和 ROC 曲线来验证分类器的有效性。 ROC 曲线衡量当模型参数调整的时候，其真阳性率和假阳性率的变化情况。在我们的案例中，我们调整的是分类器模型截断阈值的概率。一般来说，ROC 曲线下的面积（AUC）越大，该模型的表现越好。你可以在这里找到更多关于 ROC 曲线的资料
（https://en.wikipedia.org/wiki/Receiver_operating_characteristic）
在这个案例中我们使用罗吉斯回归的随机梯度下降法作为分类器算法。
"""
#Use classification algorithm (i.e.Stochastic Logistic Regression) on training set, then assess model performance on test set
from sklearn.linear model import SGDClassifier
lr = SGDClassifier(loss='log' ，penalty='11' )
lr.fit(train_vecs, y_train)
print' Test Accuracy: %.2f' % r.score(test vecs, y_test )
```
```python
# 随后我们利用 matplotlib 和 metric 库来构建 ROC 曲线
#Crea t e ROC curve
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
pred_probas = lr.predict_proba(test_vecs)[:, 1]
fpr, tpr, _ = roc_curve(y_test, pred_probas )
roc_auc = auc(fpr, tpr)
plt.plot(fpr,tpr,label='area = %.2f' % roc_ auc)
plt.plot([0,1]，[0，1],'k--')
plt. xlim( [0. 0 ，1. 0 ])
plt.ylim([0.0, 1.05])
plt.legend(loc='lower right')
plt.show()
```
================================================
FILE: docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/官方教程翻译/0.md
================================================
# 描述
在本教程竞赛中，我们对情感分析进行了一些“深入”研究。谷歌的 Word2Vec 是一种受深度学习启发的方法，专注于单词的含义。 Word2Vec 试图理解单词之间的意义和语义关系。它的工作方式类似于深度方法，例如循环神经网络或深度神经网络，但计算效率更高。本教程重点介绍用于情感分析的 Word2Vec。
情感分析是机器学习中的一个挑战性课题。人们用语言来表达自己的情感，这种语言经常被讽刺，二义性和文字游戏所掩盖，所有这些都会对人类和计算机产生误导。还有另一个 Kaggle 电影评论情绪分析竞赛。在本教程中，我们将探讨如何将 Word2Vec 应用于类似的问题。
在过去的几年里，深度学习在新闻中大量出现，甚至进入纽约时报的头版。这些机器学习技术受到人类大脑架构的启发，并且由于计算能力的最新进展而实现，由于图像识别，语音处理和自然语言任务的突破性结果，已经成为浪潮。最近，深度学习方法赢得了几项 Kaggle 比赛，包括药物发现任务和猫狗图像识别。
### 教程概览
本教程将帮助你开始使用 Word2Vec 进行自然语言处理。 它有两个目标：
基本自然语言处理：本教程的第 1 部分适用于初学者，涵盖了本教程后续部分所需的基本自然语言处理技术。
文本理解的深度学习：在第 2 部分和第 3 部分中，我们深入研究如何使用 Word2Vec 训练模型以及如何使用生成的单词向量进行情感分析。
由于深度学习是一个快速发展的领域，大量的工作尚未发表，或仅作为学术论文存在。 本教程的第 3 部分比说明性更具探索性 - 我们尝试了几种使用 Word2Vec 的方法，而不是为你提供使用输出的方法。
为了实现这些目标，我们依靠 IMDB 情绪分析数据集，其中包含 100,000 个多段电影评论，包括正面和负面。
### 致谢
此数据集是与以下出版物一起收集的：
> [Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). "Learning Word Vectors for Sentiment Analysis." The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).](http://ai.stanford.edu/~ang/papers/acl11-WordVectorsSentimentAnalysis.pdf)
如果你将数据用于任何研究应用，请发送电子邮件给该论文的作者。 该教程由 [Angela Chapman](http://www.linkedin.com/pub/angela-chapman/5/330/b97) 在 2014 年夏天在 Kaggle 实习期间开发。
### 什么是深度学习
术语“深度学习”是在2006年创造的，指的是具有多个非线性层并且可以学习特征层次结构的机器学习算法[1]。
大多数现代机器学习依赖于特征工程或某种级别的领域知识来获得良好的结果。 在深度学习系统中，情况并非如此 - 相反，算法可以自动学习特征层次结构，这些层次结构表示抽象级别增加的对象。 虽然许多深度学习算法的基本要素已存在多年，但由于计算能力的提高，计算硬件成本的下降以及机器学习研究的进步，它们目前正日益受到欢迎。
深度学习算法可以按其架构（前馈，反馈或双向）和训练协议（监督，混合或无监督）进行分类[2]。
一些好的背景材料包括：
[1] ["Deep Learning for Signal and Information Processing", by Li Deng and Dong Yu (out of Microsoft)](http://cs.tju.edu.cn/web/docs/2013-Deep%20Learning%20for%20Signal%20and%20Information%20Processing.pdf)
[2] ["Deep Learning Tutorial" (2013 Presentation by Yann LeCun and Marc'Aurelio Ranzato)](http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf)
### Word2Vec 适合哪里？
Word2Vec的工作方式类似于深度方法，如循环神经网络或深度神经网络，但它实现了某些算法，例如分层 softmax，使计算效率更高。
对于 Word2Vec 以及本文的更多信息，请参阅本教程的第 2 部分，以及这篇论文：[Efficient Estimation of Word Representations in Vector Space](http://arxiv.org/pdf/1301.3781v3.pdf)
在本教程中，我们使用混合方法进行训练 - 由无监督的片段（Word2Vec）和监督学习（随机森林）组成。
### 库和包
以下列表并不是详尽无遗的。
Python 中：
Theano 提供非常底层的基本功能，用于构建深度学习系统。 你还可以在他们的网站上找到一些很好的教程。
Caffe 是 Berkeley 视觉和学习中心的深度学习框架。
Pylearn2 包装了 Theano，似乎更加用户友好。
OverFeat 用于赢得 Kaggle 猫和狗的比赛。
Lua 中：
Torch 是一个受欢迎的包，并附带一个教程。
R 中：
截至 2014 年 8 月，有一些软件包刚刚开始开发，但没有可以在教程中使用的，非常成熟的包。
其他语言也可能有很好的包，但我们还没有对它们进行过研究。
### 更多教程
O'Reilly 博客有一系列深度学习文章和教程：
[什么是深度学习，为什么要关心？](http://radar.oreilly.com/2014/07/what-is-deep-learning-and-why-should-you-care.html)
[如何构建和运行你的第一个深度学习网络](http://radar.oreilly.com/2014/07/how-to-build-and-run-your-first-deep-learning-network.html)
[网络广播：如何起步深入学习计算机视觉](http://www.oreilly.com/pub/e/3121)
还有[几个使用 Theano 的教程](http://deeplearning.net/tutorial/)。
如果你想从零开始创建神经网络，请查看 Geoffrey Hinton 的 [Coursera 课程](https://www.coursera.org/course/neuralnets)。
对于 NLP，请查看斯坦福大学最近的这个讲座：[没有魔法的 NLP 的深度学习](http://techtalks.tv/talks/deep-learning-for-nlp-without-magic-part-1/58414/)。
这本免费的在线书籍还介绍了用于深度学习的神经网络：[神经网络和深度学习](http://neuralnetworksanddeeplearning.com/)。
### 配置你的系统
如果你之前没有安装过 Python 模块，请查看[此教程](http://programminghistorian.org/lessons/installing-python-modules-pip)，提供了从终端（在 Mac / Linux 中）或命令提示符（在 Windows 中）安装模块的指南。
运行本教程需要安装以下软件包。 在大多数（或所有）情况下，我们建议你使用[`pip`](https://pypi.python.org/pypi/pip)来安装软件包。
* [pandas](http://pandas.pydata.org/pandas-docs/stable/install.html)
* [numpy](http://docs.scipy.org/doc/numpy/user/install.html)
* scipy
* [scikit-learn ](http://scikit-learn.org/stable/install.html)
* [Beautiful Soup](http://www.crummy.com/software/BeautifulSoup/bs4/doc/)
* [NLTK](http://www.nltk.org/install.html)
* [Cython](http://docs.cython.org/src/quickstart/install.html)
* [gensim](http://radimrehurek.com/gensim/install.html)
Word2Vec 可以在`gensim`包中找到。 请注意，到目前为止，我们只在 Mac OS X 上成功运行了本教程，而不是 Windows。
如果你在 Mac Mavericks（10.9）上安装软件包时遇到问题，[本教程](http://hackercodex.com/guide/python-development-environment-on-mac-osx/)包含正确配置系统的说明。
本教程中的代码是为 Python 2.7 开发的。
================================================
FILE: docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/官方教程翻译/1.md
================================================
## 第一部分：写给入门者的词袋
### 什么是 NLP
NLP（自然语言处理）是一组用于处理文本问题的技术。这个页面将帮助你从加载和清理IMDB电影评论来起步，然后应用一个简单的[词袋](http://en.wikipedia.org/wiki/Bag-of-words_model)模型，来获得令人惊讶的准确预测，评论是点赞还是点踩。
### 在你开始之前
本教程使用 Python。如果你之前没有使用过 Python，我们建议你前往[泰坦尼克号竞赛 Python 教程](http://www.kaggle.com/c/titanic-gettingStarted)，熟悉一下（查看随机森林介绍）。
如果你已熟悉 Python 并使用基本的 NLP 技术，则可能需要跳到第 2 部分。
本教程的这一部分不依赖于平台。在本教程中，我们将使用各种 Python 模块进行文本处理，深度学习，随机森林和其他应用。详细信息请参阅“配置你的系统”页面。
有很多很好的教程，以及实际上用 Python 写的关于 NLP 和文本处理的[整本书](http://www.nltk.org/book/)。本教程绝不是详尽无遗的 - 只是为了帮助你以电影评论起步。
### 代码
第 1 部分的教程代码就在[这里](https://github.com/wendykan/DeepLearningMovies/blob/master/BagOfWords.py)。
### 读取数据
可以从“数据”页面下载必要的文件。你需要的第一个文件是`unlabeledTrainData`，其中包含 25,000 个 IMDB 电影评论，每个评论都带有正面或负面情感标签。
接下来，将制表符分隔文件读入 Python。为此，我们可以使用泰坦尼克号教程中介绍的`pandas`包，它提供了`read_csv`函数，用于轻松读取和写入数据文件。如果你之前没有使用过`pandas`，则可能需要安装它。
```py
# 导入 pandas 包，然后使用 "read_csv" 函数读取标记的训练数据
import pandas as pd
train = pd.read_csv("labeledTrainData.tsv", header=0, \
delimiter="\t", quoting=3)
```
这里，`header=0`表示文件的第一行包含列名，`delimiter=\t`表示字段由制表符分隔，`quoting=3`让 Python 忽略双引号，否则试图读取文件时，可能会遇到错误。
我们可以确保读取 25,000 行和 3 列，如下所示：
```py
>>> train.shape
(25000, 3)
>>> train.columns.values
array([id, sentiment, review], dtype=object)
```
这三列被称为`"id"`，`"sentiment"`和`"array"`。 现在你已经读取了培训集，请查看几条评论：
```py
print train["review"][0]
```
提醒一下，这将显示名为`"review"`的列中的第一个电影评论。 你应该看到一个像这样开头的评论：
```py
"With all this stuff going down at the moment with MJ i've started listening to his music, watching the odd documentary here and there, watched The Wiz and watched Moonwalker again. Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent. Moonwalker is part biography, part feature film which i remember going to see at the cinema when it was originally released. Some of it has subtle messages about MJ's feeling towards the press and also the obvious message of drugs are bad m'kay.
..."
```
有 HTML 标签，如`"
"`，缩写，标点符号 - 处理在线文本时的所有常见问题。 花一些时间来查看训练集中的其他评论 - 下一节将讨论如何为机器学习整理文本。
### 数据清理和文本预处理
删除 HTML 标记：`BeautifulSoup`包
首先，我们将删除 HTML 标记。 为此，我们将使用[`BeautifulSoup`库](http://www.crummy.com/software/BeautifulSoup/bs4/doc/)。 如果你没有安装，请从命令行（不是从 Python 内部）执行以下操作：
```
$ sudo pip install BeautifulSoup4
```
然后，从 Python 中加载包并使用它从评论中提取文本：
```py
# Import BeautifulSoup into your workspace
from bs4 import BeautifulSoup
# Initialize the BeautifulSoup object on a single movie review
example1 = BeautifulSoup(train["review"][0])
# Print the raw review and then the output of get_text(), for
# comparison
print train["review"][0]
print example1.get_text()
```
调用`get_text()`会为你提供不带标签的评论文本。如果你浏览`BeautifulSoup`文档，你会发现它是一个非常强大的库 - 比我们对此数据集所需的功能更强大。但是，使用正则表达式删除标记并不是一种可靠的做法，因此即使对于像这样简单的应用程序，通常最好使用像`BeautifulSoup`这样的包。
处理标点符号，数字和停止词：NLTK 和正则表达式
在考虑如何清理文本时，我们应该考虑我们试图解决的数据问题。对于许多问题，删除标点符号是有意义的。另一方面，在这种情况下，我们正在解决情感分析问题，并且有可能`"!!!"`或者`":-("`可以带有情感，应该被视为单词。在本教程中，为简单起见，我们完全删除了标点符号，但这是你可以自己玩的东西。
与之相似，在本教程中我们将删除数字，但还有其他方法可以处理它们，这些方法同样有意义。例如，我们可以将它们视为单词，或者使用占位符字符串（例如`"NUM"`）替换它们。
要删除标点符号和数字，我们将使用一个包来处理正则表达式，称为`re`。Python 内置了该软件包；无需安装任何东西。对于正则表达式如何工作的详细说明，请参阅[包文档](https://docs.python.org/2/library/re.html#)。现在，尝试以下方法：
```py
import re
# 使用正则表达式执行查找和替换
letters_only = re.sub("[^a-zA-Z]", # 要查找的模式串
" ", # 要替换成的模式串
example1.get_text() ) # 要从中查找的字符串
print letters_only
```
正则表达式的完整概述超出了本教程的范围，但是现在知道`[]`表示分组成员而`^`表示“不”就足够了。 换句话说，上面的`re.sub()`语句说：“查找任何不是小写字母（`a-z`）或大写字母（`A-Z`）的内容，并用空格替换它。”
我们还将我们的评论转换为小写并将它们分成单个单词（在 NLP 术语中称为“[分词](http://en.wikipedia.org/wiki/Tokenization)”）：
```py
lower_case = letters_only.lower() # 转换为小写
words = lower_case.split() # 分割为单词
```
最后，我们需要决定如何处理那些没有多大意义的经常出现的单词。 这样的词被称为“停止词”；在英语中，它们包括诸如“a”，“and”，“is”和“the”之类的单词。方便的是，Python 包中内置了停止词列表。让我们从 Python 自然语言工具包（NLTK）导入停止词列表。 如果你的计算机上还没有该库，则需要安装该库；你还需要安装附带的数据包，如下所示：
```py
import nltk
nltk.download() # 下载文本数据集，包含停止词
```
现在我们可以使用`nltk`来获取停止词列表：
```py
from nltk.corpus import stopwords # 导入停止词列表
print stopwords.words("english")
```
这将允许你查看英语停止词列表。 要从我们的电影评论中删除停止词，请执行：
```py
# 从 "words" 中移除停止词
words = [w for w in words if not w in stopwords.words("english")]
print words
```
这会查看`words`列表中的每个单词，并丢弃在停止词列表中找到的任何内容。 完成所有这些步骤后，你的评论现在应该是这样的：
```py
[u'stuff', u'going', u'moment', u'mj', u've', u'started', u'listening', u'music', u'watching', u'odd', u'documentary', u'watched', u'wiz', u'watched', u'moonwalker', u'maybe', u'want', u'get', u'certain', u'insight', u'guy', u'thought', u'really', u'cool', u'eighties', u'maybe', u'make', u'mind', u'whether', u'guilty', u'innocent', u'moonwalker', u'part', u'biography', u'part', u'feature', u'film', u'remember', u'going', u'see', u'cinema', u'originally', u'released', u'subtle', u'messages', u'mj', u'feeling', u'towards', u'press', u'also', u'obvious', u'message', u'drugs', u'bad', u'm', u'kay',.....]
```
不要担心在每个单词之前的`u`；它只是表明 Python 在内部将每个单词表示为 [unicode 字符串](https://docs.python.org/2/howto/unicode.html#python-2-x-s-unicode-support)。
我们可以对数据做很多其他的事情 - 例如，Porter Stemming（词干提取）和 Lemmatizing（词形还原）（都在 NLTK 中提供）将允许我们将`"messages"`，`"message"`和`"messaging"`视为同一个词，这当然可能很有用。 但是，为简单起见，本教程将就此打住。
### 把它们放在一起
现在我们有了清理评论的代码 - 但我们需要清理 25,000 个训练评论！ 为了使我们的代码可重用，让我们创建一个可以多次调用的函数：
```py
def review_to_words( raw_review ):
# 将原始评论转换为单词字符串的函数
# 输入是单个字符串（原始电影评论），
# 输出是单个字符串（预处理过的电影评论）
# 1. 移除 HTML
review_text = BeautifulSoup(raw_review).get_text()
#
# 2. 移除非字母
letters_only = re.sub("[^a-zA-Z]", " ", review_text)
#
# 3. 转换为小写，分成单个单词
words = letters_only.lower().split()
#
# 4. 在Python中，搜索集合比搜索列表快得多，
# 所以将停止词转换为一个集合
stops = set(stopwords.words("english"))
#
# 5. 删除停止词
meaningful_words = [w for w in words if not w in stops]
#
# 6. 将单词连接成由空格分隔的字符串，
# 并返回结果。
return( " ".join( meaningful_words ))
```
这里有两个新元素：首先，我们将停止词列表转换为不同的数据类型，即集合。 这是为了速度；因为我们将调用这个函数数万次，所以它需要很快，而 Python 中的搜索集合比搜索列表要快得多。
其次，我们将这些单词合并为一段。 这是为了使输出更容易在我们的词袋中使用，在下面。 定义上述函数后，如果你为单个评论调用该函数：
```py
clean_review = review_to_words( train["review"][0] )
print clean_review
```
它应该为你提供与前面教程部分中所做的所有单独步骤完全相同的输出。 现在让我们遍历并立即清理所有训练集（这可能需要几分钟，具体取决于你的计算机）：
```py
# 根据 dataframe 列大小获取评论数
num_reviews = train["review"].size
# 初始化空列表来保存清理后的评论
clean_train_reviews = []
# 遍历每个评论；创建索引 i
# 范围是 0 到电影评论列表长度
for i in xrange( 0, num_reviews ):
# 为每个评论调用我们的函数，
# 并将结果添加到清理后评论列表中
clean_train_reviews.append( review_to_words( train["review"][i] ) )
```
有时等待冗长的代码的运行会很烦人。 编写提供状态更新的代码会很有帮助。 要让 Python 在其处理每 1000 个评论后打印状态更新，请尝试在上面的代码中添加一两行：
```py
print "Cleaning and parsing the training set movie reviews...\n"
clean_train_reviews = []
for i in xrange( 0, num_reviews ):
# 如果索引被 1000 整除，打印消息
if( (i+1)%1000 == 0 ):
print "Review %d of %d\n" % ( i+1, num_reviews )
clean_train_reviews.append( review_to_words( train["review"][i] ))
```
### 从词袋创建特征（使用`sklearn`）
现在我们已经整理了我们的训练评论，我们如何将它们转换为机器学习的某种数字表示？一种常见的方法叫做词袋。词袋模型从所有文档中学习词汇表，然后通过计算每个单词出现的次数对每个文档进行建模。例如，考虑以下两句话：
句子1：`"The cat sat on the hat"`
句子2：`"The dog ate the cat and the hat"`
从这两个句子中，我们的词汇如下：
`{ the, cat, sat, on, hat, dog, ate, and }`
为了得到我们的词袋，我们计算每个单词出现在每个句子中的次数。在句子 1 中，“the”出现两次，“cat”，“sat”，“on”和“hat”每次出现一次，因此句子 1 的特征向量是：
`{ the, cat, sat, on, hat, dog, ate, and }`
句子 1：`{ 2, 1, 1, 1, 1, 0, 0, 0 }`
同样，句子 2 的特征是：`{ 3, 1, 0, 0, 1, 1, 1, 1}`
在 IMDB 数据中，我们有大量的评论，这将为我们提供大量的词汇。要限制特征向量的大小，我们应该选择最大词汇量。下面，我们使用 5000 个最常用的单词（记住已经删除了停止词）。
我们将使用 scikit-learn 中的`feature_extraction`模块来创建词袋特征。如果你学习了泰坦尼克号竞赛中的随机森林教程，那么你应该已经安装了 scikit-learn；否则你需要[安装它](http://scikit-learn.org/stable/install.html)。
```py
print "Creating the bag of words...\n"
from sklearn.feature_extraction.text import CountVectorizer
# 初始化 "CountVectorizer" 对象，
# 这是 scikit-learn 的一个词袋工具。
vectorizer = CountVectorizer(analyzer = "word", \
tokenizer = None, \
preprocessor = None, \
stop_words = None, \
max_features = 5000)
# fit_transform() 有两个功能：
# 首先，它拟合模型并学习词汇；
# 第二，它将我们的训练数据转换为特征向量。
# fit_transform 的输入应该是字符串列表。
train_data_features = vectorizer.fit_transform(clean_train_reviews)
# Numpy 数组很容易使用，因此将结果转换为数组
train_data_features = train_data_features.toarray()
```
要查看训练数据数组现在的样子，请执行以下操作：
```py
>>> print train_data_features.shape
(25000, 5000)
```
它有 25,000 行和 5,000 个特征（每个词汇一个）。
请注意，`CountVectorizer`有自己的选项来自动执行预处理，标记化和停止词删除 - 对于其中的每一个，我们不指定`None`，可以使用内置方法或指定我们自己的函数来使用。 详细信息请参阅[函数文档](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)。 但是，我们想在本教程中编写我们自己的数据清理函数，来向你展示如何逐步完成它。
现在词袋模型已经训练好了，让我们来看看词汇表：
```py
# 看看词汇表中的单词
vocab = vectorizer.get_feature_names()
print vocab
```
如果你有兴趣，还可以打印词汇表中每个单词的计数：
```py
import numpy as np
# 求和词汇表中每个单词的计数
dist = np.sum(train_data_features, axis=0)
# 对于每个词，打印它和它在训练集中的出现次数
for tag, count in zip(vocab, dist):
print count, tag
```
### 随机森林
到了这里，我们有词袋的数字训练特征和每个特征向量的原始情感标签，所以让我们做一些监督学习！ 在这里，我们将使用我们在泰坦尼克号教程中介绍的随机森林分类器。 随机森林算法包含在 scikit-learn 中（[随机森林](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)使用许多基于树的分类器来进行预测，因此是“森林”）。 下面，我们将树的数量设置为 100 作为合理的默认值。 更多树可能（或可能不）表现更好，但肯定需要更长时间来运行。 同样，每个评论所包含的特征越多，所需的时间就越长。
```py
print "Training the random forest..."
from sklearn.ensemble import RandomForestClassifier
# 使用 100 棵树初始化随机森林分类器
forest = RandomForestClassifier(n_estimators = 100)
# 使用词袋作为特征并将情感标签作为响应变量，使森林拟合训练集
# 这可能需要几分钟来运行
forest = forest.fit( train_data_features, train["sentiment"] )
```
### 创建提交
剩下的就是在我们的测试集上运行训练好的随机森林并创建一个提交文件。 如果你还没有这样做，请从“数据”页面下载`testData.tsv`。 此文件包含另外 25,000 条评论和标签；我们的任务是预测情感标签。
请注意，当我们使用词袋作为测试集时，我们只调用`transform`，而不是像训练集那样调用`fit_transform`。 在机器学习中，你不应该使用测试集来拟合你的模型，否则你将面临[过拟合](http://blog.kaggle.com/2012/07/06/the-dangers-of-overfitting-psychopathy-post-mortem/)的风险。 出于这个原因，我们将测试集保持在禁止状态，直到我们准备好进行预测。
```py
# 读取测试数据
test = pd.read_csv("testData.tsv", header=0, delimiter="\t", \
quoting=3 )
# 验证有 25,000 行和 2 列
print test.shape
# 创建一个空列表并逐个附加干净的评论
num_reviews = len(test["review"])
clean_test_reviews = []
print "Cleaning and parsing the test set movie reviews...\n"
for i in xrange(0,num_reviews):
if( (i+1) % 1000 == 0 ):
print "Review %d of %d\n" % (i+1, num_reviews)
clean_review = review_to_words( test["review"][i] )
clean_test_reviews.append( clean_review )
# 获取测试集的词袋，并转换为 numpy 数组
test_data_features = vectorizer.transform(clean_test_reviews)
test_data_features = test_data_features.toarray()
# 使用随机森林进行情感标签预测
result = forest.predict(test_data_features)
# 将结果复制到带有 "id" 列和 "sentiment" 列的 pandas dataframe
output = pd.DataFrame( data={"id":test["id"], "sentiment":result} )
# 使用 pandas 编写逗号分隔的输出文件
output.to_csv( "Bag_of_Words_model.csv", index=False, quoting=3 )
```
恭喜，你已准备好第一次提交！ 尝试不同的事情，看看你的结果如何变化。 你可以以不同方式清理评论，为词袋表示选择不同数量的词汇表单词，尝试 Porter Stemming，不同的分类器或任何其他的东西。 要在不同的数据集上试用你的 NLP 招式，你还可以参加我们的[烂番茄比赛](https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews)。 或者，如果你为完全不同的东西做好了准备，请访问深度学习和词向量页面。
================================================
FILE: docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/官方教程翻译/2.md
================================================
## 第二部分：词向量
### 代码
第二部分的教程代码[在这里](https://github.com/wendykan/DeepLearningMovies/blob/master/Word2Vec_AverageVectors.py)。
### 分布式词向量简介
本教程的这一部分将重点介绍使用 Word2Vec 算法创建分布式单词向量。 （深度学习的概述，以及其他一些教程的链接，请参阅“什么是深度学习？”页面）。
第 2 部分和第 3 部分比第 1 部分假设你更熟悉Python。我们在双核 Macbook Pro 上开发了以下代码，但是，我们还没有在 Windows 上成功运行代码。如果你是 Windows 用户并且使其正常运行，请在论坛中留言如何进行操作！更多详细信息，请参阅“配置系统”页面。
[Word2vec](https://code.google.com/p/word2vec/)，由 Google 于 2013 年发表，是一种神经网络实现，可以学习单词的[分布式表示](http://www.cs.toronto.edu/~bonner/courses/2014s/csc321/lectures/lec5.pdf)。在此之前已经提出了用于学习单词表示的其他深度或循环神经网络架构，但是这些的主要问题是训练模型所需时长间。 Word2vec 相对于其他模型学习得快。
Word2Vec 不需要标签来创建有意义的表示。这很有用，因为现实世界中的大多数数据都是未标记的。如果给网络足够的训练数据（数百亿个单词），它会产生特征极好的单词向量。具有相似含义的词出现在簇中，并且簇具有间隔，使得可以使用向量数学来再现诸如类比的一些词关系。着名的例子是，通过训练好的单词向量，“国王 - 男人 + 女人 = 女王”。
查看 [Google 的代码，文章和附带的论文](https://code.google.com/p/word2vec/)。 [此演示](https://docs.google.com/file/d/0B7XkCwpI5KDYRWRnd1RzWXQ2TWc/edit)也很有帮助。 原始代码是 C 写的，但它已被移植到其他语言，包括 Python。 我们鼓励你使用原始 C 工具，但如果你是初学程序员（我们必须手动编辑头文件来编译），请注意它不是用户友好的。
最近斯坦福大学的工作也将[深度学习应用于情感分析](http://nlp.stanford.edu/sentiment/)；他们的代码以 Java 提供。 但是，他们的方法依赖于句子解析，不能直接应用于任意长度的段落。
分布式词向量强大，可用于许多应用，尤其是单词预测和转换。 在这里，我们将尝试将它们应用于情感分析。
### 在 Python 中使用 word2vec
在 Python 中，我们将使用`gensim`包中的 word2vec 的优秀实现。 如果你还没有安装`gensim`，则需要[安装](http://radimrehurek.com/gensim/install.html)它。 [这里](http://radimrehurek.com/2014/02/word2vec-tutorial/)有一个包含 Python Word2Vec 实现的优秀教程。
虽然 Word2Vec 不像许多深度学习算法那样需要图形处理单元（GPU），但它是计算密集型的。 Google 的版本和 Python 版本都依赖于多线程（在你的计算机上并行运行多个进程以节省时间）。 为了在合理的时间内训练你的模型，你需要安装 cython（[这里是指南](http://docs.cython.org/src/quickstart/install.html)）。 Word2Vec 可在没有安装 cython 的情况下运行，但运行它需要几天而不是几分钟。
### 为训练模型做准备
现在到了细节！ 首先，我们使用`pandas`读取数据，就像我们在第 1 部分中所做的那样。与第 1 部分不同，我们现在使用`unlabeledTrain.tsv`，其中包含 50,000 个额外的评论，没有标签。 当我们在第 1 部分中构建词袋模型时，额外的未标记的训练评论没有用。 但是，由于 Word2Vec 可以从未标记的数据中学习，现在可以使用这些额外的 50,000 条评论。
```py
import pandas as pd
# 从文件读取数据
train = pd.read_csv( "labeledTrainData.tsv", header=0,
delimiter="\t", quoting=3 )
test = pd.read_csv( "testData.tsv", header=0, delimiter="\t", quoting=3 )
unlabeled_train = pd.read_csv( "unlabeledTrainData.tsv", header=0,
delimiter="\t", quoting=3 )
# 验证已读取的评论数量（总共 100,000 个）
print "Read %d labeled train reviews, %d labeled test reviews, " \
"and %d unlabeled reviews\n" % (train["review"].size,
test["review"].size, unlabeled_train["review"].size )
```
我们为清理数据而编写的函数也与第 1 部分类似，尽管现在存在一些差异。 首先，为了训练 Word2Vec，最好不要删除停止词，因为算法依赖于句子的更广泛的上下文，以便产生高质量的词向量。 因此，我们将在下面的函数中，将停止词删除变成可选的。 最好不要删除数字，但我们将其留作读者的练习。
```py
# Import various modules for string cleaning
from bs4 import BeautifulSoup
import re
from nltk.corpus import stopwords
def review_to_wordlist( review, remove_stopwords=False ):
# 将文档转换为单词序列的函数，可选地删除停止词。 返回单词列表。
#
# 1. 移除 HTML
review_text = BeautifulSoup(review).get_text()
#
# 2. 移除非字母
review_text = re.sub("[^a-zA-Z]"," ", review_text)
#
# 3. 将单词转换为小写并将其拆分
words = review_text.lower().split()
#
# 4. 可选地删除停止词（默认为 false）
if remove_stopwords:
stops = set(stopwords.words("english"))
words = [w for w in words if not w in stops]
#
# 5. 返回单词列表
return(words)
```
接下来，我们需要一种特定的输入格式。 Word2Vec 需要单个句子，每个句子都是一列单词。 换句话说，输入格式是列表的列表。
如何将一个段落分成句子并不简单。 自然语言中有各种各样的问题。 英语句子可能以“?”，“!”，“"”或“.”等结尾，并且间距和大写也不是可靠的标志。因此，我们将使用 NLTK 的`punkt`分词器进行句子分割。为了使用它，你需要安装 NLTK 并使用`nltk.download()`下载`punkt`的相关训练文件。
```py
# 为句子拆分下载 punkt 分词器
import nltk.data
nltk.download()
# 加载 punkt 分词器
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
# 定义一个函数将评论拆分为已解析的句子
def review_to_sentences( review, tokenizer, remove_stopwords=False ):
# 将评论拆分为已解析句子的函数。
# 返回句子列表，其中每个句子都是单词列表
# 1. 使用 NLTK 分词器将段落拆分为句子
raw_sentences = tokenizer.tokenize(review.strip())
#
# 2. 遍历每个句子
sentences = []
for raw_sentence in raw_sentences:
# 如果句子为空，则跳过
if len(raw_sentence) > 0:
# 否则，调用 review_to_wordlist 来获取单词列表
sentences.append( review_to_wordlist( raw_sentence, \
remove_stopwords ))
# 返回句子列表（每个句子都是单词列表，
# 因此返回列表的列表）
return sentences
```
现在我们可以应用此函数，来准备 Word2Vec 的输入数据（这将需要几分钟）：
```py
sentences = [] # 初始化空的句子列表
print "Parsing sentences from training set"
for review in train["review"]:
sentences += review_to_sentences(review, tokenizer)
print "Parsing sentences from unlabeled set"
for review in unlabeled_train["review"]:
sentences += review_to_sentences(review, tokenizer)
```
你可能会从`BeautifulSoup`那里得到一些关于句子中 URL 的警告。 这些都不用担心（尽管你可能需要考虑在清理文本时删除 URL）。
我们可以看一下输出，看看它与第 1 部分的不同之处：
```py
>>> # 检查我们总共有多少句子 - 应该是 850,000+ 左右
... print len(sentences)
857234
>>> print sentences[0]
[u'with', u'all', u'this', u'stuff', u'going', u'down', u'at', u'the', u'moment', u'with', u'mj', u'i', u've', u'started', u'listening', u'to', u'his', u'music', u'watching', u'the', u'odd', u'documentary', u'here', u'and', u'there', u'watched', u'the', u'wiz', u'and', u'watched', u'moonwalker', u'again']
>>> print sentences[1]
[u'maybe', u'i', u'just', u'want', u'to', u'get', u'a', u'certain', u'insight', u'into', u'this', u'guy', u'who', u'i', u'thought', u'was', u'really', u'cool', u'in', u'the', u'eighties', u'just', u'to', u'maybe', u'make', u'up', u'my', u'mind', u'whether', u'he', u'is', u'guilty', u'or', u'innocent']
```
需要注意的一个小细节是 Python 列表中`+=`和`append`之间的区别。 在许多应用中，这两者是可以互换的，但在这里它们不是。 如果要将列表列表附加到另一个列表列表，`append`仅仅附加外层列表; 你需要使用`+=`才能连接所有内层列表。
> 译者注：原文中这里的解释有误，已修改。
### 训练并保存你的模型
使用精心解析的句子列表，我们已准备好训练模型。 有许多参数选项会影响运行时间和生成的最终模型的质量。 以下算法的详细信息，请参阅 [word2vec API 文档](http://radimrehurek.com/gensim/models/word2vec.html)以及 [Google 文档](https://code.google.com/p/word2vec/)。
+ 架构：架构选项是 skip-gram（默认）或 CBOW。 我们发现 skip-gram 非常慢，但产生了更好的结果。
+ 训练算法：分层 softmax（默认）或负采样。 对我们来说，默认效果很好。
+ 对频繁词汇进行下采样：Google 文档建议值介于`.00001`和`.001`之间。 对我们来说，接近0.001的值似乎可以提高最终模型的准确性。
+ 单词向量维度：更多特征会产生更长的运行时间，并且通常（但并非总是）会产生更好的模型。 合理的值可能介于几十到几百；我们用了 300。
+ 上下文/窗口大小：训练算法应考虑多少个上下文单词？ 10 似乎适用于分层 softmax（越多越好，达到一定程度）。
+ 工作线程：要运行的并行进程数。 这是特定于计算机的，但 4 到 6 之间应该适用于大多数系统。
+ 最小词数：这有助于将词汇量的大小限制为有意义的单词。 在所有文档中，至少没有出现这个次数的任何单词都将被忽略。 合理的值可以在 10 到 100 之间。在这种情况下，由于每个电影出现 30 次，我们将最小字数设置为 40，来避免过分重视单个电影标题。 这导致了整体词汇量大约为 15,000 个单词。 较高的值也有助于限制运行时间。
选择参数并不容易，但是一旦我们选择了参数，创建 Word2Vec 模型就很简单：
```py
# 导入内置日志记录模块并配置它，以便 Word2Vec 创建良好的输出消息
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',\
level=logging.INFO)
# 设置各种参数的值
num_features = 300 # 词向量维度
min_word_count = 40 # 最小单词数
num_workers = 4 # 并行运行的线程数
context = 10 # 上下文窗口大小
downsampling = 1e-3 # 为频繁词设置下采样
# 初始化并训练模型（这需要一些时间）
from gensim.models import word2vec
print "Training model..."
model = word2vec.Word2Vec(sentences, workers=num_workers, \
size=num_features, min_count = min_word_count, \
window = context, sample = downsampling)
# 如果你不打算再进一步训练模型，
# 则调用 init_sims 将使模型更具内存效率。
model.init_sims(replace=True)
# 创建有意义的模型名称并保存模型以供以后使用会很有帮助。
# 你可以稍后使用 Word2Vec.load() 加载它
model_name = "300features_40minwords_10context"
model.save(model_name)
```
在双核 Macbook Pro 上，使用 4 个工作线程来运行，花费不到 15 分钟。 但是，它会因你的计算机而异。 幸运的是，日志记录功能可以打印带有信息的消息。
如果你使用的是 Mac 或 Linux 系统，则可以使用终端内（而不是来自 Python 内部）的`top`命令，来查看你的系统是否在模型训练时成功并行化。 键入：
```
> top -o cpu
```
在模型训练时进入终端窗口。 对于 4 个 worker，列表中的第一个进程应该是 Python，它应该显示 300-400% 的 CPU 使用率。
如果你的 CPU 使用率较低，则可能是你的计算机上的 cython 无法正常运行。
### 探索模型结果
恭喜你到目前为止成功通过了一切！ 让我们来看看我们在 75,000 个训练评论中创建的模型。
`doesnt_match`函数将尝试推断集合中哪个单词与其他单词最不相似：
```py
>>> model.doesnt_match("man woman child kitchen".split())
'kitchen'
```
我们的模型能够区分意义上的差异！ 它知道男人，女人和孩子彼此更相似，而不是厨房。 更多的探索表明，该模型对意义上更微妙的差异敏感，例如国家和城市之间的差异：
```py
>>> model.doesnt_match("france england germany berlin".split())
'berlin'
```
...虽然我们使用的训练集相对较小，但肯定不完美：
```py
>>> model.doesnt_match("paris berlin london austria".split())
'paris'
```
我们还可以使用`most_similar`函数来深入了解模型的单词簇：
```py
>>> model.most_similar("man")
[(u'woman', 0.6056041121482849), (u'guy', 0.4935004413127899), (u'boy', 0.48933547735214233), (u'men', 0.4632953703403473), (u'person', 0.45742249488830566), (u'lady', 0.4487500488758087), (u'himself', 0.4288588762283325), (u'girl', 0.4166809320449829), (u'his', 0.3853422999382019), (u'he', 0.38293731212615967)]
>>> model.most_similar("queen")
[(u'princess', 0.519856333732605), (u'latifah', 0.47644317150115967), (u'prince', 0.45914226770401), (u'king', 0.4466976821422577), (u'elizabeth', 0.4134873151779175), (u'antoinette', 0.41033703088760376), (u'marie', 0.4061327874660492), (u'stepmother', 0.4040161967277527), (u'belle', 0.38827288150787354), (u'lovely', 0.38668593764305115)]
```
鉴于我们特定的训练集，“Latifah”与“女王”的相似性最高，也就不足为奇了。
或者，与情感分析更相关：
```py
>>> model.most_similar("awful")
[(u'terrible', 0.6812670230865479), (u'horrible', 0.62867271900177), (u'dreadful', 0.5879652500152588), (u'laughable', 0.5469599962234497), (u'horrendous', 0.5167273283004761), (u'atrocious', 0.5115568041801453), (u'ridiculous', 0.5104714632034302), (u'abysmal', 0.5015234351158142), (u'pathetic', 0.4880446791648865), (u'embarrassing', 0.48272213339805603)]
```
因此，似乎我们有相当好的语义意义模型 - 至少和词袋一样好。 但是，我们如何才能将这些花哨的分布式单词向量用于监督学习呢？ 下一节将对此进行一次尝试。
================================================
FILE: docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/官方教程翻译/3.md
================================================
## 第三部分：词向量的更多乐趣
### 代码
第三部分的代码在[这里](https://github.com/wendykan/DeepLearningMovies/blob/master/Word2Vec_BagOfCentroids.py)。
### 单词的数值表示
现在我们有了训练好的模型，对单词有一些语义理解，我们应该如何使用它？ 如果你看它的背后，第 2 部分训练的 Word2Vec 模型由词汇表中每个单词的特征向量组成，存储在一个名为`syn0`的[`numpy`](http://www.numpy.org/)数组中：
```py
>>> # Load the model that we created in Part 2
>>> from gensim.models import Word2Vec
>>> model = Word2Vec.load("300features_40minwords_10context")
2014-08-03 14:50:15,126 : INFO : loading Word2Vec object from 300features_40min_word_count_10context
2014-08-03 14:50:15,777 : INFO : setting ignored attribute syn0norm to None
>>> type(model.syn0)
>>> model.syn0.shape
(16492, 300)
```
`syn0`中的行数是模型词汇表中的单词数，列数对应于我们在第 2 部分中设置的特征向量的大小。将最小单词计数设置为 40 ，总词汇量为 16,492 个单词，每个词有 300 个特征。 可以通过以下方式访问单个单词向量：
```py
>>> model["flower"]
```
...返回一个 1x300 的`numpy`数组。
### 从单词到段落，尝试 1：向量平均
IMDB 数据集的一个挑战是可变长度评论。 我们需要找到一种方法来获取单个单词向量并将它们转换为每个评论的长度相同的特征集。
由于每个单词都是 300 维空间中的向量，我们可以使用向量运算来组合每个评论中的单词。 我们尝试的一种方法是简单地平均给定的评论中的单词向量（为此，我们删除了停止词，这只会增加噪音）。
以下代码基于第 2 部分的代码构建了特征向量的平均值。
```py
import numpy as np # Make sure that numpy is imported
def makeFeatureVec(words, model, num_features):
# 用于平均给定段落中的所有单词向量的函数
#
# 预初始化一个空的 numpy 数组（为了速度）
featureVec = np.zeros((num_features,),dtype="float32")
#
nwords = 0.
#
# Index2word 是一个列表，包含模型词汇表中的单词名称。
# 为了获得速度，将其转换为集合。
index2word_set = set(model.index2word)
#
# 遍历评论中的每个单词，如果它在模型的词汇表中，
# 则将其特征向量加到 total
for word in words:
if word in index2word_set:
nwords = nwords + 1.
featureVec = np.add(featureVec,model[word])
#
# 将结果除以单词数来获得平均值
featureVec = np.divide(featureVec,nwords)
return featureVec
def getAvgFeatureVecs(reviews, model, num_features):
# 给定一组评论（每个评论都是单词列表），计算每个评论的平均特征向量并返回2D numpy数组
#
# 初始化计数器
counter = 0.
#
# 为了速度，预分配 2D numpy 数组
reviewFeatureVecs = np.zeros((len(reviews),num_features),dtype="float32")
#
# 遍历评论
for review in reviews:
#
# 每 1000 个评论打印一次状态消息
if counter%1000. == 0.:
print "Review %d of %d" % (counter, len(reviews))
#
# 调用生成平均特征向量的函数（定义如上）
reviewFeatureVecs[counter] = makeFeatureVec(review, model, \
num_features)
#
# 增加计数器
counter = counter + 1.
return reviewFeatureVecs
```
现在，我们可以调用这些函数来为每个段落创建平均向量。 以下操作将需要几分钟：
```py
# ****************************************************************
# 使用我们在上面定义的函数，
# 计算训练和测试集的平均特征向量。
# 请注意，我们现在删除停止词。
clean_train_reviews = []
for review in train["review"]:
clean_train_reviews.append( review_to_wordlist( review, \
remove_stopwords=True ))
trainDataVecs = getAvgFeatureVecs( clean_train_reviews, model, num_features )
print "Creating average feature vecs for test reviews"
clean_test_reviews = []
for review in test["review"]:
clean_test_reviews.append( review_to_wordlist( review, \
remove_stopwords=True ))
testDataVecs = getAvgFeatureVecs( clean_test_reviews, model, num_features )
```
接下来，使用平均段落向量来训练随机森林。 请注意，与第 1 部分一样，我们只能使用标记的训练评论来训练模型。
```py
# 使用 100 棵树让随机森林拟合训练数据
from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier( n_estimators = 100 )
print "Fitting a random forest to labeled training data..."
forest = forest.fit( trainDataVecs, train["sentiment"] )
# 测试和提取结果
result = forest.predict( testDataVecs )
# 写出测试结果
output = pd.DataFrame( data={"id":test["id"], "sentiment":result} )
output.to_csv( "Word2Vec_AverageVectors.csv", index=False, quoting=3 )
```
我们发现这产生了比偶然更好的结果，但是表现比词袋低了几个百分点。
由于向量的元素平均值没有产生惊人的结果，或许我们可以以更聪明的方式实现？ 加权单词向量的标准方法是应用[“tf-idf”](http://en.wikipedia.org/wiki/Tf%E2%80%93idf)权重，它衡量给定单词在给定文档集中的重要程度。 在 Python 中提取 tf-idf 权重的一种方法，是使用 scikit-learn 的[`TfidfVectorizer`](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html)，它具有类似于我们在第 1 部分中使用的`CountVectorizer`的接口。但是，当我们尝试以这种方式加权我们的单词向量时，我们发现没有实质的性能改善。
### 从单词到段落，尝试 2：聚类
Word2Vec 创建语义相关单词的簇，因此另一种可能的方法是利用簇中单词的相似性。 以这种方式来分组向量称为“向量量化”。 为了实现它，我们首先需要找到单词簇的中心，我们可以通过使用[聚类算法](http://scikit-learn.org/stable/modules/clustering.html)（如 [K-Means](http://en.wikipedia.org/wiki/K-means_clustering)）来完成。
在 K-Means 中，我们需要设置的一个参数是“K”，或者是簇的数量。 我们应该如何决定要创建多少个簇？ 试错法表明，每个簇平均只有5个单词左右的小簇，比具有多个词的大簇产生更好的结果。 聚类代码如下。 我们使用 [scikit-learn 来执行我们的 K-Means](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)。
具有较大 K 的 K-Means 聚类可能非常慢；以下代码在我的计算机上花了 40 多分钟。 下面，我们给 K-Means 函数设置一个计时器，看看它需要多长时间。
```py
from sklearn.cluster import KMeans
import time
start = time.time() # Start time
# 将“k”（num_clusters）设置为词汇量大小的 1/5，或每个簇平均 5 个单词
word_vectors = model.syn0
num_clusters = word_vectors.shape[0] / 5
# 初始化 k-means 对象并使用它来提取质心
kmeans_clustering = KMeans( n_clusters = num_clusters )
idx = kmeans_clustering.fit_predict( word_vectors )
# 获取结束时间并打印该过程所需的时间
end = time.time()
elapsed = end - start
print "Time taken for K Means clustering: ", elapsed, "seconds."
```
现在，每个单词的聚类分布都存储在`idx`中，而原始 Word2Vec 模型中的词汇表仍存储在`model.index2word`中。 为方便起见，我们将它们压缩成一个字典，如下所示：
```py
# 创建单词/下标字典，将每个词汇表单词映射为簇编号
word_centroid_map = dict(zip( model.index2word, idx ))
```
这有点抽象，所以让我们仔细看看我们的簇包含什么。 你的簇可能会有所不同，因为 Word2Vec 依赖于随机数种子。 这是一个循环，打印出簇 0 到 9 的单词：
```py
# 对于前 10 个簇
for cluster in xrange(0,10):
#
# 打印簇编号
print "\nCluster %d" % cluster
#
# 找到该簇编号的所有单词，然后将其打印出来
words = []
for i in xrange(0,len(word_centroid_map.values())):
if( word_centroid_map.values()[i] == cluster ):
words.append(word_centroid_map.keys()[i])
print words
```
结果很有意思：
```py
Cluster 0
[u'passport', u'penthouse', u'suite', u'seattle', u'apple']
Cluster 1
[u'unnoticed']
Cluster 2
[u'midst', u'forming', u'forefront', u'feud', u'bonds', u'merge', u'collide', u'dispute', u'rivalry', u'hostile', u'torn', u'advancing', u'aftermath', u'clans', u'ongoing', u'paths', u'opposing', u'sexes', u'factions', u'journeys']
Cluster 3
[u'lori', u'denholm', u'sheffer', u'howell', u'elton', u'gladys', u'menjou', u'caroline', u'polly', u'isabella', u'rossi', u'nora', u'bailey', u'mackenzie', u'bobbie', u'kathleen', u'bianca', u'jacqueline', u'reid', u'joyce', u'bennett', u'fay', u'alexis', u'jayne', u'roland', u'davenport', u'linden', u'trevor', u'seymour', u'craig', u'windsor', u'fletcher', u'barrie', u'deborah', u'hayward', u'samantha', u'debra', u'frances', u'hildy', u'rhonda', u'archer', u'lesley', u'dolores', u'elsie', u'harper', u'carlson', u'ella', u'preston', u'allison', u'sutton', u'yvonne', u'jo', u'bellamy', u'conte', u'stella', u'edmund', u'cuthbert', u'maude', u'ellen', u'hilary', u'phyllis', u'wray', u'darren', u'morton', u'withers', u'bain', u'keller', u'martha', u'henderson', u'madeline', u'kay', u'lacey', u'topper', u'wilding', u'jessie', u'theresa', u'auteuil', u'dane', u'jeanne', u'kathryn', u'bentley', u'valerie', u'suzanne', u'abigail']
Cluster 4
[u'fest', u'flick']
Cluster 5
[u'lobster', u'deer']
Cluster 6
[u'humorless', u'dopey', u'limp']
Cluster 7
[u'enlightening', u'truthful']
Cluster 8
[u'dominates', u'showcases', u'electrifying', u'powerhouse', u'standout', u'versatility', u'astounding']
Cluster 9
[u'succumbs', u'comatose', u'humiliating', u'temper', u'looses', u'leans']
```
我们可以看到这些簇的质量各不相同。 有些是有道理的 - 簇 3 主要包含名称，而簇 6- 8包含相关的形容词（簇 6 是我最喜欢的）。 另一方面，簇 5 有点神秘：龙虾和鹿有什么共同之处（除了是两只动物）？ 簇 0 更糟糕：阁楼和套房似乎属于一个东西，但它们似乎不属于苹果和护照。 簇 2 包含......可能与战争有关的词？ 也许我们的算法在形容词上效果最好。
无论如何，现在我们为每个单词分配了一个簇（或“质心”），我们可以定义一个函数将评论转换为质心袋。 这就像词袋一样，但使用语义相关的簇而不是单个单词：
```py
def create_bag_of_centroids( wordlist, word_centroid_map ):
#
# 簇的数量等于单词/质心映射中的最大的簇索引
num_centroids = max( word_centroid_map.values() ) + 1
#
# 预分配质心向量袋（为了速度）
bag_of_centroids = np.zeros( num_centroids, dtype="float32" )
#
# 遍历评论中的单词。如果单词在词汇表中，
# 找到它所属的簇，并将该簇的计数增加 1
for word in wordlist:
if word in word_centroid_map:
index = word_centroid_map[word]
bag_of_centroids[index] += 1
#
# 返回“质心袋”
return bag_of_centroids
```
上面的函数将为每个评论提供一个`numpy`数组，每个数组的特征都与簇数相等。 最后，我们为训练和测试集创建了质心袋，然后训练随机森林并提取结果：
```py
# 为训练集质心预分配一个数组（为了速度）
train_centroids = np.zeros( (train["review"].size, num_clusters), \
dtype="float32" )
# 将训练集评论转换为质心袋
counter = 0
for review in clean_train_reviews:
train_centroids[counter] = create_bag_of_centroids( review, \
word_centroid_map )
counter += 1
# 对测试评论重复
test_centroids = np.zeros(( test["review"].size, num_clusters), \
dtype="float32" )
counter = 0
for review in clean_test_reviews:
test_centroids[counter] = create_bag_of_centroids( review, \
word_centroid_map )
counter += 1
```
```py
# 拟合随机森林并提取预测
forest = RandomForestClassifier(n_estimators = 100)
# 拟合可能需要几分钟
print "Fitting a random forest to labeled training data..."
forest = forest.fit(train_centroids,train["sentiment"])
result = forest.predict(test_centroids)
# 写出测试结果
output = pd.DataFrame(data={"id":test["id"], "sentiment":result})
output.to_csv( "BagOfCentroids.csv", index=False, quoting=3 )
```
我们发现与第 1 部分中的词袋相比，上面的代码给出了相同（或略差）的结果。
### 深度和非深度学习方法的比较
你可能会问：为什么词袋更好？
最大的原因是，在我们的教程中，平均向量和使用质心会失去单词的顺序，这使得它与词袋的概念非常相似。性能相似（在标准误差范围内）的事实使得所有三种方法实际上相同。
一些要尝试的事情：
首先，在更多文本上训练 Word2Vec 应该会大大提高性能。谷歌的结果基于从超过十亿字的语料库中学到的单词向量；我们标记和未标记的训练集合在一起只有 1800 万字左右。方便的是，Word2Vec 提供了加载由谷歌原始 C 工具输出的任何预训练模型的函数，因此也可以用 C 训练模型然后将其导入 Python。
其次，在已发表的文献中，分布式单词向量技术已被证明优于词袋模型。在本文中，在 IMDB 数据集上使用了一种名为段落向量的算法，来生成迄今为止最先进的一些结果。在某种程度上，它比我们在这里尝试的方法更好，因为向量平均和聚类会丢失单词顺序，而段落向量会保留单词顺序信息。
================================================
FILE: docs/Kaggle/competitions/getting-started/word2vec-nlp-tutorial/官方教程翻译/README.md
================================================
# Kaggle word2vec NLP 教程
> 原文：[Bag of Words Meets Bags of Popcorn](https://www.kaggle.com/c/word2vec-nlp-tutorial)
>
> 译者：[飞龙](https://github.com/wizardforcel)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
>
> 自豪地采用[谷歌翻译](https://translate.google.cn/)
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/baseline.md
================================================
# baseline
> Author: https://www.kaggle.com/ivanwang2016
> From: https://www.kaggle.com/ivanwang2016/baseline
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
In [1]:
```py
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
# Any results you write to the current directory are saved as output.
```
```
['test', 'sample_submission.csv', 'train.csv', 'train']
```
In [2]:
```py
from PIL import Image
from tqdm import tqdm
from keras.preprocessing.image import ImageDataGenerator
def load_data(dataframe=None, batch_size=16, mode='categorical'):
if dataframe is None:
dataframe = pd.read_csv('../input/train.csv')
dataframe['has_cactus'] = dataframe['has_cactus'].apply(str)
gen = ImageDataGenerator(rescale=1./255., validation_split=0.1, horizontal_flip=True, vertical_flip=True)
trainGen = gen.flow_from_dataframe(dataframe, directory='../input/train/train', x_col='id', y_col='has_cactus', has_ext=True, target_size=(32, 32),
class_mode=mode, batch_size=batch_size, shuffle=True, subset='training')
testGen = gen.flow_from_dataframe(dataframe, directory='../input/train/train', x_col='id', y_col='has_cactus', has_ext=True, target_size=(32, 32),
class_mode=mode, batch_size=batch_size, shuffle=True, subset='validation')
return trainGen, testGen
```
```
Using TensorFlow backend.
```
In [3]:
```py
from keras.layers import Conv2D, MaxPool2D, Dense, BatchNormalization, Activation, GlobalAveragePooling2D
from keras.models import Sequential, Model
from keras.regularizers import l2
def baseline_model():
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(32, 32, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPool2D())
model.add(Conv2D(64, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPool2D())
model.add(Conv2D(128, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(1e-4)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPool2D())
model.add(GlobalAveragePooling2D())
model.add(Dense(2, activation='softmax'))
return model
```
In [4]:
```py
from keras.optimizers import Adam, SGD
from keras.callbacks import CSVLogger, ModelCheckpoint, ReduceLROnPlateau
def train_baseline():
batch_size = 32
trainGen, valGen = load_data(batch_size=batch_size)
model = baseline_model()
opt = Adam(1e-3)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
cbs = [ReduceLROnPlateau(monitor='loss', factor=0.5, patience=1, min_lr=1e-5, verbose=1)]
model.fit_generator(trainGen, steps_per_epoch=4922, epochs=3, validation_data=valGen,
validation_steps=493, shuffle=True, callbacks=cbs)
return model
def predict_baseline(model):
testdf = pd.read_csv('../input/sample_submission.csv')
pred = np.empty((testdf.shape[0],))
for n in tqdm(range(testdf.shape[0])):
data = np.array(Image.open('../input/test/test/'+testdf.id[n]))
data = data.astype(np.float32) / 255.
pred[n] = model.predict(data.reshape((1, 32, 32, 3)))[0][1]
testdf['has_cactus'] = pred
testdf.to_csv('sample_submission.csv', index=False)
```
In [5]:
```py
model = train_baseline()
predict_baseline(model)
```
```
Found 15750 images belonging to 2 classes.
Found 1750 images belonging to 2 classes.
Epoch 1/3
4922/4922 [==============================] - 247s 50ms/step - loss: 0.0674 - acc: 0.9889 - val_loss: 0.0786 - val_acc: 0.9813
Epoch 2/3
4922/4922 [==============================] - 230s 47ms/step - loss: 0.0326 - acc: 0.9965 - val_loss: 0.0428 - val_acc: 0.9888
Epoch 3/3
1400/4922 [=======>......................] - ETA: 2:35 - loss: 0.0273 - acc: 0.9974
```
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/cactus-identification-ensemble-transfer-learning.md
================================================
# Cactus Identification (Ensemble+Transfer Learning)
> Author: https://www.kaggle.com/harshel7
> From: https://www.kaggle.com/harshel7/cactus-identification-ensemble-transfer-learning
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
> Score: 0.9970
In [1]:
```py
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import glob
import os
import matplotlib.pyplot as plt
import seaborn as sns
import cv2
from tqdm import tqdm_notebook
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
# Any results you write to the current directory are saved as output.
```
```
['test', 'train', 'train.csv', 'sample_submission.csv']
```
In [2]:
```py
#read in all our files
train_df = pd.read_csv('../input/train.csv')
train_images = '../input/train/*'
test_images = '../input/test/*'
```
In [3]:
```py
train_df.head()
```
Out[3]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |
| --- | --- | --- |
| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |
| --- | --- | --- |
| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |
| --- | --- | --- |
| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |
| --- | --- | --- |
| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |
| --- | --- | --- |
In [4]:
```py
sns.set(style = 'darkgrid')
plt.figure(figsize = (12,10))
sns.countplot(train_df['has_cactus'])
```
Out[4]:
```
```
In [5]:
```py
#let's visualize some cactus images
IMAGES = os.path.join(train_images, "*")
all_images = glob.glob(IMAGES)
```
In [6]:
```py
#visualize some images
plt.figure(figsize = (12,10))
plt.subplot(1, 3, 1)
plt.imshow(plt.imread(all_images[0]))
plt.xticks([])
plt.yticks([])
plt.subplot(1, 3, 2)
plt.imshow(plt.imread(all_images[10]))
plt.xticks([])
plt.yticks([])
plt.subplot(1, 3, 3)
plt.imshow(plt.imread(all_images[20]))
plt.xticks([])
plt.yticks([])
```
Out[6]:
```
([], )
```
In [7]:
```py
train_path = '../input/train/train/'
test_path = '../input/test/test/'
```
In [8]:
```py
#let's get our image data and image labels toegether
#read in all the images
images_id = train_df['id'].values
X = [] #this list will contain all our images
for id_ in images_id:
img = cv2.imread(train_path + id_)
X.append(img)
```
In [9]:
```py
#now let's get our labels
label_list = [] #will contain all our labels
for img_id in images_id:
label_list.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])
```
In [10]:
```py
#now we can convert our images list and the labels list into numpy array
X = np.array(X)
y = np.array(label_list)
```
In [11]:
```py
print(f"THE SIZE OF OUR TRAINING DATA : {X.shape}")
print(f"THE SIZE OF OUR TRAINING LABELS : {y.shape}")
```
```
THE SIZE OF OUR TRAINING DATA : (17500, 32, 32, 3)
THE SIZE OF OUR TRAINING LABELS : (17500,)
```
In [12]:
```py
#let's do some preprocessing such as normalizing our data
X = X.astype('float32') / 255
```
In [13]:
```py
#loading in and preprocessing the test data
X_test = []
test_images = []
for img_id in tqdm_notebook(os.listdir(test_path)):
X_test.append(cv2.imread(test_path + img_id))
test_images.append(img_id)
X_test = np.array(X_test)
X_test = X_test.astype('float32') / 255
```
## BUILD CNN
In [14]:
```py
#import the required libraries
import keras
from keras.layers import Conv2D
from keras.layers import Dense
from keras.layers import BatchNormalization
from keras.layers import Flatten
from keras.layers import Activation
from keras.layers import Dropout
from keras.layers import MaxPooling2D
from keras.models import Sequential
from keras import optimizers
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from keras import backend as K
```
```
Using TensorFlow backend.
```
In [15]:
```py
class CNN:
def build(height, width, classes, channels):
model = Sequential()
inputShape = (height, width, channels)
chanDim = -1
if K.image_data_format() == 'channels_first':
inputShape = (channels, height, width)
chanDim = 1
model.add(Conv2D(32, (3,3), padding = 'same', input_shape = inputShape))
model.add(BatchNormalization(axis = chanDim))
model.add(Activation('relu'))
model.add(Conv2D(32, (3,3), padding = 'same'))
model.add(BatchNormalization(axis = chanDim))
model.add(Activation('relu'))
model.add(MaxPooling2D(2,2))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3,3), padding = 'same', input_shape = inputShape))
model.add(BatchNormalization(axis = chanDim))
model.add(Activation('relu'))
model.add(Conv2D(128, (3,3), padding = 'same'))
model.add(BatchNormalization(axis = chanDim))
model.add(Activation('relu'))
model.add(MaxPooling2D(2,2))
model.add(Dropout(0.25))
model.add(Conv2D(256, (3,3), padding = 'same', input_shape = inputShape))
model.add(BatchNormalization(axis = chanDim))
model.add(Activation('relu'))
model.add(Conv2D(256, (3,3), padding = 'same'))
model.add(BatchNormalization(axis = chanDim))
model.add(Activation('relu'))
model.add(MaxPooling2D(2,2))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation = 'relu'))
model.add(BatchNormalization(axis = chanDim))
model.add(Dropout(0.5))
model.add(Dense(32, activation = 'relu'))
model.add(BatchNormalization(axis = chanDim))
model.add(Dropout(0.5))
model.add(Dense(classes, activation = 'sigmoid'))
return model
```
## ENSEMBLE NEURAL NETWORK
OutputIn [16]:
```py
input_dim = X.shape[1:]
activation = 'relu'
classes = 1
height = 32
width = 32
channels = 3
history = dict() #dictionery to store the history of individual models for later visualization
prediction_scores = dict() #dictionery to store the predicted scores of individual models on the test dataset
#here we will be training the same model for a total of 10 times and will be considering the mean of the output values for predictions
for i in np.arange(0, 5):
optim = optimizers.Adam(lr = 0.001)
ensemble_model = CNN.build(height = height, width = width, classes = classes, channels = channels)
ensemble_model.compile(loss = 'binary_crossentropy', optimizer = optim, metrics = ['accuracy'])
print('TRAINING MODEL NO : {}'.format(i))
H = ensemble_model.fit(X, y,
batch_size = 32,
epochs = 200,
verbose = 1)
history[i] = H
ensemble_model.save('MODEL_{}.model'.format(i))
predictions = ensemble_model.predict(X_test, verbose = 1, batch_size = 32)
prediction_scores[i] = predictions
```
```
TRAINING MODEL NO : 0
Epoch 1/200
17500/17500 [==============================] - 12s 711us/step - loss: 0.2101 - acc: 0.9271
Epoch 2/200
17500/17500 [==============================] - 8s 461us/step - loss: 0.0847 - acc: 0.9743
Epoch 3/200
17500/17500 [==============================] - 8s 467us/step - loss: 0.0669 - acc: 0.9787
Epoch 4/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0560 - acc: 0.9831
Epoch 5/200
17500/17500 [==============================] - 8s 463us/step - loss: 0.0398 - acc: 0.9883
Epoch 6/200
17500/17500 [==============================] - 8s 461us/step - loss: 0.0435 - acc: 0.9875
Epoch 7/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0310 - acc: 0.9905
Epoch 8/200
17500/17500 [==============================] - 8s 461us/step - loss: 0.0294 - acc: 0.9914
Epoch 9/200
17500/17500 [==============================] - 8s 457us/step - loss: 0.0223 - acc: 0.9937
Epoch 10/200
17500/17500 [==============================] - 8s 458us/step - loss: 0.0260 - acc: 0.9921
Epoch 11/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0248 - acc: 0.9931
Epoch 12/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0168 - acc: 0.9946
Epoch 13/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0230 - acc: 0.9933
Epoch 14/200
17500/17500 [==============================] - 8s 458us/step - loss: 0.0149 - acc: 0.9955
Epoch 15/200
17500/17500 [==============================] - 8s 458us/step - loss: 0.0171 - acc: 0.9949
Epoch 16/200
17500/17500 [==============================] - 8s 459us/step - loss: 0.0141 - acc: 0.9964
Epoch 17/200
17500/17500 [==============================] - 8s 458us/step - loss: 0.0107 - acc: 0.9969
Epoch 18/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0118 - acc: 0.9966
Epoch 19/200
17500/17500 [==============================] - 8s 459us/step - loss: 0.0116 - acc: 0.9966
Epoch 20/200
17500/17500 [==============================] - 8s 458us/step - loss: 0.0113 - acc: 0.9966
Epoch 21/200
17500/17500 [==============================] - 8s 458us/step - loss: 0.0081 - acc: 0.9980
Epoch 22/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0102 - acc: 0.9969
Epoch 23/200
17500/17500 [==============================] - 8s 459us/step - loss: 0.0080 - acc: 0.9978
Epoch 24/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0103 - acc: 0.9973
Epoch 25/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0071 - acc: 0.9981
Epoch 26/200
17500/17500 [==============================] - 9s 504us/step - loss: 0.0060 - acc: 0.9982
Epoch 27/200
17500/17500 [==============================] - 8s 458us/step - loss: 0.0087 - acc: 0.9975
Epoch 28/200
17500/17500 [==============================] - 8s 457us/step - loss: 0.0079 - acc: 0.9974
Epoch 29/200
17500/17500 [==============================] - 8s 461us/step - loss: 0.0036 - acc: 0.9987
Epoch 30/200
17500/17500 [==============================] - 8s 458us/step - loss: 0.0045 - acc: 0.9986
Epoch 31/200
17500/17500 [==============================] - 8s 457us/step - loss: 0.0095 - acc: 0.9975
Epoch 32/200
17500/17500 [==============================] - 9s 494us/step - loss: 0.0064 - acc: 0.9976
Epoch 33/200
17500/17500 [==============================] - 9s 514us/step - loss: 0.0058 - acc: 0.9982
Epoch 34/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0061 - acc: 0.9984
Epoch 35/200
17500/17500 [==============================] - 8s 459us/step - loss: 0.0015 - acc: 0.9997
Epoch 36/200
17500/17500 [==============================] - 8s 459us/step - loss: 0.0065 - acc: 0.9981
Epoch 37/200
17500/17500 [==============================] - 8s 461us/step - loss: 0.0058 - acc: 0.9982
Epoch 38/200
17500/17500 [==============================] - 8s 459us/step - loss: 0.0035 - acc: 0.9989
Epoch 39/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0030 - acc: 0.9991
Epoch 40/200
17500/17500 [==============================] - 8s 459us/step - loss: 0.0049 - acc: 0.9985
Epoch 41/200
17500/17500 [==============================] - 8s 465us/step - loss: 0.0044 - acc: 0.9987
Epoch 42/200
17500/17500 [==============================] - 8s 459us/step - loss: 0.0032 - acc: 0.9990
Epoch 43/200
17500/17500 [==============================] - 8s 458us/step - loss: 0.0086 - acc: 0.9974
Epoch 44/200
17500/17500 [==============================] - 8s 460us/step - loss: 0.0027 - acc: 0.9991
Epoch 45/200
17500/17500 [==============================] - 8s 461us/step - loss: 0.0020 - acc: 0.9997
Epoch 46/200
3488/17500 [====>.........................] - ETA: 6s - loss: 2.9141e-04 - acc: 1.0000
```
## VGG16
In [17]:
```py
from keras.applications.vgg16 import VGG16
```
In [18]:
```py
vgg16 = VGG16(weights = 'imagenet', input_shape = (32, 32, 3), include_top = False)
vgg16.summary()
```
```
Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
58892288/58889256 [==============================] - 1s 0us/step
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 32, 32, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 32, 32, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 32, 32, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 16, 16, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 16, 16, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 16, 16, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 8, 8, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 8, 8, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 8, 8, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 8, 8, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 4, 4, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 4, 4, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 4, 4, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 4, 4, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 2, 2, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 2, 2, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 2, 2, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 2, 2, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 1, 1, 512) 0
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________
```
In [19]:
```py
for layer in vgg16.layers:
layer.trainable = False
```
In [20]:
```py
vgg_model = Sequential()
vgg_model.add(vgg16)
vgg_model.add(Flatten())
vgg_model.add(Dense(256, activation = 'relu'))
vgg_model.add(BatchNormalization())
vgg_model.add(Dropout(0.5))
vgg_model.add(Dense(128, activation = 'relu'))
vgg_model.add(BatchNormalization())
vgg_model.add(Dropout(0.5))
vgg_model.add(Dense(1, activation = 'sigmoid'))
vgg_model.summary()
```
```
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
vgg16 (Model) (None, 1, 1, 512) 14714688
_________________________________________________________________
flatten_6 (Flatten) (None, 512) 0
_________________________________________________________________
dense_16 (Dense) (None, 256) 131328
_________________________________________________________________
batch_normalization_41 (Batc (None, 256) 1024
_________________________________________________________________
dropout_26 (Dropout) (None, 256) 0
_________________________________________________________________
dense_17 (Dense) (None, 128) 32896
_________________________________________________________________
batch_normalization_42 (Batc (None, 128) 512
_________________________________________________________________
dropout_27 (Dropout) (None, 128) 0
_________________________________________________________________
dense_18 (Dense) (None, 1) 129
=================================================================
Total params: 14,880,577
Trainable params: 165,121
Non-trainable params: 14,715,456
_________________________________________________________________
```
In [21]:
```py
#compile the model
vgg_model.compile(loss = 'binary_crossentropy', optimizer = optim, metrics = ['accuracy'])
```
In [22]:
```py
#fit the model on our data
vgg_history = vgg_model.fit(X, y,
batch_size = 64,
epochs = 500,
verbose = 1)
```
```
Epoch 1/500
17500/17500 [==============================] - 6s 320us/step - loss: 0.1719 - acc: 0.9358
Epoch 2/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.1074 - acc: 0.9611
Epoch 3/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0990 - acc: 0.9636
Epoch 4/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0892 - acc: 0.9658
Epoch 5/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0869 - acc: 0.9683
Epoch 6/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0828 - acc: 0.9701
Epoch 7/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0766 - acc: 0.9723
Epoch 8/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0802 - acc: 0.9686
Epoch 9/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0755 - acc: 0.9718
Epoch 10/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0752 - acc: 0.9714
Epoch 11/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0719 - acc: 0.9731
Epoch 12/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0718 - acc: 0.9762
Epoch 13/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0702 - acc: 0.9744
Epoch 14/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0694 - acc: 0.9745
Epoch 15/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0687 - acc: 0.9751
Epoch 16/500
17500/17500 [==============================] - 3s 200us/step - loss: 0.0699 - acc: 0.9752
Epoch 17/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0667 - acc: 0.9755
Epoch 18/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0639 - acc: 0.9765
Epoch 19/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0636 - acc: 0.9763
Epoch 20/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0661 - acc: 0.9759
Epoch 21/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0607 - acc: 0.9777
Epoch 22/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0627 - acc: 0.9763
Epoch 23/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0609 - acc: 0.9781
Epoch 24/500
17500/17500 [==============================] - 4s 205us/step - loss: 0.0618 - acc: 0.9776
Epoch 25/500
17500/17500 [==============================] - 4s 229us/step - loss: 0.0636 - acc: 0.9764
Epoch 26/500
17500/17500 [==============================] - 4s 229us/step - loss: 0.0623 - acc: 0.9777
Epoch 27/500
17500/17500 [==============================] - 4s 215us/step - loss: 0.0576 - acc: 0.9797
Epoch 28/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0604 - acc: 0.9779
Epoch 29/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0590 - acc: 0.9782
Epoch 30/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0600 - acc: 0.9779
Epoch 31/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0557 - acc: 0.9782
Epoch 32/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0586 - acc: 0.9778
Epoch 33/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0575 - acc: 0.9791
Epoch 34/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0586 - acc: 0.9777
Epoch 35/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0609 - acc: 0.9779
Epoch 36/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0544 - acc: 0.9805
Epoch 37/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0550 - acc: 0.9800
Epoch 38/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0560 - acc: 0.9797
Epoch 39/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0554 - acc: 0.9796
Epoch 40/500
17500/17500 [==============================] - 4s 204us/step - loss: 0.0581 - acc: 0.9791
Epoch 41/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0549 - acc: 0.9795
Epoch 42/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0544 - acc: 0.9799
Epoch 43/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0544 - acc: 0.9801
Epoch 44/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0534 - acc: 0.9803
Epoch 45/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0526 - acc: 0.9812
Epoch 46/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0547 - acc: 0.9801
Epoch 47/500
17500/17500 [==============================] - 4s 204us/step - loss: 0.0536 - acc: 0.9800
Epoch 48/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0492 - acc: 0.9817
Epoch 49/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0553 - acc: 0.9802
Epoch 50/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0502 - acc: 0.9820
Epoch 51/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0505 - acc: 0.9823
Epoch 52/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0534 - acc: 0.9801
Epoch 53/500
17500/17500 [==============================] - 4s 204us/step - loss: 0.0505 - acc: 0.9809
Epoch 54/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0460 - acc: 0.9831
Epoch 55/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0492 - acc: 0.9819
Epoch 56/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0481 - acc: 0.9826
Epoch 57/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0515 - acc: 0.9814
Epoch 58/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0495 - acc: 0.9816
Epoch 59/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0476 - acc: 0.9837
Epoch 60/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0483 - acc: 0.9821
Epoch 61/500
17500/17500 [==============================] - 4s 216us/step - loss: 0.0456 - acc: 0.9830
Epoch 62/500
17500/17500 [==============================] - 4s 227us/step - loss: 0.0472 - acc: 0.9829
Epoch 63/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0462 - acc: 0.9833
Epoch 64/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0496 - acc: 0.9828
Epoch 65/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0482 - acc: 0.9826
Epoch 66/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0477 - acc: 0.9828
Epoch 67/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0465 - acc: 0.9825
Epoch 68/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0481 - acc: 0.9827
Epoch 69/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0470 - acc: 0.9830
Epoch 70/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0469 - acc: 0.9833
Epoch 71/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0470 - acc: 0.9819
Epoch 72/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0471 - acc: 0.9829
Epoch 73/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0466 - acc: 0.9835
Epoch 74/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0461 - acc: 0.9838
Epoch 75/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0439 - acc: 0.9841
Epoch 76/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0462 - acc: 0.9830
Epoch 77/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0465 - acc: 0.9831
Epoch 78/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0452 - acc: 0.9832
Epoch 79/500
17500/17500 [==============================] - 4s 205us/step - loss: 0.0452 - acc: 0.9829
Epoch 80/500
17500/17500 [==============================] - 4s 204us/step - loss: 0.0444 - acc: 0.9835
Epoch 81/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0463 - acc: 0.9823
Epoch 82/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0461 - acc: 0.9829
Epoch 83/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0436 - acc: 0.9845
Epoch 84/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0468 - acc: 0.9837
Epoch 85/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0405 - acc: 0.9850
Epoch 86/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0416 - acc: 0.9845
Epoch 87/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0419 - acc: 0.9842
Epoch 88/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0439 - acc: 0.9841
Epoch 89/500
17500/17500 [==============================] - 4s 200us/step - loss: 0.0393 - acc: 0.9858
Epoch 90/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0440 - acc: 0.9853
Epoch 91/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0428 - acc: 0.9847
Epoch 92/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0451 - acc: 0.9838
Epoch 93/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0410 - acc: 0.9849
Epoch 94/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0419 - acc: 0.9846
Epoch 95/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0427 - acc: 0.9847
Epoch 96/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0394 - acc: 0.9855
Epoch 97/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0419 - acc: 0.9844
Epoch 98/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0412 - acc: 0.9850
Epoch 99/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0390 - acc: 0.9859
Epoch 100/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0442 - acc: 0.9843
Epoch 101/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0414 - acc: 0.9853
Epoch 102/500
17500/17500 [==============================] - 4s 204us/step - loss: 0.0414 - acc: 0.9844
Epoch 103/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0390 - acc: 0.9864
Epoch 104/500
17500/17500 [==============================] - 4s 204us/step - loss: 0.0426 - acc: 0.9846
Epoch 105/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0394 - acc: 0.9859
Epoch 106/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0399 - acc: 0.9861
Epoch 107/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0398 - acc: 0.9857
Epoch 108/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0426 - acc: 0.9851
Epoch 109/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0407 - acc: 0.9852
Epoch 110/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0421 - acc: 0.9847
Epoch 111/500
17500/17500 [==============================] - 4s 204us/step - loss: 0.0386 - acc: 0.9860
Epoch 112/500
17500/17500 [==============================] - 4s 227us/step - loss: 0.0422 - acc: 0.9847
Epoch 113/500
17500/17500 [==============================] - 4s 230us/step - loss: 0.0382 - acc: 0.9863
Epoch 114/500
17500/17500 [==============================] - 4s 219us/step - loss: 0.0386 - acc: 0.9865
Epoch 115/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0391 - acc: 0.9857
Epoch 116/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0406 - acc: 0.9843
Epoch 117/500
17500/17500 [==============================] - 4s 200us/step - loss: 0.0376 - acc: 0.9864
Epoch 118/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0397 - acc: 0.9853
Epoch 119/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0394 - acc: 0.9861
Epoch 120/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0365 - acc: 0.9866
Epoch 121/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0391 - acc: 0.9855
Epoch 122/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0383 - acc: 0.9861
Epoch 123/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0360 - acc: 0.9874
Epoch 124/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0357 - acc: 0.9869
Epoch 125/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0376 - acc: 0.9862
Epoch 126/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0389 - acc: 0.9855
Epoch 127/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0405 - acc: 0.9852
Epoch 128/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0370 - acc: 0.9861
Epoch 129/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0378 - acc: 0.9867
Epoch 130/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0367 - acc: 0.9861
Epoch 131/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0375 - acc: 0.9861
Epoch 132/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0376 - acc: 0.9860
Epoch 133/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0380 - acc: 0.9867
Epoch 134/500
17500/17500 [==============================] - 3s 200us/step - loss: 0.0394 - acc: 0.9866
Epoch 135/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0371 - acc: 0.9875
Epoch 136/500
17500/17500 [==============================] - 4s 204us/step - loss: 0.0399 - acc: 0.9859
Epoch 137/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0377 - acc: 0.9874
Epoch 138/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0368 - acc: 0.9874
Epoch 139/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0400 - acc: 0.9851
Epoch 140/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0395 - acc: 0.9854
Epoch 141/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0366 - acc: 0.9869
Epoch 142/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0375 - acc: 0.9871
Epoch 143/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0354 - acc: 0.9881
Epoch 144/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0367 - acc: 0.9861
Epoch 145/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0380 - acc: 0.9864
Epoch 146/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0352 - acc: 0.9876
Epoch 147/500
17500/17500 [==============================] - 4s 221us/step - loss: 0.0343 - acc: 0.9874
Epoch 148/500
17500/17500 [==============================] - 4s 219us/step - loss: 0.0351 - acc: 0.9873
Epoch 149/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0364 - acc: 0.9863
Epoch 150/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0376 - acc: 0.9870
Epoch 151/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0385 - acc: 0.9869
Epoch 152/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0342 - acc: 0.9883
Epoch 153/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0355 - acc: 0.9867
Epoch 154/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0338 - acc: 0.9874
Epoch 155/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0360 - acc: 0.9868
Epoch 156/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0329 - acc: 0.9874
Epoch 157/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0383 - acc: 0.9856
Epoch 158/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0362 - acc: 0.9872
Epoch 159/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0361 - acc: 0.9862
Epoch 160/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0361 - acc: 0.9866
Epoch 161/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0342 - acc: 0.9875
Epoch 162/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0348 - acc: 0.9873
Epoch 163/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0350 - acc: 0.9871
Epoch 164/500
17500/17500 [==============================] - 4s 206us/step - loss: 0.0364 - acc: 0.9859
Epoch 165/500
17500/17500 [==============================] - 4s 205us/step - loss: 0.0317 - acc: 0.9881
Epoch 166/500
17500/17500 [==============================] - 3s 200us/step - loss: 0.0355 - acc: 0.9872
Epoch 167/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0406 - acc: 0.9864
Epoch 168/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0364 - acc: 0.9866
Epoch 169/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0352 - acc: 0.9879
Epoch 170/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0366 - acc: 0.9869
Epoch 171/500
17500/17500 [==============================] - 4s 203us/step - loss: 0.0362 - acc: 0.9872
Epoch 172/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0359 - acc: 0.9866
Epoch 173/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0345 - acc: 0.9869
Epoch 174/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0364 - acc: 0.9877
Epoch 175/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0339 - acc: 0.9875
Epoch 176/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0356 - acc: 0.9871
Epoch 177/500
17500/17500 [==============================] - 4s 201us/step - loss: 0.0323 - acc: 0.9886
Epoch 178/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0336 - acc: 0.9882
Epoch 179/500
17500/17500 [==============================] - 4s 202us/step - loss: 0.0336 - acc: 0.9886
Epoch 180/500
4928/17500 [=======>......................] - ETA: 2s - loss: 0.0328 - acc: 0.9874
```
In [23]:
```py
#making predictions on test dat
predictions_vgg = vgg_model.predict(X_test)
```
In [24]:
```py
predictions_vgg.shape
```
Out[24]:
```
(4000, 1)
```
## RESNET50
In [25]:
```py
from keras.applications.resnet50 import ResNet50
```
OutputIn [26]:
```py
resnet = ResNet50(weights = 'imagenet', input_shape = (32, 32, 3), include_top = False)
resnet.summary()
```
```
/opt/conda/lib/python3.6/site-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.
warnings.warn('The output shape of `ResNet50(include_top=False)` '
```
```
Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
94658560/94653016 [==============================] - 1s 0us/step
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) (None, 32, 32, 3) 0
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D) (None, 38, 38, 3) 0 input_2[0][0]
__________________________________________________________________________________________________
conv1 (Conv2D) (None, 16, 16, 64) 9472 conv1_pad[0][0]
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization) (None, 16, 16, 64) 256 conv1[0][0]
__________________________________________________________________________________________________
activation_31 (Activation) (None, 16, 16, 64) 0 bn_conv1[0][0]
__________________________________________________________________________________________________
pool1_pad (ZeroPadding2D) (None, 18, 18, 64) 0 activation_31[0][0]
__________________________________________________________________________________________________
max_pooling2d_16 (MaxPooling2D) (None, 8, 8, 64) 0 pool1_pad[0][0]
__________________________________________________________________________________________________
res2a_branch2a (Conv2D) (None, 8, 8, 64) 4160 max_pooling2d_16[0][0]
__________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizati (None, 8, 8, 64) 256 res2a_branch2a[0][0]
__________________________________________________________________________________________________
activation_32 (Activation) (None, 8, 8, 64) 0 bn2a_branch2a[0][0]
__________________________________________________________________________________________________
res2a_branch2b (Conv2D) (None, 8, 8, 64) 36928 activation_32[0][0]
__________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizati (None, 8, 8, 64) 256 res2a_branch2b[0][0]
__________________________________________________________________________________________________
activation_33 (Activation) (None, 8, 8, 64) 0 bn2a_branch2b[0][0]
__________________________________________________________________________________________________
res2a_branch2c (Conv2D) (None, 8, 8, 256) 16640 activation_33[0][0]
__________________________________________________________________________________________________
res2a_branch1 (Conv2D) (None, 8, 8, 256) 16640 max_pooling2d_16[0][0]
__________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizati (None, 8, 8, 256) 1024 res2a_branch2c[0][0]
__________________________________________________________________________________________________
bn2a_branch1 (BatchNormalizatio (None, 8, 8, 256) 1024 res2a_branch1[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 8, 8, 256) 0 bn2a_branch2c[0][0]
bn2a_branch1[0][0]
__________________________________________________________________________________________________
activation_34 (Activation) (None, 8, 8, 256) 0 add_1[0][0]
__________________________________________________________________________________________________
res2b_branch2a (Conv2D) (None, 8, 8, 64) 16448 activation_34[0][0]
__________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizati (None, 8, 8, 64) 256 res2b_branch2a[0][0]
__________________________________________________________________________________________________
activation_35 (Activation) (None, 8, 8, 64) 0 bn2b_branch2a[0][0]
__________________________________________________________________________________________________
res2b_branch2b (Conv2D) (None, 8, 8, 64) 36928 activation_35[0][0]
__________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizati (None, 8, 8, 64) 256 res2b_branch2b[0][0]
__________________________________________________________________________________________________
activation_36 (Activation) (None, 8, 8, 64) 0 bn2b_branch2b[0][0]
__________________________________________________________________________________________________
res2b_branch2c (Conv2D) (None, 8, 8, 256) 16640 activation_36[0][0]
__________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizati (None, 8, 8, 256) 1024 res2b_branch2c[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 8, 8, 256) 0 bn2b_branch2c[0][0]
activation_34[0][0]
__________________________________________________________________________________________________
activation_37 (Activation) (None, 8, 8, 256) 0 add_2[0][0]
__________________________________________________________________________________________________
res2c_branch2a (Conv2D) (None, 8, 8, 64) 16448 activation_37[0][0]
__________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizati (None, 8, 8, 64) 256 res2c_branch2a[0][0]
__________________________________________________________________________________________________
activation_38 (Activation) (None, 8, 8, 64) 0 bn2c_branch2a[0][0]
__________________________________________________________________________________________________
res2c_branch2b (Conv2D) (None, 8, 8, 64) 36928 activation_38[0][0]
__________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizati (None, 8, 8, 64) 256 res2c_branch2b[0][0]
__________________________________________________________________________________________________
activation_39 (Activation) (None, 8, 8, 64) 0 bn2c_branch2b[0][0]
__________________________________________________________________________________________________
res2c_branch2c (Conv2D) (None, 8, 8, 256) 16640 activation_39[0][0]
__________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizati (None, 8, 8, 256) 1024 res2c_branch2c[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 8, 8, 256) 0 bn2c_branch2c[0][0]
activation_37[0][0]
__________________________________________________________________________________________________
activation_40 (Activation) (None, 8, 8, 256) 0 add_3[0][0]
__________________________________________________________________________________________________
res3a_branch2a (Conv2D) (None, 4, 4, 128) 32896 activation_40[0][0]
__________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizati (None, 4, 4, 128) 512 res3a_branch2a[0][0]
__________________________________________________________________________________________________
activation_41 (Activation) (None, 4, 4, 128) 0 bn3a_branch2a[0][0]
__________________________________________________________________________________________________
res3a_branch2b (Conv2D) (None, 4, 4, 128) 147584 activation_41[0][0]
__________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizati (None, 4, 4, 128) 512 res3a_branch2b[0][0]
__________________________________________________________________________________________________
activation_42 (Activation) (None, 4, 4, 128) 0 bn3a_branch2b[0][0]
__________________________________________________________________________________________________
res3a_branch2c (Conv2D) (None, 4, 4, 512) 66048 activation_42[0][0]
__________________________________________________________________________________________________
res3a_branch1 (Conv2D) (None, 4, 4, 512) 131584 activation_40[0][0]
__________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizati (None, 4, 4, 512) 2048 res3a_branch2c[0][0]
__________________________________________________________________________________________________
bn3a_branch1 (BatchNormalizatio (None, 4, 4, 512) 2048 res3a_branch1[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, 4, 4, 512) 0 bn3a_branch2c[0][0]
bn3a_branch1[0][0]
__________________________________________________________________________________________________
activation_43 (Activation) (None, 4, 4, 512) 0 add_4[0][0]
__________________________________________________________________________________________________
res3b_branch2a (Conv2D) (None, 4, 4, 128) 65664 activation_43[0][0]
__________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizati (None, 4, 4, 128) 512 res3b_branch2a[0][0]
__________________________________________________________________________________________________
activation_44 (Activation) (None, 4, 4, 128) 0 bn3b_branch2a[0][0]
__________________________________________________________________________________________________
res3b_branch2b (Conv2D) (None, 4, 4, 128) 147584 activation_44[0][0]
__________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizati (None, 4, 4, 128) 512 res3b_branch2b[0][0]
__________________________________________________________________________________________________
activation_45 (Activation) (None, 4, 4, 128) 0 bn3b_branch2b[0][0]
__________________________________________________________________________________________________
res3b_branch2c (Conv2D) (None, 4, 4, 512) 66048 activation_45[0][0]
__________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizati (None, 4, 4, 512) 2048 res3b_branch2c[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, 4, 4, 512) 0 bn3b_branch2c[0][0]
activation_43[0][0]
__________________________________________________________________________________________________
activation_46 (Activation) (None, 4, 4, 512) 0 add_5[0][0]
__________________________________________________________________________________________________
res3c_branch2a (Conv2D) (None, 4, 4, 128) 65664 activation_46[0][0]
__________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizati (None, 4, 4, 128) 512 res3c_branch2a[0][0]
__________________________________________________________________________________________________
activation_47 (Activation) (None, 4, 4, 128) 0 bn3c_branch2a[0][0]
__________________________________________________________________________________________________
res3c_branch2b (Conv2D) (None, 4, 4, 128) 147584 activation_47[0][0]
__________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizati (None, 4, 4, 128) 512 res3c_branch2b[0][0]
__________________________________________________________________________________________________
activation_48 (Activation) (None, 4, 4, 128) 0 bn3c_branch2b[0][0]
__________________________________________________________________________________________________
res3c_branch2c (Conv2D) (None, 4, 4, 512) 66048 activation_48[0][0]
__________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizati (None, 4, 4, 512) 2048 res3c_branch2c[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, 4, 4, 512) 0 bn3c_branch2c[0][0]
activation_46[0][0]
__________________________________________________________________________________________________
activation_49 (Activation) (None, 4, 4, 512) 0 add_6[0][0]
__________________________________________________________________________________________________
res3d_branch2a (Conv2D) (None, 4, 4, 128) 65664 activation_49[0][0]
__________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizati (None, 4, 4, 128) 512 res3d_branch2a[0][0]
__________________________________________________________________________________________________
activation_50 (Activation) (None, 4, 4, 128) 0 bn3d_branch2a[0][0]
__________________________________________________________________________________________________
res3d_branch2b (Conv2D) (None, 4, 4, 128) 147584 activation_50[0][0]
__________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizati (None, 4, 4, 128) 512 res3d_branch2b[0][0]
__________________________________________________________________________________________________
activation_51 (Activation) (None, 4, 4, 128) 0 bn3d_branch2b[0][0]
__________________________________________________________________________________________________
res3d_branch2c (Conv2D) (None, 4, 4, 512) 66048 activation_51[0][0]
__________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizati (None, 4, 4, 512) 2048 res3d_branch2c[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, 4, 4, 512) 0 bn3d_branch2c[0][0]
activation_49[0][0]
__________________________________________________________________________________________________
activation_52 (Activation) (None, 4, 4, 512) 0 add_7[0][0]
__________________________________________________________________________________________________
res4a_branch2a (Conv2D) (None, 2, 2, 256) 131328 activation_52[0][0]
__________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizati (None, 2, 2, 256) 1024 res4a_branch2a[0][0]
__________________________________________________________________________________________________
activation_53 (Activation) (None, 2, 2, 256) 0 bn4a_branch2a[0][0]
__________________________________________________________________________________________________
res4a_branch2b (Conv2D) (None, 2, 2, 256) 590080 activation_53[0][0]
__________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizati (None, 2, 2, 256) 1024 res4a_branch2b[0][0]
__________________________________________________________________________________________________
activation_54 (Activation) (None, 2, 2, 256) 0 bn4a_branch2b[0][0]
__________________________________________________________________________________________________
res4a_branch2c (Conv2D) (None, 2, 2, 1024) 263168 activation_54[0][0]
__________________________________________________________________________________________________
res4a_branch1 (Conv2D) (None, 2, 2, 1024) 525312 activation_52[0][0]
__________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizati (None, 2, 2, 1024) 4096 res4a_branch2c[0][0]
__________________________________________________________________________________________________
bn4a_branch1 (BatchNormalizatio (None, 2, 2, 1024) 4096 res4a_branch1[0][0]
__________________________________________________________________________________________________
add_8 (Add) (None, 2, 2, 1024) 0 bn4a_branch2c[0][0]
bn4a_branch1[0][0]
__________________________________________________________________________________________________
activation_55 (Activation) (None, 2, 2, 1024) 0 add_8[0][0]
__________________________________________________________________________________________________
res4b_branch2a (Conv2D) (None, 2, 2, 256) 262400 activation_55[0][0]
__________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizati (None, 2, 2, 256) 1024 res4b_branch2a[0][0]
__________________________________________________________________________________________________
activation_56 (Activation) (None, 2, 2, 256) 0 bn4b_branch2a[0][0]
__________________________________________________________________________________________________
res4b_branch2b (Conv2D) (None, 2, 2, 256) 590080 activation_56[0][0]
__________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizati (None, 2, 2, 256) 1024 res4b_branch2b[0][0]
__________________________________________________________________________________________________
activation_57 (Activation) (None, 2, 2, 256) 0 bn4b_branch2b[0][0]
__________________________________________________________________________________________________
res4b_branch2c (Conv2D) (None, 2, 2, 1024) 263168 activation_57[0][0]
__________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizati (None, 2, 2, 1024) 4096 res4b_branch2c[0][0]
__________________________________________________________________________________________________
add_9 (Add) (None, 2, 2, 1024) 0 bn4b_branch2c[0][0]
activation_55[0][0]
__________________________________________________________________________________________________
activation_58 (Activation) (None, 2, 2, 1024) 0 add_9[0][0]
__________________________________________________________________________________________________
res4c_branch2a (Conv2D) (None, 2, 2, 256) 262400 activation_58[0][0]
__________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizati (None, 2, 2, 256) 1024 res4c_branch2a[0][0]
__________________________________________________________________________________________________
activation_59 (Activation) (None, 2, 2, 256) 0 bn4c_branch2a[0][0]
__________________________________________________________________________________________________
res4c_branch2b (Conv2D) (None, 2, 2, 256) 590080 activation_59[0][0]
__________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizati (None, 2, 2, 256) 1024 res4c_branch2b[0][0]
__________________________________________________________________________________________________
activation_60 (Activation) (None, 2, 2, 256) 0 bn4c_branch2b[0][0]
__________________________________________________________________________________________________
res4c_branch2c (Conv2D) (None, 2, 2, 1024) 263168 activation_60[0][0]
__________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizati (None, 2, 2, 1024) 4096 res4c_branch2c[0][0]
__________________________________________________________________________________________________
add_10 (Add) (None, 2, 2, 1024) 0 bn4c_branch2c[0][0]
activation_58[0][0]
__________________________________________________________________________________________________
activation_61 (Activation) (None, 2, 2, 1024) 0 add_10[0][0]
__________________________________________________________________________________________________
res4d_branch2a (Conv2D) (None, 2, 2, 256) 262400 activation_61[0][0]
__________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizati (None, 2, 2, 256) 1024 res4d_branch2a[0][0]
__________________________________________________________________________________________________
activation_62 (Activation) (None, 2, 2, 256) 0 bn4d_branch2a[0][0]
__________________________________________________________________________________________________
res4d_branch2b (Conv2D) (None, 2, 2, 256) 590080 activation_62[0][0]
__________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizati (None, 2, 2, 256) 1024 res4d_branch2b[0][0]
__________________________________________________________________________________________________
activation_63 (Activation) (None, 2, 2, 256) 0 bn4d_branch2b[0][0]
__________________________________________________________________________________________________
res4d_branch2c (Conv2D) (None, 2, 2, 1024) 263168 activation_63[0][0]
__________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizati (None, 2, 2, 1024) 4096 res4d_branch2c[0][0]
__________________________________________________________________________________________________
add_11 (Add) (None, 2, 2, 1024) 0 bn4d_branch2c[0][0]
activation_61[0][0]
__________________________________________________________________________________________________
activation_64 (Activation) (None, 2, 2, 1024) 0 add_11[0][0]
__________________________________________________________________________________________________
res4e_branch2a (Conv2D) (None, 2, 2, 256) 262400 activation_64[0][0]
__________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizati (None, 2, 2, 256) 1024 res4e_branch2a[0][0]
__________________________________________________________________________________________________
activation_65 (Activation) (None, 2, 2, 256) 0 bn4e_branch2a[0][0]
__________________________________________________________________________________________________
res4e_branch2b (Conv2D) (None, 2, 2, 256) 590080 activation_65[0][0]
__________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizati (None, 2, 2, 256) 1024 res4e_branch2b[0][0]
__________________________________________________________________________________________________
activation_66 (Activation) (None, 2, 2, 256) 0 bn4e_branch2b[0][0]
__________________________________________________________________________________________________
res4e_branch2c (Conv2D) (None, 2, 2, 1024) 263168 activation_66[0][0]
__________________________________________________________________________________________________
bn4e_branch2c (BatchNormalizati (None, 2, 2, 1024) 4096 res4e_branch2c[0][0]
__________________________________________________________________________________________________
add_12 (Add) (None, 2, 2, 1024) 0 bn4e_branch2c[0][0]
activation_64[0][0]
__________________________________________________________________________________________________
activation_67 (Activation) (None, 2, 2, 1024) 0 add_12[0][0]
__________________________________________________________________________________________________
res4f_branch2a (Conv2D) (None, 2, 2, 256) 262400 activation_67[0][0]
__________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizati (None, 2, 2, 256) 1024 res4f_branch2a[0][0]
__________________________________________________________________________________________________
activation_68 (Activation) (None, 2, 2, 256) 0 bn4f_branch2a[0][0]
__________________________________________________________________________________________________
res4f_branch2b (Conv2D) (None, 2, 2, 256) 590080 activation_68[0][0]
__________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizati (None, 2, 2, 256) 1024 res4f_branch2b[0][0]
__________________________________________________________________________________________________
activation_69 (Activation) (None, 2, 2, 256) 0 bn4f_branch2b[0][0]
__________________________________________________________________________________________________
res4f_branch2c (Conv2D) (None, 2, 2, 1024) 263168 activation_69[0][0]
__________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizati (None, 2, 2, 1024) 4096 res4f_branch2c[0][0]
__________________________________________________________________________________________________
add_13 (Add) (None, 2, 2, 1024) 0 bn4f_branch2c[0][0]
activation_67[0][0]
__________________________________________________________________________________________________
activation_70 (Activation) (None, 2, 2, 1024) 0 add_13[0][0]
__________________________________________________________________________________________________
res5a_branch2a (Conv2D) (None, 1, 1, 512) 524800 activation_70[0][0]
__________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizati (None, 1, 1, 512) 2048 res5a_branch2a[0][0]
__________________________________________________________________________________________________
activation_71 (Activation) (None, 1, 1, 512) 0 bn5a_branch2a[0][0]
__________________________________________________________________________________________________
res5a_branch2b (Conv2D) (None, 1, 1, 512) 2359808 activation_71[0][0]
__________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizati (None, 1, 1, 512) 2048 res5a_branch2b[0][0]
__________________________________________________________________________________________________
activation_72 (Activation) (None, 1, 1, 512) 0 bn5a_branch2b[0][0]
__________________________________________________________________________________________________
res5a_branch2c (Conv2D) (None, 1, 1, 2048) 1050624 activation_72[0][0]
__________________________________________________________________________________________________
res5a_branch1 (Conv2D) (None, 1, 1, 2048) 2099200 activation_70[0][0]
__________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizati (None, 1, 1, 2048) 8192 res5a_branch2c[0][0]
__________________________________________________________________________________________________
bn5a_branch1 (BatchNormalizatio (None, 1, 1, 2048) 8192 res5a_branch1[0][0]
__________________________________________________________________________________________________
add_14 (Add) (None, 1, 1, 2048) 0 bn5a_branch2c[0][0]
bn5a_branch1[0][0]
__________________________________________________________________________________________________
activation_73 (Activation) (None, 1, 1, 2048) 0 add_14[0][0]
__________________________________________________________________________________________________
res5b_branch2a (Conv2D) (None, 1, 1, 512) 1049088 activation_73[0][0]
__________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizati (None, 1, 1, 512) 2048 res5b_branch2a[0][0]
__________________________________________________________________________________________________
activation_74 (Activation) (None, 1, 1, 512) 0 bn5b_branch2a[0][0]
__________________________________________________________________________________________________
res5b_branch2b (Conv2D) (None, 1, 1, 512) 2359808 activation_74[0][0]
__________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizati (None, 1, 1, 512) 2048 res5b_branch2b[0][0]
__________________________________________________________________________________________________
activation_75 (Activation) (None, 1, 1, 512) 0 bn5b_branch2b[0][0]
__________________________________________________________________________________________________
res5b_branch2c (Conv2D) (None, 1, 1, 2048) 1050624 activation_75[0][0]
__________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizati (None, 1, 1, 2048) 8192 res5b_branch2c[0][0]
__________________________________________________________________________________________________
add_15 (Add) (None, 1, 1, 2048) 0 bn5b_branch2c[0][0]
activation_73[0][0]
__________________________________________________________________________________________________
activation_76 (Activation) (None, 1, 1, 2048) 0 add_15[0][0]
__________________________________________________________________________________________________
res5c_branch2a (Conv2D) (None, 1, 1, 512) 1049088 activation_76[0][0]
__________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizati (None, 1, 1, 512) 2048 res5c_branch2a[0][0]
__________________________________________________________________________________________________
activation_77 (Activation) (None, 1, 1, 512) 0 bn5c_branch2a[0][0]
__________________________________________________________________________________________________
res5c_branch2b (Conv2D) (None, 1, 1, 512) 2359808 activation_77[0][0]
__________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizati (None, 1, 1, 512) 2048 res5c_branch2b[0][0]
__________________________________________________________________________________________________
activation_78 (Activation) (None, 1, 1, 512) 0 bn5c_branch2b[0][0]
__________________________________________________________________________________________________
res5c_branch2c (Conv2D) (None, 1, 1, 2048) 1050624 activation_78[0][0]
__________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizati (None, 1, 1, 2048) 8192 res5c_branch2c[0][0]
__________________________________________________________________________________________________
add_16 (Add) (None, 1, 1, 2048) 0 bn5c_branch2c[0][0]
activation_76[0][0]
__________________________________________________________________________________________________
activation_79 (Activation) (None, 1, 1, 2048) 0 add_16[0][0]
==================================================================================================
Total params: 23,587,712
Trainable params: 23,534,592
Non-trainable params: 53,120
__________________________________________________________________________________________________
```
In [27]:
```py
for layer in resnet.layers:
layer.trainable = False
```
In [28]:
```py
resnet_model = Sequential()
resnet_model.add(resnet)
resnet_model.add(Flatten())
resnet_model.add(Dense(256, activation = 'relu'))
resnet_model.add(BatchNormalization())
resnet_model.add(Dropout(0.5))
resnet_model.add(Dense(128, activation = 'relu'))
resnet_model.add(BatchNormalization())
resnet_model.add(Dropout(0.5))
resnet_model.add(Dense(1, activation = 'sigmoid'))
resnet_model.summary()
```
```
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
resnet50 (Model) (None, 1, 1, 2048) 23587712
_________________________________________________________________
flatten_7 (Flatten) (None, 2048) 0
_________________________________________________________________
dense_19 (Dense) (None, 256) 524544
_________________________________________________________________
batch_normalization_43 (Batc (None, 256) 1024
_________________________________________________________________
dropout_28 (Dropout) (None, 256) 0
_________________________________________________________________
dense_20 (Dense) (None, 128) 32896
_________________________________________________________________
batch_normalization_44 (Batc (None, 128) 512
_________________________________________________________________
dropout_29 (Dropout) (None, 128) 0
_________________________________________________________________
dense_21 (Dense) (None, 1) 129
=================================================================
Total params: 24,146,817
Trainable params: 558,337
Non-trainable params: 23,588,480
_________________________________________________________________
```
In [29]:
```py
#compile the model
resnet_model.compile(loss = 'binary_crossentropy', optimizer = optim, metrics = ['accuracy'])
```
OutputIn [30]:
```py
#fit the model on our data
resnet_history = resnet_model.fit(X, y,
batch_size = 64,
epochs = 500,
verbose = 1)
```
```
Epoch 1/500
17500/17500 [==============================] - 10s 574us/step - loss: 0.1821 - acc: 0.9312
Epoch 2/500
17500/17500 [==============================] - 5s 298us/step - loss: 0.1203 - acc: 0.9552
Epoch 3/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0996 - acc: 0.9625
Epoch 4/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0987 - acc: 0.9617
Epoch 5/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0914 - acc: 0.9627
Epoch 6/500
17500/17500 [==============================] - 5s 299us/step - loss: 0.0881 - acc: 0.9666
Epoch 7/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0886 - acc: 0.9674
Epoch 8/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0824 - acc: 0.9683
Epoch 9/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0769 - acc: 0.9713
Epoch 10/500
17500/17500 [==============================] - 5s 293us/step - loss: 0.0718 - acc: 0.9721
Epoch 11/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0688 - acc: 0.9737
Epoch 12/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0660 - acc: 0.9747
Epoch 13/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0658 - acc: 0.9750
Epoch 14/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0670 - acc: 0.9743
Epoch 15/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0645 - acc: 0.9766
Epoch 16/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0617 - acc: 0.9771
Epoch 17/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0586 - acc: 0.9785
Epoch 18/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0560 - acc: 0.9789
Epoch 19/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0584 - acc: 0.9782
Epoch 20/500
17500/17500 [==============================] - 5s 293us/step - loss: 0.0611 - acc: 0.9762
Epoch 21/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0542 - acc: 0.9798
Epoch 22/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0596 - acc: 0.9769
Epoch 23/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0545 - acc: 0.9807
Epoch 24/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0498 - acc: 0.9818
Epoch 25/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0532 - acc: 0.9797
Epoch 26/500
17500/17500 [==============================] - 5s 301us/step - loss: 0.0511 - acc: 0.9816
Epoch 27/500
17500/17500 [==============================] - 6s 326us/step - loss: 0.0536 - acc: 0.9803
Epoch 28/500
17500/17500 [==============================] - 5s 312us/step - loss: 0.0502 - acc: 0.9813
Epoch 29/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0491 - acc: 0.9823
Epoch 30/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0485 - acc: 0.9818
Epoch 31/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0461 - acc: 0.9831
Epoch 32/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0423 - acc: 0.9842
Epoch 33/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0476 - acc: 0.9832
Epoch 34/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0431 - acc: 0.9836
Epoch 35/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0428 - acc: 0.9842
Epoch 36/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0466 - acc: 0.9827
Epoch 37/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0439 - acc: 0.9844
Epoch 38/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0403 - acc: 0.9850
Epoch 39/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0407 - acc: 0.9847
Epoch 40/500
17500/17500 [==============================] - 5s 297us/step - loss: 0.0430 - acc: 0.9846
Epoch 41/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0402 - acc: 0.9855
Epoch 42/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0366 - acc: 0.9869
Epoch 43/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0399 - acc: 0.9857
Epoch 44/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0408 - acc: 0.9838
Epoch 45/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0391 - acc: 0.9856
Epoch 46/500
17500/17500 [==============================] - 6s 319us/step - loss: 0.0378 - acc: 0.9862
Epoch 47/500
17500/17500 [==============================] - 5s 301us/step - loss: 0.0400 - acc: 0.9851
Epoch 48/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0375 - acc: 0.9865
Epoch 49/500
17500/17500 [==============================] - 5s 293us/step - loss: 0.0368 - acc: 0.9874
Epoch 50/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0420 - acc: 0.9849
Epoch 51/500
17500/17500 [==============================] - 5s 297us/step - loss: 0.0394 - acc: 0.9851
Epoch 52/500
17500/17500 [==============================] - 5s 297us/step - loss: 0.0363 - acc: 0.9873
Epoch 53/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0351 - acc: 0.9863
Epoch 54/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0323 - acc: 0.9881
Epoch 55/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0395 - acc: 0.9861
Epoch 56/500
17500/17500 [==============================] - 5s 299us/step - loss: 0.0378 - acc: 0.9872
Epoch 57/500
17500/17500 [==============================] - 5s 297us/step - loss: 0.0376 - acc: 0.9866
Epoch 58/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0332 - acc: 0.9883
Epoch 59/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0351 - acc: 0.9881
Epoch 60/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0341 - acc: 0.9888
Epoch 61/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0381 - acc: 0.9850
Epoch 62/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0372 - acc: 0.9881
Epoch 63/500
17500/17500 [==============================] - 5s 297us/step - loss: 0.0328 - acc: 0.9874
Epoch 64/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0323 - acc: 0.9883
Epoch 65/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0340 - acc: 0.9878
Epoch 66/500
17500/17500 [==============================] - 5s 297us/step - loss: 0.0295 - acc: 0.9891
Epoch 67/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0342 - acc: 0.9878
Epoch 68/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0318 - acc: 0.9885
Epoch 69/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0345 - acc: 0.9873
Epoch 70/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0301 - acc: 0.9895
Epoch 71/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0321 - acc: 0.9880
Epoch 72/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0295 - acc: 0.9894
Epoch 73/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0318 - acc: 0.9883
Epoch 74/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0308 - acc: 0.9890
Epoch 75/500
17500/17500 [==============================] - 5s 298us/step - loss: 0.0296 - acc: 0.9898
Epoch 76/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0274 - acc: 0.9899
Epoch 77/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0292 - acc: 0.9902
Epoch 78/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0267 - acc: 0.9902
Epoch 79/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0295 - acc: 0.9897
Epoch 80/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0309 - acc: 0.9894
Epoch 81/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0294 - acc: 0.9901
Epoch 82/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0276 - acc: 0.9905
Epoch 83/500
17500/17500 [==============================] - 5s 299us/step - loss: 0.0308 - acc: 0.9891
Epoch 84/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0263 - acc: 0.9903
Epoch 85/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0292 - acc: 0.9899
Epoch 86/500
17500/17500 [==============================] - 5s 307us/step - loss: 0.0277 - acc: 0.9895
Epoch 87/500
17500/17500 [==============================] - 6s 325us/step - loss: 0.0271 - acc: 0.9901
Epoch 88/500
17500/17500 [==============================] - 5s 306us/step - loss: 0.0263 - acc: 0.9910
Epoch 89/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0280 - acc: 0.9900
Epoch 90/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0280 - acc: 0.9895
Epoch 91/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0262 - acc: 0.9907
Epoch 92/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0290 - acc: 0.9902
Epoch 93/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0278 - acc: 0.9899
Epoch 94/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0264 - acc: 0.9902
Epoch 95/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0268 - acc: 0.9905
Epoch 96/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0266 - acc: 0.9908
Epoch 97/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0299 - acc: 0.9896
Epoch 98/500
17500/17500 [==============================] - 5s 298us/step - loss: 0.0242 - acc: 0.9924
Epoch 99/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0305 - acc: 0.9905
Epoch 100/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0224 - acc: 0.9920
Epoch 101/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0268 - acc: 0.9901
Epoch 102/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0269 - acc: 0.9899
Epoch 103/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0261 - acc: 0.9916
Epoch 104/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0245 - acc: 0.9914
Epoch 105/500
17500/17500 [==============================] - 6s 325us/step - loss: 0.0243 - acc: 0.9916
Epoch 106/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0239 - acc: 0.9911
Epoch 107/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0211 - acc: 0.9924
Epoch 108/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0250 - acc: 0.9908
Epoch 109/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0225 - acc: 0.9919
Epoch 110/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0283 - acc: 0.9908
Epoch 111/500
17500/17500 [==============================] - 5s 293us/step - loss: 0.0264 - acc: 0.9906
Epoch 112/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0244 - acc: 0.9921
Epoch 113/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0251 - acc: 0.9917
Epoch 114/500
17500/17500 [==============================] - 5s 297us/step - loss: 0.0196 - acc: 0.9936
Epoch 115/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0235 - acc: 0.9916
Epoch 116/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0221 - acc: 0.9921
Epoch 117/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0237 - acc: 0.9910
Epoch 118/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0222 - acc: 0.9909
Epoch 119/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0226 - acc: 0.9925
Epoch 120/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0205 - acc: 0.9930
Epoch 121/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0219 - acc: 0.9922
Epoch 122/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0243 - acc: 0.9910
Epoch 123/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0238 - acc: 0.9915
Epoch 124/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0213 - acc: 0.9919
Epoch 125/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0238 - acc: 0.9914
Epoch 126/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0214 - acc: 0.9926
Epoch 127/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0218 - acc: 0.9925
Epoch 128/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0243 - acc: 0.9915
Epoch 129/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0217 - acc: 0.9921
Epoch 130/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0244 - acc: 0.9916
Epoch 131/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0232 - acc: 0.9922
Epoch 132/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0210 - acc: 0.9928
Epoch 133/500
17500/17500 [==============================] - 5s 296us/step - loss: 0.0210 - acc: 0.9925
Epoch 134/500
17500/17500 [==============================] - 5s 294us/step - loss: 0.0230 - acc: 0.9917
Epoch 135/500
17500/17500 [==============================] - 5s 295us/step - loss: 0.0209 - acc: 0.9927
Epoch 136/500
1984/17500 [==>...........................] - ETA: 4s - loss: 0.0189 - acc: 0.9940
```
In [31]:
```py
resnet_predictions = resnet_model.predict(X_test)
```
## MAKING SUBMISSIONS
1. Ensemble Model
In [32]:
```py
#making predictions
prediction = np.hstack([p.reshape(-1,1) for p in prediction_scores.values()]) #taking the scores of all the trained models
predictions_ensemble = np.mean(prediction, axis = 1)
print(predictions_ensemble.shape)
```
```
(4000,)
```
In [33]:
```py
df_ensemble = pd.DataFrame(predictions_ensemble, columns = ['has_cactus'])
df_ensemble['has_cactus'] = df_ensemble['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)
```
In [34]:
```py
df_ensemble['id'] = ''
cols = df_ensemble.columns.tolist()
cols = cols[-1:] + cols[:-1]
df_ensemble = df_ensemble[cols]
for i, img in enumerate(test_images):
df_ensemble.set_value(i,'id',img)
#making submission
df_ensemble.to_csv('ensemble_submission.csv',index = False)
```
```
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead
import sys
```
1. VGG16
In [35]:
```py
df_vgg = pd.DataFrame(predictions_vgg, columns = ['has_cactus'])
df_vgg['has_cactus'] = df_vgg['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)
```
In [36]:
```py
df_vgg['id'] = ''
cols = df_vgg.columns.tolist()
cols = cols[-1:] + cols[:-1]
df_vgg = df_vgg[cols]
for i, img in enumerate(test_images):
df_vgg.set_value(i,'id',img)
#making submission
df_vgg.to_csv('vgg_submission.csv',index = False)
```
```
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead
import sys
```
In [37]:
```py
df_vgg.head()
```
Out[37]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | c662bde123f0f83b3caae0ffda237a93.jpg | 1 |
| --- | --- | --- |
| 1 | 9553eed7793d4cf88b5226d446d93dae.jpg | 0 |
| --- | --- | --- |
| 2 | 19f059a7ce41b25be1548bc4049b45ec.jpg | 1 |
| --- | --- | --- |
| 3 | fb4f464486f4894330273346ce939252.jpg | 1 |
| --- | --- | --- |
| 4 | b52558a522db6ec2501ae188b6d6e526.jpg | 1 |
| --- | --- | --- |
1. Resnet50
In [38]:
```py
df_resnet = pd.DataFrame(resnet_predictions, columns = ['has_cactus'])
df_resnet['has_cactus'] = df_resnet['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)
```
In [39]:
```py
df_resnet['id'] = ''
cols = df_resnet.columns.tolist()
cols = cols[-1:] + cols[:-1]
df_resnet = df_resnet[cols]
for i, img in enumerate(test_images):
df_resnet.set_value(i,'id',img)
#making submission
df_resnet.to_csv('resnet_submission.csv',index = False)
```
```
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead
import sys
```
1. Ensemble and VGG16
In [40]:
```py
df_vgg1 = pd.DataFrame(predictions_vgg, columns = ['has_cactus'])
df_ensemble1 = pd.DataFrame(predictions_ensemble, columns = ['has_cactus'])
df_t = 0.5 * df_vgg1['has_cactus'] + 0.5 * df_ensemble1['has_cactus']
df_t = pd.DataFrame(df_t, columns = ['has_cactus'])
df_t['has_cactus'] = df_t['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)
df_t['id'] = ''
cols = df_t.columns.tolist()
cols = cols[-1:] + cols[:-1]
df_t = df_t[cols]
for i, img in enumerate(test_images):
df_t.set_value(i,'id',img)
#making submission
df_t.to_csv('vgg_ensemble_submission.csv',index = False)
```
```
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:14: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead
```
1. Ensemble, VGG16 and ResNet50
In [41]:
```py
df_vgg2 = pd.DataFrame(predictions_vgg, columns = ['has_cactus'])
df_ensemble2 = pd.DataFrame(predictions_ensemble, columns = ['has_cactus'])
df_resnet2 = pd.DataFrame(resnet_predictions, columns = ['has_cactus'])
df_t2 = 0.45 * df_vgg2['has_cactus'] + 0.45 * df_ensemble2['has_cactus'] + 0.10 * df_resnet2['has_cactus']
df_t2 = pd.DataFrame(df_t2, columns = ['has_cactus'])
df_t2['has_cactus'] = df_t2['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)
df_t2['id'] = ''
cols = df_t2.columns.tolist()
cols = cols[-1:] + cols[:-1]
df_t2 = df_t2[cols]
for i, img in enumerate(test_images):
df_t2.set_value(i,'id',img)
#making submission
df_t2.to_csv('vgg_ensemble_resnet_submission.csv',index = False)
```
```
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:15: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead
from ipykernel import kernelapp as app
```
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/cactus-identification-fastai-v1-0-46-ensemble.md
================================================
# Cactus Identification fastai v1.0.46 ensemble
> Author: https://www.kaggle.com/mnpinto
> From: https://www.kaggle.com/mnpinto/cactus-identification-fastai-v1-0-46-ensemble
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
> Score: 0.9996
# Cactus Identification fastai baseline
In [1]:
```py
import fastai
from fastai.vision import *
from sklearn.model_selection import KFold
```
In [2]:
```py
# Copy pretrained model weights to the default path
!mkdir '/tmp/.torch'
!mkdir '/tmp/.torch/models/'
#!cp '../input/resnet18/resnet18.pth' '/tmp/.torch/models/resnet18-5c106cde.pth'
#!cp '../input/densenet121/densenet121.pth' '/tmp/.torch/models/densenet121-a639ec97.pth'
!cp '../input/densenet201/densenet201.pth' '/tmp/.torch/models/densenet201-c1103571.pth'
```
In [3]:
```py
fastai.__version__
```
Out[3]:
```
'1.0.46'
```
In [4]:
```py
data_path = Path('../input/aerial-cactus-identification')
df = pd.read_csv(data_path/'train.csv')
df.head()
```
Out[4]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |
| --- | --- | --- |
| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |
| --- | --- | --- |
| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |
| --- | --- | --- |
| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |
| --- | --- | --- |
| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |
| --- | --- | --- |
In [5]:
```py
sub_csv = pd.read_csv(data_path/'sample_submission.csv')
sub_csv.head()
```
Out[5]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 000940378805c44108d287872b2f04ce.jpg | 0.5 |
| --- | --- | --- |
| 1 | 0017242f54ececa4512b4d7937d1e21e.jpg | 0.5 |
| --- | --- | --- |
| 2 | 001ee6d8564003107853118ab87df407.jpg | 0.5 |
| --- | --- | --- |
| 3 | 002e175c3c1e060769475f52182583d0.jpg | 0.5 |
| --- | --- | --- |
| 4 | 0036e44a7e8f7218e9bc7bf8137e4943.jpg | 0.5 |
| --- | --- | --- |
In [6]:
```py
def create_databunch(valid_idx):
test = ImageList.from_df(sub_csv, path=data_path/'test', folder='test')
data = (ImageList.from_df(df, path=data_path/'train', folder='train')
.split_by_idx(valid_idx)
.label_from_df()
.add_test(test)
.transform(get_transforms(flip_vert=True, max_rotate=20.0), size=128)
.databunch(path='.', bs=64)
.normalize(imagenet_stats)
)
return data
```
**5 fold ensemble**
In [7]:
```py
kf = KFold(n_splits=5, random_state=379)
epochs = 6
lr = 1e-2
preds = []
for train_idx, valid_idx in kf.split(df):
data = create_databunch(valid_idx)
learn = create_cnn(data, models.densenet201, metrics=[accuracy])
learn.fit_one_cycle(epochs, slice(lr))
learn.unfreeze()
learn.fit_one_cycle(epochs, slice(lr/400, lr/4))
learn.fit_one_cycle(epochs, slice(lr/800, lr/8))
preds.append(learn.get_preds(ds_type=DatasetType.Test))
```
Total time: 06:07
| epoch | train_loss | valid_loss | accuracy | time |
| --- | --- | --- | --- | --- |
| 1 | 0.056767 | 0.018588 | 0.994000 | 01:08 |
| 2 | 0.022490 | 0.010378 | 0.995429 | 01:00 |
| 3 | 0.025892 | 0.003561 | 0.998857 | 01:00 |
| 4 | 0.012079 | 0.038481 | 0.996571 | 00:59 |
| 5 | 0.006535 | 0.002324 | 0.999143 | 00:57 |
| 6 | 0.003622 | 0.002164 | 0.999143 | 01:01 |
66.67% [4/6 04:47<02:23]
| epoch | train_loss | valid_loss | accuracy | time |
| --- | --- | --- | --- | --- |
| 1 | 0.007291 | 0.003572 | 0.998286 | 01:12 |
| 2 | 0.014519 | 0.005793 | 0.998286 | 01:11 |
| 3 | 0.008674 | 0.003707 | 0.998286 | 01:10 |
| 4 | 0.007575 | 0.001763 | 0.999143 | 01:13 |
91.28% [199/218 00:58<00:05 0.0037]In [8]:
```py
ens = torch.cat([preds[i][0][:,1].view(-1, 1) for i in range(5)], dim=1)
ens = (ens.mean(1)>0.5).long(); ens[:10]
```
Out[8]:
```
tensor([1, 1, 0, 0, 1, 1, 1, 1, 1, 0])
```
In [9]:
```py
sub_csv['has_cactus'] = ens
```
In [10]:
```py
sub_csv.to_csv('submission.csv', index=False)
```
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/cactus-identification-with-pytorch.md
================================================
# Cactus Identification with Pytorch
> Author: https://www.kaggle.com/nelsongriffiths
> From: https://www.kaggle.com/nelsongriffiths/cactus-identification-with-pytorch
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
In [1]:
```py
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.model_selection import train_test_split
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
import torchvision.transforms as transforms
from torch.utils.data.sampler import SubsetRandomSampler
import cv2
import matplotlib.pyplot as plt
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
```
```
CUDA is available! Training on GPU ...
```
# Data Prep
First, I am going to import our data sources and take a look at what we are working with. We have a csv file that contains our target variable and a folder with our cactus images.
In [2]:
```py
df = pd.read_csv('../input/train.csv')
df.head()
```
Out[2]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |
| --- | --- | --- |
| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |
| --- | --- | --- |
| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |
| --- | --- | --- |
| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |
| --- | --- | --- |
| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |
| --- | --- | --- |
In [3]:
```py
df['has_cactus'].value_counts(normalize=True)
```
Out[3]:
```
1 0.750629
0 0.249371
Name: has_cactus, dtype: float64
```
In [4]:
```py
train_df, val_df = train_test_split(df, stratify = df.has_cactus, test_size=.2)
```
In [5]:
```py
#Checking that validation set has same proportions as original training data
val_df['has_cactus'].value_counts(normalize=True)
```
Out[5]:
```
1 0.750571
0 0.249429
Name: has_cactus, dtype: float64
```
In [6]:
```py
#Build a class for our data to put our images and target variables into our pytorch dataloader
# https://stanford.edu/~shervine/blog/pytorch-how-to-generate-data-parallel
class DataSet(torch.utils.data.Dataset):
def __init__(self, labels, data_directory, transform=None):
super().__init__()
self.labels = labels.values
self.data_dir = data_directory
self.transform=transform
def __len__(self):
return len(self.labels)
def __getitem__(self, index):
name, label = self.labels[index]
img_path = os.path.join(self.data_dir, name)
img = cv2.imread(img_path)
if self.transform is not None:
img = self.transform(img)
return img, label
```
In [7]:
```py
batch_size = 32
# Transform training data with random flips and normalize it to prepare it for dataloader
train_transforms = transforms.Compose([transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
val_transforms = transforms.Compose([transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
train_data = DataSet(train_df,'../input/train/train', transform = train_transforms)
val_data = DataSet(val_df,'../input/train/train', transform = val_transforms)
train_data_loader = torch.utils.data.DataLoader(train_data, batch_size = batch_size, shuffle = True)
val_data_loader = torch.utils.data.DataLoader(train_data, batch_size = batch_size, shuffle = True)
```
In [8]:
```py
#Checking what our cactus look like
fig,ax = plt.subplots(1,3,figsize=(15,5))
for i, idx in enumerate(train_df[train_df['has_cactus']==1]['id'][0:3]):
path = os.path.join('../input/train/train',idx)
ax[i].imshow(cv2.imread(path))
```
In [9]:
```py
#Building a CNN from scratch
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, 3, padding = 1)
self.conv2 = nn.Conv2d(16, 32, 3, padding = 1)
self.conv3 = nn.Conv2d(32, 64, 3, padding = 1)
self.conv4 = nn.Conv2d(64, 128, 3, padding = 1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(2*16*16, 256)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, 64)
self.fc4 = nn.Linear(64, 2)
self.dropout = nn.Dropout(p = .25)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3(x)))
x = self.pool(F.relu(self.conv4(x)))
x = x.view(-1, 2*16*16)
x = self.dropout(x)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = F.relu(self.fc2(x))
x = self.dropout(x)
x = F.relu(self.fc3(x))
x = self.dropout(x)
x = F.relu(self.fc4(x))
return x
```
In [10]:
```py
model = Net()
if train_on_gpu:
model = model.cuda()
epochs = 30
learning_rate = .003
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adamax(model.parameters(), lr=learning_rate)
```
In [11]:
```py
#Training and validation for model
best_loss = np.Inf
best_model = Net()
if train_on_gpu:
best_model.cuda()
for epoch in range(1, epochs+1):
train_loss = 0
val_loss = 0
model.train()
for images, labels in train_data_loader:
if train_on_gpu:
images = images.cuda()
labels = labels.cuda()
optimizer.zero_grad()
out = model(images)
loss = criterion(out, labels)
loss.backward()
optimizer.step()
train_loss += loss.item()
#print('Loss: {}'.format(loss.item()))
model.eval()
for images, labels in val_data_loader:
if train_on_gpu:
images = images.cuda()
labels = labels.cuda()
out = model(images)
loss = criterion(out, labels)
val_loss += loss.item()
train_loss = train_loss/len(train_data_loader.dataset)
val_loss = val_loss/len(val_data_loader.dataset)
print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(epoch, train_loss, val_loss))
#Saving the weights of the best model according to validation score
if val_loss < best_loss:
print('Improved Model Score - Updating Best Model Parameters...')
best_model.load_state_dict(model.state_dict())
```
```
Epoch: 1 Training Loss: 0.007002 Validation Loss: 0.003353
Improved Model Score - Updating Best Model Parameters...
Epoch: 2 Training Loss: 0.003053 Validation Loss: 0.002155
Improved Model Score - Updating Best Model Parameters...
Epoch: 3 Training Loss: 0.002027 Validation Loss: 0.001604
Improved Model Score - Updating Best Model Parameters...
Epoch: 4 Training Loss: 0.001602 Validation Loss: 0.001545
Improved Model Score - Updating Best Model Parameters...
Epoch: 5 Training Loss: 0.001286 Validation Loss: 0.001005
Improved Model Score - Updating Best Model Parameters...
Epoch: 6 Training Loss: 0.001038 Validation Loss: 0.001039
Improved Model Score - Updating Best Model Parameters...
Epoch: 7 Training Loss: 0.000975 Validation Loss: 0.001050
Improved Model Score - Updating Best Model Parameters...
Epoch: 8 Training Loss: 0.000826 Validation Loss: 0.000844
Improved Model Score - Updating Best Model Parameters...
Epoch: 9 Training Loss: 0.000768 Validation Loss: 0.000574
Improved Model Score - Updating Best Model Parameters...
Epoch: 10 Training Loss: 0.000666 Validation Loss: 0.000445
Improved Model Score - Updating Best Model Parameters...
Epoch: 11 Training Loss: 0.000604 Validation Loss: 0.000394
Improved Model Score - Updating Best Model Parameters...
Epoch: 12 Training Loss: 0.000607 Validation Loss: 0.000716
Improved Model Score - Updating Best Model Parameters...
Epoch: 13 Training Loss: 0.000459 Validation Loss: 0.000416
Improved Model Score - Updating Best Model Parameters...
Epoch: 14 Training Loss: 0.000544 Validation Loss: 0.000305
Improved Model Score - Updating Best Model Parameters...
Epoch: 15 Training Loss: 0.000390 Validation Loss: 0.000316
Improved Model Score - Updating Best Model Parameters...
Epoch: 16 Training Loss: 0.000345 Validation Loss: 0.000183
Improved Model Score - Updating Best Model Parameters...
Epoch: 17 Training Loss: 0.000329 Validation Loss: 0.000158
Improved Model Score - Updating Best Model Parameters...
Epoch: 18 Training Loss: 0.000285 Validation Loss: 0.000158
Improved Model Score - Updating Best Model Parameters...
Epoch: 19 Training Loss: 0.000244 Validation Loss: 0.000087
Improved Model Score - Updating Best Model Parameters...
Epoch: 20 Training Loss: 0.000260 Validation Loss: 0.000235
Improved Model Score - Updating Best Model Parameters...
Epoch: 21 Training Loss: 0.000208 Validation Loss: 0.000061
Improved Model Score - Updating Best Model Parameters...
Epoch: 22 Training Loss: 0.000214 Validation Loss: 0.000726
Improved Model Score - Updating Best Model Parameters...
Epoch: 23 Training Loss: 0.000208 Validation Loss: 0.000097
Improved Model Score - Updating Best Model Parameters...
Epoch: 24 Training Loss: 0.000172 Validation Loss: 0.000169
Improved Model Score - Updating Best Model Parameters...
Epoch: 25 Training Loss: 0.000192 Validation Loss: 0.000245
Improved Model Score - Updating Best Model Parameters...
Epoch: 26 Training Loss: 0.000222 Validation Loss: 0.000091
Improved Model Score - Updating Best Model Parameters...
Epoch: 27 Training Loss: 0.000112 Validation Loss: 0.000061
Improved Model Score - Updating Best Model Parameters...
Epoch: 28 Training Loss: 0.000119 Validation Loss: 0.000155
Improved Model Score - Updating Best Model Parameters...
Epoch: 29 Training Loss: 0.000139 Validation Loss: 0.000022
Improved Model Score - Updating Best Model Parameters...
Epoch: 30 Training Loss: 0.000132 Validation Loss: 0.000035
Improved Model Score - Updating Best Model Parameters...
```
In [12]:
```py
#Check model accuracy
best_model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in val_data_loader:
if train_on_gpu:
images = images.cuda()
labels = labels.cuda()
outputs = best_model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Test Accuracy: {} %'.format(100 * correct / total))
```
```
Test Accuracy: 99.96428571428571 %
```
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/cnn-model-with-keras.md
================================================
# Simple CNN Model with Keras
> Author: https://www.kaggle.com/frules11
> From: https://www.kaggle.com/frules11/cnn-model-with-keras
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
> Score: 1.0000
In [1]:
```py
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
# Any results you write to the current directory are saved as output.
```
```
['test', 'train', 'train.csv', 'sample_submission.csv']
```
In [2]:
```py
import os
import cv2
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tqdm import tqdm
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization, Dropout
from keras.models import Sequential
from keras.optimizers import Adam
```
```
Using TensorFlow backend.
```
In [3]:
```py
class DataLoader:
def __init__(self, npy_file: str = "npy_data"):
self.npy_file = npy_file
self.csv_name = "../input/train.csv"
self.df = self.read_csv()
self.n_classes = 2
os.makedirs(self.npy_file, exist_ok=True)
def read_csv(self):
df = pd.read_csv(self.csv_name)
return df
def read_data(self, load_from_npy: bool = True, size2resize: tuple = (75, 75), make_gray: bool = True,
save: bool = True, categorical: bool = False, n_classes: int = 2):
x_data = []
y_data = []
if load_from_npy:
try:
x_data = np.load(fr"{self.npy_file}/x_data.npy")
y_data = np.load(fr"{self.npy_file}/y_data.npy")
except FileNotFoundError:
load_from_npy = False
print("NPY files not found!")
pass
if not load_from_npy:
x_data = []
y_data = []
for dir_label in tqdm(self.df.values):
img = cv2.imread(os.path.join("../input", "train/train", dir_label[0]))
if make_gray:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, size2resize)
x_data.append(img)
y_data.append(int(dir_label[1]))
del img
x_data = np.array(x_data)
y_data = np.array(y_data)
if save:
np.save(fr"{self.npy_file}/x_data.npy", x_data)
np.save(fr"{self.npy_file}/y_data.npy", y_data)
if categorical:
y_data = tf.keras.utils.to_categorical(y_data, num_classes=n_classes)
if not categorical:
y_data = y_data.reshape(-1, 1)
if load_from_npy and make_gray:
try:
x_data_2 = [cv2.cvtColor(n, cv2.COLOR_BGR2GRAY) for n in x_data]
x_data = x_data_2
except cv2.error:
pass
if make_gray:
x_data = np.expand_dims(x_data, axis=-1)
return x_data, y_data
def read_test_data(self, load_from_npy: bool = True, size2resize: tuple = (75, 75), make_gray: bool = True,
save: bool = True, categorical: bool = False, n_classes: int = 2):
test_df = pd.read_csv("../input/sample_submission.csv")
x_data = []
y_data = []
if load_from_npy:
try:
x_data = np.load(fr"{self.npy_file}/x_data_test.npy")
y_data = np.load(fr"{self.npy_file}/y_data_test.npy")
except FileNotFoundError:
load_from_npy = False
print("NPY files not found!")
pass
if not load_from_npy:
x_data = []
y_data = []
for dir_label in tqdm(test_df.values):
img = cv2.imread(os.path.join("../input", "test/test", dir_label[0]))
if make_gray:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, size2resize)
x_data.append(img)
y_data.append(int(dir_label[1]))
del img
x_data = np.array(x_data)
y_data = np.array(y_data)
if save:
np.save(fr"{self.npy_file}/x_data_test.npy", x_data)
np.save(fr"{self.npy_file}/y_data_test.npy", y_data)
if categorical:
y_data = tf.keras.utils.to_categorical(y_data, num_classes=n_classes)
if not categorical:
y_data = y_data.reshape(-1, 1)
if load_from_npy and make_gray:
try:
x_data_2 = [cv2.cvtColor(n, cv2.COLOR_BGR2GRAY) for n in x_data]
x_data = x_data_2
except cv2.error:
pass
if make_gray:
x_data = np.expand_dims(x_data, axis=-1)
return x_data, y_data
```
In [4]:
```py
class TrainWithKeras:
def __init__(self, x_data, y_data, lr: float = 0.001, epochs: int = 10, batch_size: int = 32,
loss: str = "categorical_crossentropy", model_path: str = "model.h5"):
self.x_data = x_data
self.y_data = y_data
self.model_path = model_path
self.epochs = epochs
self.batch_size = batch_size
self.optimizer = Adam(lr=lr)
self.loss = loss
def make_model(self, summarize: bool = True):
model = Sequential()
model.add(Conv2D(64, (3, 3), strides=1, activation="relu",
input_shape=(self.x_data.shape[1], self.x_data.shape[2], self.x_data.shape[3])))
model.add(MaxPooling2D())
model.add(Conv2D(128, (3, 3), strides=1, activation="relu"))
model.add(Dropout(0.3))
model.add(BatchNormalization())
model.add(Conv2D(256, (3, 3), strides=1, activation="relu"))
model.add(MaxPooling2D())
model.add(Conv2D(512, (3, 3), strides=1, activation="relu"))
model.add(Dropout(0.3))
model.add(Conv2D(1024, (3, 3), strides=1, activation="relu"))
model.add(Flatten())
model.add(Dense(1024, activation="relu"))
model.add(Dropout(0.3))
model.add(Dense(2, activation="softmax"))
if summarize:
model.summary()
return model
def compile(self, kmodel: Sequential):
kmodel.compile(loss=self.loss, optimizer=self.optimizer, metrics=["acc"])
return kmodel
def train(self, kmodel: Sequential, save: bool = True):
history = kmodel.fit(self.x_data, self.y_data, batch_size=self.batch_size, epochs=self.epochs,
validation_split=0.0)
if save:
kmodel.save(self.model_path)
return history, kmodel
```
In [5]:
```py
class MakeSubmission:
def __init__(self, x_test: np.array, model_path: str, csv_path: str):
self.x_test = x_test
self.model_path = model_path
self.csv_path = csv_path
self.model = tf.keras.models.load_model(self.model_path)
self.df = pd.read_csv(self.csv_path)
preds = self.make_predictions()
submission = pd.DataFrame({'id': self.df['id'], 'has_cactus': preds})
submission.to_csv("sample_submission.csv", index=False)
def make_predictions(self, make_it_ready: bool = True):
preds = self.model.predict(self.x_test)
if make_it_ready:
preds = [np.argmax(n) for n in preds]
return preds
```
In [6]:
```py
os.makedirs("models", exist_ok=True)
dl = DataLoader()
X_data, Y_data = dl.read_data(True, (32, 32), False, True, True, 2)
```
```
0%| | 71/17500 [00:00<00:24, 703.67it/s]
```
```
NPY files not found!
```
```
100%|██████████| 17500/17500 [00:25<00:00, 692.41it/s]
```
In [7]:
```py
trainer = TrainWithKeras(X_data, Y_data, model_path="models/model.h5", epochs=50, batch_size=1024, lr=0.0002)
model = trainer.make_model()
model = trainer.compile(model)
histroy = trainer.train(model)
```
```
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 30, 30, 64) 1792
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 15, 15, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 13, 13, 128) 73856
_________________________________________________________________
dropout_1 (Dropout) (None, 13, 13, 128) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 13, 13, 128) 512
_________________________________________________________________
conv2d_3 (Conv2D) (None, 11, 11, 256) 295168
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 5, 5, 256) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 3, 3, 512) 1180160
_________________________________________________________________
dropout_2 (Dropout) (None, 3, 3, 512) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 1, 1, 1024) 4719616
_________________________________________________________________
flatten_1 (Flatten) (None, 1024) 0
_________________________________________________________________
dense_1 (Dense) (None, 1024) 1049600
_________________________________________________________________
dropout_3 (Dropout) (None, 1024) 0
_________________________________________________________________
dense_2 (Dense) (None, 2) 2050
=================================================================
Total params: 7,322,754
Trainable params: 7,322,498
Non-trainable params: 256
_________________________________________________________________
Epoch 1/50
17500/17500 [==============================] - 5s 291us/step - loss: 0.4455 - acc: 0.7814
Epoch 2/50
17500/17500 [==============================] - 2s 88us/step - loss: 0.1582 - acc: 0.9434
Epoch 3/50
17500/17500 [==============================] - 2s 88us/step - loss: 0.0925 - acc: 0.9665
Epoch 4/50
17500/17500 [==============================] - 2s 88us/step - loss: 0.0686 - acc: 0.9747
Epoch 5/50
17500/17500 [==============================] - 2s 88us/step - loss: 0.0623 - acc: 0.9774
Epoch 6/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0475 - acc: 0.9833
Epoch 7/50
17500/17500 [==============================] - 2s 88us/step - loss: 0.0484 - acc: 0.9819
Epoch 8/50
17500/17500 [==============================] - 2s 88us/step - loss: 0.0393 - acc: 0.9862
Epoch 9/50
17500/17500 [==============================] - 2s 89us/step - loss: 0.0394 - acc: 0.9858
Epoch 10/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0325 - acc: 0.9884
Epoch 11/50
17500/17500 [==============================] - 2s 86us/step - loss: 0.0468 - acc: 0.9848
Epoch 12/50
17500/17500 [==============================] - 1s 85us/step - loss: 0.0314 - acc: 0.9895
Epoch 13/50
17500/17500 [==============================] - 1s 86us/step - loss: 0.0414 - acc: 0.9859
Epoch 14/50
17500/17500 [==============================] - 1s 85us/step - loss: 0.0295 - acc: 0.9893
Epoch 15/50
17500/17500 [==============================] - 1s 86us/step - loss: 0.0203 - acc: 0.9929
Epoch 16/50
17500/17500 [==============================] - 2s 86us/step - loss: 0.0251 - acc: 0.9920
Epoch 17/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0193 - acc: 0.9935
Epoch 18/50
17500/17500 [==============================] - 2s 86us/step - loss: 0.0247 - acc: 0.9914
Epoch 19/50
17500/17500 [==============================] - 2s 86us/step - loss: 0.0179 - acc: 0.9938
Epoch 20/50
17500/17500 [==============================] - 2s 86us/step - loss: 0.0149 - acc: 0.9946
Epoch 21/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0119 - acc: 0.9957
Epoch 22/50
17500/17500 [==============================] - 2s 86us/step - loss: 0.0110 - acc: 0.9963
Epoch 23/50
17500/17500 [==============================] - 1s 85us/step - loss: 0.0120 - acc: 0.9958
Epoch 24/50
17500/17500 [==============================] - 2s 86us/step - loss: 0.0228 - acc: 0.9921
Epoch 25/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0134 - acc: 0.9960
Epoch 26/50
17500/17500 [==============================] - 2s 88us/step - loss: 0.0104 - acc: 0.9963
Epoch 27/50
17500/17500 [==============================] - 2s 86us/step - loss: 0.0136 - acc: 0.9954
Epoch 28/50
17500/17500 [==============================] - 1s 85us/step - loss: 0.0086 - acc: 0.9970
Epoch 29/50
17500/17500 [==============================] - 2s 86us/step - loss: 0.0096 - acc: 0.9967
Epoch 30/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0080 - acc: 0.9971
Epoch 31/50
17500/17500 [==============================] - 1s 86us/step - loss: 0.0204 - acc: 0.9934
Epoch 32/50
17500/17500 [==============================] - 1s 86us/step - loss: 0.0141 - acc: 0.9948
Epoch 33/50
17500/17500 [==============================] - 2s 86us/step - loss: 0.0067 - acc: 0.9974
Epoch 34/50
17500/17500 [==============================] - 1s 85us/step - loss: 0.0070 - acc: 0.9975
Epoch 35/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0129 - acc: 0.9950
Epoch 36/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0072 - acc: 0.9976
Epoch 37/50
17500/17500 [==============================] - 2s 88us/step - loss: 0.0204 - acc: 0.9926
Epoch 38/50
17500/17500 [==============================] - 2s 88us/step - loss: 0.0097 - acc: 0.9964
Epoch 39/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0054 - acc: 0.9983
Epoch 40/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0055 - acc: 0.9979
Epoch 41/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0059 - acc: 0.9978
Epoch 42/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0095 - acc: 0.9965
Epoch 43/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0057 - acc: 0.9979
Epoch 44/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0058 - acc: 0.9983
Epoch 45/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0055 - acc: 0.9982
Epoch 46/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0036 - acc: 0.9986
Epoch 47/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0049 - acc: 0.9983
Epoch 48/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0055 - acc: 0.9981
Epoch 49/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0044 - acc: 0.9984
Epoch 50/50
17500/17500 [==============================] - 2s 87us/step - loss: 0.0031 - acc: 0.9989
```
In [8]:
```py
X_data_test, Y_data_test = dl.read_test_data(True, (32, 32), False, True, False)
ms = MakeSubmission(X_data_test, "models/model.h5", "../input/sample_submission.csv")
```
```
2%|▏ | 62/4000 [00:00<00:06, 614.87it/s]
```
```
NPY files not found!
```
```
100%|██████████| 4000/4000 [00:05<00:00, 669.71it/s]
```
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/data-augmentation-vgg16-cnn.md
================================================
# Data Augmentation + VGG16 + CNN
> Author: https://www.kaggle.com/alperkoc
> From: https://www.kaggle.com/alperkoc/data-augmentation-vgg16-cnn
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
> Score: 0.9995
## Import Packages
In [1]:
```py
import cv2
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
import os
from tqdm import tqdm, tqdm_notebook
from keras.models import Sequential
import keras
from keras.layers import Activation, Dropout, Flatten, Dense,Conv2D,Conv3D,MaxPooling2D,AveragePooling2D,BatchNormalization
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix,classification_report,roc_auc_score
import seaborn as sns
import tensorflow as tf
import matplotlib.image as mpimg
print(os.listdir("../input"))
print(os.listdir("../input/weights/"))
IMAGE_SIZE = 32
```
```
Using TensorFlow backend.
```
```
['aerial-cactus-identification', 'weights']
['vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5']
```
## Set Directories
In [2]:
```py
train_dir = "../input/aerial-cactus-identification/train/train/"
test_dir = "../input/aerial-cactus-identification/test/test/"
train_df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
train_df.head()
```
Out[2]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |
| --- | --- | --- |
| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |
| --- | --- | --- |
| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |
| --- | --- | --- |
| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |
| --- | --- | --- |
| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |
| --- | --- | --- |
## Check out an image sample
In [3]:
```py
im = cv2.imread("../input/aerial-cactus-identification/train/train/01e30c0ba6e91343a12d2126fcafc0dd.jpg")
plt.imshow(im)
```
Out[3]:
```
```
## Read and convert horizontally an vertically augmented images to numpy array
In [4]:
```py
X_tr = []
Y_tr = []
imges = train_df['id'].values
for img_id in tqdm_notebook(imges):
image = np.array(cv2.imread(train_dir + img_id))
X_tr.append(image)
Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])
X_tr.append(np.flip(image))
Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])
X_tr.append(np.flipud(image))
Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])
X_tr.append(np.fliplr(image))
Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])
X_tr = np.asarray(X_tr)
X_tr = X_tr.astype('float32')
X_tr /= 255
Y_tr = np.asarray(Y_tr)
```
# Save an instance of initial images for future use
In [5]:
```py
X_tr_2 = X_tr
Y_tr_2 = Y_tr
```
In [6]:
```py
X_tr = X_tr_2
Y_tr = Y_tr_2
```
In [7]:
```py
X_tr.shape,Y_tr.shape
```
Out[7]:
```
((70000, 32, 32, 3), (70000,))
```
# Read test images
In [8]:
```py
test_image_names = []
for filename in os.listdir(test_dir):
test_image_names.append(filename)
test_image_names.sort()
X_ts = []
#imges = test_df['id'].values
for img_id in tqdm_notebook(test_image_names):
X_ts.append(cv2.imread(test_dir + img_id))
X_ts = np.asarray(X_ts)
X_ts = X_ts.astype('float32')
X_ts /= 255
```
In [9]:
```py
x_train,x_test,y_train,y_test = train_test_split(X_tr, Y_tr, test_size = 0.2 , stratify = Y_tr )
```
# Load weights of pretrained VGG16 model
In [10]:
```py
base=keras.applications.vgg16.VGG16(include_top=False, weights='../input/weights/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',input_shape=(32,32,3))
```
# Train
In [11]:
```py
print("Current train size:",X_tr.shape)
model = Sequential()
model.add(base)
model.add(Flatten())
model.add(Dense(256, use_bias=True))
model.add(BatchNormalization())
model.add(Activation("relu"))
model.add(Dropout(0.5))
model.add(Dense(256,activation='relu'))
model.add(BatchNormalization())
model.add(Dense(16, activation='tanh'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()
callback=[keras.callbacks.EarlyStopping(monitor='val_acc', patience=20, verbose=1, mode='auto', restore_best_weights=True),
keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=1, mode='auto')]
model.fit(X_tr,Y_tr,batch_size=64, epochs=80, verbose=1, validation_split=0.1,callbacks=callback)
```
```
Current train size: (70000, 32, 32, 3)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
vgg16 (Model) (None, 1, 1, 512) 14714688
_________________________________________________________________
flatten_1 (Flatten) (None, 512) 0
_________________________________________________________________
dense_1 (Dense) (None, 256) 131328
_________________________________________________________________
batch_normalization_1 (Batch (None, 256) 1024
_________________________________________________________________
activation_1 (Activation) (None, 256) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_2 (Dense) (None, 256) 65792
_________________________________________________________________
batch_normalization_2 (Batch (None, 256) 1024
_________________________________________________________________
dense_3 (Dense) (None, 16) 4112
_________________________________________________________________
dense_4 (Dense) (None, 1) 17
=================================================================
Total params: 14,917,985
Trainable params: 14,916,961
Non-trainable params: 1,024
_________________________________________________________________
Train on 63000 samples, validate on 7000 samples
Epoch 1/80
63000/63000 [==============================] - 33s 519us/step - loss: 0.0982 - acc: 0.9667 - val_loss: 0.1541 - val_acc: 0.9523
Epoch 2/80
63000/63000 [==============================] - 30s 472us/step - loss: 0.0478 - acc: 0.9842 - val_loss: 0.1781 - val_acc: 0.9426
Epoch 3/80
63000/63000 [==============================] - 30s 471us/step - loss: 0.0353 - acc: 0.9891 - val_loss: 0.0701 - val_acc: 0.9766
Epoch 4/80
63000/63000 [==============================] - 30s 471us/step - loss: 0.0292 - acc: 0.9907 - val_loss: 0.0387 - val_acc: 0.9890
Epoch 5/80
63000/63000 [==============================] - 30s 473us/step - loss: 0.0237 - acc: 0.9925 - val_loss: 0.1911 - val_acc: 0.9254
Epoch 6/80
63000/63000 [==============================] - 30s 470us/step - loss: 0.0195 - acc: 0.9940 - val_loss: 0.3157 - val_acc: 0.8936
Epoch 7/80
63000/63000 [==============================] - 30s 469us/step - loss: 0.0183 - acc: 0.9945 - val_loss: 0.0464 - val_acc: 0.9847
Epoch 8/80
63000/63000 [==============================] - 30s 472us/step - loss: 0.0144 - acc: 0.9955 - val_loss: 0.0153 - val_acc: 0.9950
Epoch 9/80
63000/63000 [==============================] - 30s 468us/step - loss: 0.0122 - acc: 0.9962 - val_loss: 0.0357 - val_acc: 0.9931
Epoch 10/80
63000/63000 [==============================] - 30s 469us/step - loss: 0.0118 - acc: 0.9966 - val_loss: 0.0175 - val_acc: 0.9957
Epoch 11/80
63000/63000 [==============================] - 30s 472us/step - loss: 0.0114 - acc: 0.9969 - val_loss: 0.0346 - val_acc: 0.9924
Epoch 12/80
63000/63000 [==============================] - 30s 473us/step - loss: 0.0095 - acc: 0.9973 - val_loss: 0.0335 - val_acc: 0.9923
Epoch 13/80
63000/63000 [==============================] - 30s 470us/step - loss: 0.0089 - acc: 0.9975 - val_loss: 0.0187 - val_acc: 0.9947
Epoch 14/80
63000/63000 [==============================] - 30s 469us/step - loss: 0.0088 - acc: 0.9976 - val_loss: 0.0195 - val_acc: 0.9954
Epoch 15/80
63000/63000 [==============================] - 30s 469us/step - loss: 0.0078 - acc: 0.9979 - val_loss: 0.0191 - val_acc: 0.9963
Epoch 16/80
63000/63000 [==============================] - 30s 472us/step - loss: 0.0070 - acc: 0.9983 - val_loss: 0.0293 - val_acc: 0.9943
Epoch 17/80
63000/63000 [==============================] - 30s 468us/step - loss: 0.0077 - acc: 0.9980 - val_loss: 0.3062 - val_acc: 0.9326
Epoch 18/80
63000/63000 [==============================] - 30s 469us/step - loss: 0.0054 - acc: 0.9985 - val_loss: 0.1900 - val_acc: 0.9586
Epoch 00018: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.
Epoch 19/80
63000/63000 [==============================] - 30s 472us/step - loss: 0.0016 - acc: 0.9996 - val_loss: 0.0134 - val_acc: 0.9977
Epoch 20/80
63000/63000 [==============================] - 30s 469us/step - loss: 5.7911e-04 - acc: 0.9999 - val_loss: 0.0169 - val_acc: 0.9974
Epoch 21/80
63000/63000 [==============================] - 30s 472us/step - loss: 3.5007e-04 - acc: 0.9999 - val_loss: 0.0155 - val_acc: 0.9981
Epoch 22/80
63000/63000 [==============================] - 30s 476us/step - loss: 5.3868e-04 - acc: 0.9998 - val_loss: 0.0155 - val_acc: 0.9979
Epoch 23/80
63000/63000 [==============================] - 30s 469us/step - loss: 3.6740e-04 - acc: 0.9999 - val_loss: 0.0179 - val_acc: 0.9977
Epoch 24/80
32320/63000 [==============>...............] - ETA: 13s - loss: 1.1531e-04 - acc: 1.0000
```
# Results
In [12]:
```py
clf=model
y_pred_proba = clf.predict_proba(X_tr_2)
y_pred = clf.predict_classes(X_tr_2)
conf_mat = confusion_matrix(Y_tr_2, y_pred)
fig, ax = plt.subplots(figsize=(10,10))
sns.heatmap(conf_mat, annot=True, fmt='d', xticklabels=['0','1'], yticklabels=['0','1'])
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.show()
print(classification_report(Y_tr_2, y_pred, target_names=['0','1']))
print("\n\n AUC: {:<0.4f}".format(roc_auc_score(Y_tr_2,y_pred_proba)))
```
```
precision recall f1-score support
0 1.00 1.00 1.00 17456
1 1.00 1.00 1.00 52544
micro avg 1.00 1.00 1.00 70000
macro avg 1.00 1.00 1.00 70000
weighted avg 1.00 1.00 1.00 70000
AUC: 1.0000
```
# Submit
In [13]:
```py
test_df = pd.read_csv('../input/aerial-cactus-identification/sample_submission.csv')
X_test = []
imges = test_df['id'].values
for img_id in tqdm_notebook(imges):
X_test.append(cv2.imread(test_dir + img_id))
X_test = np.asarray(X_test)
X_test = X_test.astype('float32')
X_test /= 255
y_test_pred = model.predict_proba(X_test)
test_df['has_cactus'] = y_test_pred
test_df.to_csv('tf_learning_vgg16_aug2_80epoch.csv', index=False)
```
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/detecting-cactus-with-kekas.md
================================================
# Detecting cactus with kekas
> Author: https://www.kaggle.com/artgor
> From: https://www.kaggle.com/artgor/detecting-cactus-with-kekas
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
## General information
Researchers in Mexico have created the VIGIA project, aiming to build a system for autonomous surveillance of protected areas. One of the first steps is being able to recognize the vegetation in the area. In this competition we are trying to identify whether there is a cactus in the image.
In this kernel I use kekas ([https://github.com/belskikh/kekas](https://github.com/belskikh/kekas)) as a wrapper for Pytorch.
Most of the code is taken from my other kernel: [https://www.kaggle.com/artgor/cancer-detection-with-kekas](https://www.kaggle.com/artgor/cancer-detection-with-kekas)
CodeIn [1]:
```py
# libraries
import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
import torch
from torch.utils.data import TensorDataset, DataLoader,Dataset
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
import time
from PIL import Image
train_on_gpu = True
from torch.utils.data.sampler import SubsetRandomSampler
from torch.optim.lr_scheduler import StepLR, ReduceLROnPlateau, CosineAnnealingLR
from sklearn.metrics import accuracy_score
import cv2
```
Some of good libraries for DL aren't available in Docker with GPU by default, so it is necessary to install them. (don't forget to turn on internet connection in kernels).
In [2]:
```py
!pip install albumentations > /dev/null 2>&1
!pip install pretrainedmodels > /dev/null 2>&1
!pip install kekas > /dev/null 2>&1
!pip install adabound > /dev/null 2>&1
```
CodeIn [3]:
```py
# more imports
import albumentations
from albumentations import torch as AT
import pretrainedmodels
import adabound
from kekas import Keker, DataOwner, DataKek
from kekas.transformations import Transformer, to_torch, normalize
from kekas.metrics import accuracy
from kekas.modules import Flatten, AdaptiveConcatPool2d
from kekas.callbacks import Callback, Callbacks, DebuggerCallback
from kekas.utils import DotDict
```
```
/opt/conda/lib/python3.6/site-packages/kekas/keker.py:9: UserWarning: Error 'No module named 'apex''' during importing apex library. To use mixed precison you should install it from https://github.com/NVIDIA/apex
warnings.warn(f"Error '{e}'' during importing apex library. To use mixed precison"
```
## Data overview
In [4]:
```py
labels = pd.read_csv('../input/train.csv')
fig = plt.figure(figsize=(25, 8))
train_imgs = os.listdir("../input/train/train")
for idx, img in enumerate(np.random.choice(train_imgs, 20)):
ax = fig.add_subplot(4, 20//4, idx+1, xticks=[], yticks=[])
im = Image.open("../input/train/train/" + img)
plt.imshow(im)
lab = labels.loc[labels['id'] == img, 'has_cactus'].values[0]
ax.set_title(f'Label: {lab}')
```
Images were resized, so I can see almost nothing in them...
Kekas accepts pandas DataFrame as an input and iterates over it to get image names and labels
In [5]:
```py
test_img = os.listdir('../input/test/test')
test_df = pd.DataFrame(test_img, columns=['id'])
test_df['has_cactus'] = -1
test_df['data_type'] = 'test'
labels['has_cactus'] = labels['has_cactus'].astype(int)
labels['data_type'] = 'train'
labels.head()
```
Out[5]:
| | id | has_cactus | data_type |
| --- | --- | --- | --- |
| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 | train |
| --- | --- | --- | --- |
| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 | train |
| --- | --- | --- | --- |
| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 | train |
| --- | --- | --- | --- |
| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 | train |
| --- | --- | --- | --- |
| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 | train |
| --- | --- | --- | --- |
In [6]:
```py
labels.loc[labels['data_type'] == 'train', 'has_cactus'].value_counts()
```
Out[6]:
```
1 13136
0 4364
Name: has_cactus, dtype: int64
```
We have some disbalance in the data, but it isn't too big.
In [7]:
```py
# splitting data into train and validation
train, valid = train_test_split(labels, stratify=labels.has_cactus, test_size=0.2)
```
### Reader function
At first it is necessary to create a reader function, which will open images. It accepts i and row as input (like from pandas iterrows). The function should return a dictionary with image and label. [:,:,::-1] - is a neat trick which converts BGR images to RGB, it works faster that converting to RGB by usual means.
In [8]:
```py
def reader_fn(i, row):
image = cv2.imread(f"../input/{row['data_type']}/{row['data_type']}/{row['id']}")[:,:,::-1] # BGR -> RGB
label = torch.Tensor([row["has_cactus"]])
return {"image": image, "label": label}
```
### Data transformation
Next step is defining data transformations and augmentations. This differs from standard PyTorch way. We define resizing, augmentations and normalizing separately, this allows to easily create separate transformers for train and valid/test data.
At first we define augmentations. We create a function with a list of augmentations (I prefer albumentation library: [https://github.com/albu/albumentations](https://github.com/albu/albumentations))
In [9]:
```py
def augs(p=0.5):
return albumentations.Compose([
albumentations.HorizontalFlip(),
albumentations.VerticalFlip(),
albumentations.RandomBrightness(),
], p=p)
```
Now we create a transforming function. It heavily uses Transformer from kekas.
* The first step is defining resizing. You can change arguments of function if you want images to have different height and width, otherwis you can leave it as it is.
* Next step is defining augmentations. Here we provide the key of image which is defined in reader_fn;
* The third step is defining final transformation to tensor and normalizing;
* After this we can compose separate transformations for train and valid/test data;
In [10]:
```py
def get_transforms(dataset_key, size, p):
PRE_TFMS = Transformer(dataset_key, lambda x: cv2.resize(x, (size, size)))
AUGS = Transformer(dataset_key, lambda x: augs()(image=x)["image"])
NRM_TFMS = transforms.Compose([
Transformer(dataset_key, to_torch()),
Transformer(dataset_key, normalize())
])
train_tfms = transforms.Compose([PRE_TFMS, AUGS, NRM_TFMS])
val_tfms = transforms.Compose([PRE_TFMS, NRM_TFMS])
return train_tfms, val_tfms
```
In [11]:
```py
train_tfms, val_tfms = get_transforms("image", 32, 0.5)
```
Now we can create a DataKek, which is similar to creating dataset in Pytorch. We define the data, reader function and transformation.Then we can define standard PyTorch DataLoader.
In [12]:
```py
train_dk = DataKek(df=train, reader_fn=reader_fn, transforms=train_tfms)
val_dk = DataKek(df=valid, reader_fn=reader_fn, transforms=val_tfms)
batch_size = 64
workers = 0
train_dl = DataLoader(train_dk, batch_size=batch_size, num_workers=workers, shuffle=True, drop_last=True)
val_dl = DataLoader(val_dk, batch_size=batch_size, num_workers=workers, shuffle=False)
```
In [13]:
```py
test_dk = DataKek(df=test_df, reader_fn=reader_fn, transforms=val_tfms)
test_dl = DataLoader(test_dk, batch_size=batch_size, num_workers=workers, shuffle=False)
```
### Building a neural net
Here we define the architecture of the neural net.
* Pre-trained backbone is taken from pretrainedmodels: [https://github.com/Cadene/pretrained-models.pytorch](https://github.com/Cadene/pretrained-models.pytorch) Here I take densenet169
* We also define changes to the architecture. For example, we take off the last layer and add a custom head with nn.Sequential. AdaptiveConcatPool2d is a layer in kekas, which concats AdaptiveMaxPooling and AdaptiveAveragePooling
In [14]:
```py
class Net(nn.Module):
def __init__(
self,
num_classes: int,
p: float = 0.2,
pooling_size: int = 2,
last_conv_size: int = 1664,
arch: str = "densenet169",
pretrained: str = "imagenet") -> None:
"""A simple model to finetune.
Args:
num_classes: the number of target classes, the size of the last layer's output
p: dropout probability
pooling_size: the size of the result feature map after adaptive pooling layer
last_conv_size: size of the flatten last backbone conv layer
arch: the name of the architecture form pretrainedmodels
pretrained: the mode for pretrained model from pretrainedmodels
"""
super().__init__()
net = pretrainedmodels.__dict__[arch](pretrained=pretrained)
modules = list(net.children())[:-1] # delete last layer
# add custom head
modules += [nn.Sequential(
# AdaptiveConcatPool2d is a concat of AdaptiveMaxPooling and AdaptiveAveragePooling
# AdaptiveConcatPool2d(size=pooling_size),
Flatten(),
nn.BatchNorm1d(1664),
nn.Dropout(p),
nn.Linear(1664, num_classes)
)]
self.net = nn.Sequential(*modules)
def forward(self, x):
logits = self.net(x)
return logits
```
The data for training needs to be transformed one more time - we define DataOwner, which contains all the data. For now let's define it for train and valid. Next we define model and loss. As I choose BCEWithLogitsLoss, we can set the number of classes for output to 1.
In [15]:
```py
dataowner = DataOwner(train_dl, val_dl, None)
model = Net(num_classes=1)
criterion = nn.BCEWithLogitsLoss()
```
```
Downloading: "http://data.lip6.fr/cadene/pretrainedmodels/densenet169-f470b90a4.pth" to /tmp/.torch/models/densenet169-f470b90a4.pth
57372314it [00:03, 14671003.61it/s]
```
And now we define what will the model do with the data. For example we could slice the output and take only a part of it. For now we will simply return the output of the model.
In [16]:
```py
def step_fn(model: torch.nn.Module,
batch: torch.Tensor) -> torch.Tensor:
"""Determine what your model will do with your data.
Args:
model: the pytorch module to pass input in
batch: the batch of data from the DataLoader
Returns:
The models forward pass results
"""
inp = batch["image"]
return model(inp)
```
Defining custom metrics
In [17]:
```py
def bce_accuracy(target: torch.Tensor,
preds: torch.Tensor,
thresh: bool = 0.5) -> float:
target = target.cpu().detach().numpy()
preds = (torch.sigmoid(preds).cpu().detach().numpy() > thresh).astype(int)
return accuracy_score(target, preds)
def roc_auc(target: torch.Tensor,
preds: torch.Tensor) -> float:
target = target.cpu().detach().numpy()
preds = torch.sigmoid(preds).cpu().detach().numpy()
return roc_auc_score(target, preds)
```
### Keker
Now we can define the Keker - the core Kekas class for training the model.
Here we define everything which is necessary for training:
* the model which was defined earlier;
* dataowner containing the data for training and validation;
* criterion;
* step function;
* the key of labels, which was defined in the reader function;
* the dictionary with metrics (there can be several of them);
* The optimizer and its parameters;
In [18]:
```py
keker = Keker(model=model,
dataowner=dataowner,
criterion=criterion,
step_fn=step_fn,
target_key="label",
metrics={"acc": bce_accuracy, 'auc': roc_auc},
opt=torch.optim.SGD,
opt_params={"momentum": 0.99})
```
In [19]:
```py
keker.unfreeze(model_attr="net")
layer_num = -1
keker.freeze_to(layer_num, model_attr="net")
```
In [20]:
```py
keker.kek_one_cycle(max_lr=1e-2, # the maximum learning rate
cycle_len=5, # number of epochs, actually, but not exactly
momentum_range=(0.95, 0.85), # range of momentum changes
div_factor=25, # max_lr / min_lr
increase_fraction=0.3, # the part of cycle when learning rate increases
logdir='train_logs')
keker.plot_kek('train_logs')
```
```
Epoch 1/5: 100% 218/218 [00:52<00:00, 4.94it/s, loss=0.0502, val_loss=0.0337, acc=0.9896, auc=0.9995]
Epoch 2/5: 100% 218/218 [00:42<00:00, 6.22it/s, loss=0.0131, val_loss=0.0152, acc=0.9955, auc=0.9999]
Epoch 3/5: 100% 218/218 [00:41<00:00, 6.37it/s, loss=0.0146, val_loss=0.0112, acc=0.9955, auc=1.0000]
Epoch 4/5: 100% 218/218 [00:43<00:00, 5.73it/s, loss=0.0097, val_loss=0.0109, acc=0.9955, auc=1.0000]
Epoch 5/5: 100% 218/218 [00:41<00:00, 6.24it/s, loss=0.0107, val_loss=0.0092, acc=0.9957, auc=1.0000]
```
[](https://plot.ly/)[](https://plot.ly/)[](https://plot.ly/)[](https://plot.ly/)In [21]:
```py
keker.kek_one_cycle(max_lr=1e-3, # the maximum learning rate
cycle_len=5, # number of epochs, actually, but not exactly
momentum_range=(0.95, 0.85), # range of momentum changes
div_factor=25, # max_lr / min_lr
increase_fraction=0.2, # the part of cycle when learning rate increases
logdir='train_logs1')
keker.plot_kek('train_logs1')
```
```
Epoch 1/5: 100% 218/218 [00:41<00:00, 6.29it/s, loss=0.0149, val_loss=0.0089, acc=0.9963, auc=1.0000]
Epoch 2/5: 100% 218/218 [00:41<00:00, 6.34it/s, loss=0.0221, val_loss=0.0098, acc=0.9966, auc=1.0000]
Epoch 3/5: 100% 218/218 [00:42<00:00, 6.39it/s, loss=0.0122, val_loss=0.0094, acc=0.9960, auc=1.0000]
Epoch 4/5: 100% 218/218 [00:41<00:00, 5.93it/s, loss=0.0119, val_loss=0.0087, acc=0.9963, auc=1.0000]
Epoch 5/5: 100% 218/218 [00:41<00:00, 6.48it/s, loss=0.0106, val_loss=0.0086, acc=0.9974, auc=1.0000]
```
[](https://plot.ly/)[](https://plot.ly/)[](https://plot.ly/)[](https://plot.ly/)
### Predicting and TTA
Simply predicting on test data is okay, but it is better to use TTA - test time augmentation. Let's see how it can be done with Kekas.
* define augmentations;
* define augmentation function;
* create objects with these augmentations;
* put these objects into a single dictionary;
In [22]:
```py
preds = keker.predict_loader(loader=test_dl)
```
```
Predict: 100% 63/63 [00:11<00:00, 6.02it/s]
```
In [23]:
```py
# flip_ = albumentations.HorizontalFlip(always_apply=True)
# transpose_ = albumentations.Transpose(always_apply=True)
# def insert_aug(aug, dataset_key="image", size=224):
# PRE_TFMS = Transformer(dataset_key, lambda x: cv2.resize(x, (size, size)))
# AUGS = Transformer(dataset_key, lambda x: aug(image=x)["image"])
# NRM_TFMS = transforms.Compose([
# Transformer(dataset_key, to_torch()),
# Transformer(dataset_key, normalize())
# ])
# tfm = transforms.Compose([PRE_TFMS, AUGS, NRM_TFMS])
# return tfm
# flip = insert_aug(flip_)
# transpose = insert_aug(transpose_)
# tta_tfms = {"flip": flip, "transpose": transpose}
# # third, run TTA
# keker.TTA(loader=test_dl, # loader to predict on
# tfms=tta_tfms, # list or dict of always applying transforms
# savedir="tta_preds1", # savedir
# prefix="preds") # (optional) name prefix. default is 'preds'
```
In [24]:
```py
# prediction = np.zeros((test_df.shape[0], 1))
# for i in os.listdir('tta_preds1'):
# pr = np.load('tta_preds1/' + i)
# prediction += pr
# prediction = prediction / len(os.listdir('tta_preds1'))
```
In [25]:
```py
test_preds = pd.DataFrame({'imgs': test_df.id.values, 'preds': preds.reshape(-1,)})
test_preds.columns = ['id', 'has_cactus']
test_preds.to_csv('sub.csv', index=False)
test_preds.head()
```
Out[25]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 79ac4cc3b082e0a1defe1be601806efd.jpg | 4.901506 |
| --- | --- | --- |
| 1 | e880364d6521c6f3a27748ec62b0e335.jpg | 9.305355 |
| --- | --- | --- |
| 2 | 74912492b6cdf28c4bfb9c8e1d35af3e.jpg | 8.171706 |
| --- | --- | --- |
| 3 | 078cfa961183b30693ea2f13f5ff6d17.jpg | 8.186723 |
| --- | --- | --- |
| 4 | 7fd729184ef182899ce3e7a174fb9bc0.jpg | 9.269026 |
| --- | --- | --- |
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/fast-fastai-with-condensenet.md
================================================
# Fast fastai with condensenet
> Author: https://www.kaggle.com/interneuron
> From: https://www.kaggle.com/interneuron/fast-fastai-with-condensenet
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
> Score: 1.0000
forked from [https://www.kaggle.com/kenseitrg/simple-fastai-exercise](https://www.kaggle.com/kenseitrg/simple-fastai-exercise)
In [1]:
```py
!pip install pytorchcv
```
```
Collecting pytorchcv
Downloading https://files.pythonhosted.org/packages/20/8c/c9a820af0a5d56c4f5803a3138319ce76907c2b6db61fd9edd9dec483bb9/pytorchcv-0.0.42-py2.py3-none-any.whl (280kB)
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Installing collected packages: pytorchcv
Successfully installed pytorchcv-0.0.42
```
In [2]:
```py
!pip install fastai==1.0.47
```
```
Collecting fastai==1.0.47
Downloading https://files.pythonhosted.org/packages/4b/92/134c4ce85851f6c9156e3363c7d396716a17dc9915b4921b490f96a5a4f2/fastai-1.0.47-py3-none-any.whl (205kB)
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```
In [3]:
```py
import time
start = time.time()
```
In [4]:
```py
import numpy as np
import pandas as pd
from pytorchcv.model_provider import get_model as ptcv_get_model
```
In [5]:
```py
from pathlib import Path
from fastai import *
from fastai.vision import *
import torch
```
In [6]:
```py
data_folder = Path("../input")
#data_folder.ls()
```
In [7]:
```py
train_df = pd.read_csv("../input/train.csv")
test_df = pd.read_csv("../input/sample_submission.csv")
```
In [8]:
```py
test_img = ImageList.from_df(test_df, path=data_folder/'test', folder='test')
trfm = get_transforms(do_flip=True, flip_vert=True, max_rotate=10.0, max_zoom=1.1, max_lighting=0.2, max_warp=0.2, p_affine=0.75, p_lighting=0.75)
train_img = (ImageList.from_df(train_df, path=data_folder/'train', folder='train')
.split_by_rand_pct(0.01)
.label_from_df()
.add_test(test_img)
.transform(trfm, size=128)
.databunch(path='.', bs=64, device= torch.device('cuda:0'))
.normalize(imagenet_stats)
)
```
In [9]:
```py
def md(f=None):
mdl = ptcv_get_model('condensenet74_c4_g4', pretrained=True)
mdl.features.final_pool = nn.AvgPool2d(kernel_size=7, stride=1, padding=3)
return mdl
```
In [10]:
```py
learn = cnn_learner(train_img, md, metrics=[error_rate, accuracy])
```
```
Downloading /tmp/.torch/models/condensenet74_c4_g4-0828-5ba55049.pth.zip from https://github.com/osmr/imgclsmob/releases/download/v0.0.4/condensenet74_c4_g4-0828-5ba55049.pth.zip...
```
In [11]:
```py
#learn.lr_find()
#learn.recorder.plot()
```
In [12]:
```py
lr = 3.5e-02
learn.fit_one_cycle(5, slice(lr))
```
Total time: 04:54
| epoch | train_loss | valid_loss | error_rate | accuracy | time |
| --- | --- | --- | --- | --- | --- |
| 0 | 0.083160 | 0.177766 | 0.040000 | 0.960000 | 01:06 |
| 1 | 0.054930 | 0.036326 | 0.011429 | 0.988571 | 00:58 |
| 2 | 0.040563 | 0.048152 | 0.011429 | 0.988571 | 00:58 |
| 3 | 0.009462 | 0.001094 | 0.000000 | 1.000000 | 00:56 |
| 4 | 0.004515 | 0.001212 | 0.000000 | 1.000000 | 00:54 |
In [13]:
```py
preds,_ = learn.get_preds(ds_type=DatasetType.Test)
```
In [14]:
```py
test_df.has_cactus = preds.numpy()[:, 0]
```
In [15]:
```py
test_df.to_csv('submission.csv', index=False)
```
In [16]:
```py
end = time.time()
print(end - start)
```
```
313.3328809738159
```
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/keras-transfer-vgg16.md
================================================
# Keras _Transfer_VGG16
> Author: https://www.kaggle.com/ateplyuk
> From: https://www.kaggle.com/ateplyuk/keras-transfer-vgg16
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
In [1]:
```py
import cv2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import os
from tqdm import tqdm, tqdm_notebook
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.applications import VGG16
from keras.optimizers import Adam
```
```
Using TensorFlow backend.
```
In [2]:
```py
train_dir = "../input/train/train/"
test_dir = "../input/test/test/"
train_df = pd.read_csv('../input/train.csv')
train_df.head()
```
Out[2]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |
| --- | --- | --- |
| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |
| --- | --- | --- |
| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |
| --- | --- | --- |
| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |
| --- | --- | --- |
| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |
| --- | --- | --- |
In [3]:
```py
im = cv2.imread("../input/train/train/01e30c0ba6e91343a12d2126fcafc0dd.jpg")
plt.imshow(im)
```
Out[3]:
```
```
In [4]:
```py
vgg16_net = VGG16(weights='imagenet',
include_top=False,
input_shape=(32, 32, 3))
```
```
Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
58892288/58889256 [==============================] - 2s 0us/step
```
In [5]:
```py
vgg16_net.trainable = False
vgg16_net.summary()
```
```
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 32, 32, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 32, 32, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 32, 32, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 16, 16, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 16, 16, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 16, 16, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 8, 8, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 8, 8, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 8, 8, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 8, 8, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 4, 4, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 4, 4, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 4, 4, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 4, 4, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 2, 2, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 2, 2, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 2, 2, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 2, 2, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 1, 1, 512) 0
=================================================================
Total params: 14,714,688
Trainable params: 0
Non-trainable params: 14,714,688
_________________________________________________________________
```
In [6]:
```py
model = Sequential()
model.add(vgg16_net)
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
```
In [7]:
```py
model.compile(loss='binary_crossentropy',
optimizer=Adam(lr=1e-5),
metrics=['accuracy'])
```
In [8]:
```py
X_tr = []
Y_tr = []
imges = train_df['id'].values
for img_id in tqdm_notebook(imges):
X_tr.append(cv2.imread(train_dir + img_id))
Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])
X_tr = np.asarray(X_tr)
X_tr = X_tr.astype('float32')
X_tr /= 255
Y_tr = np.asarray(Y_tr)
```
In [9]:
```py
batch_size = 32
nb_epoch = 1000
```
In [10]:
```py
%%time
# Train model
history = model.fit(X_tr, Y_tr,
batch_size=batch_size,
epochs=nb_epoch,
validation_split=0.1,
shuffle=True,
verbose=2)
```
```
Train on 15750 samples, validate on 1750 samples
Epoch 1/1000
- 6s - loss: 0.6634 - acc: 0.6057 - val_loss: 0.5174 - val_acc: 0.7469
Epoch 2/1000
- 4s - loss: 0.4968 - acc: 0.7640 - val_loss: 0.4371 - val_acc: 0.7491
Epoch 3/1000
- 4s - loss: 0.4248 - acc: 0.7969 - val_loss: 0.3778 - val_acc: 0.7846
Epoch 4/1000
- 4s - loss: 0.3735 - acc: 0.8302 - val_loss: 0.3338 - val_acc: 0.8429
Epoch 5/1000
- 4s - loss: 0.3326 - acc: 0.8596 - val_loss: 0.2985 - val_acc: 0.9057
Epoch 6/1000
- 4s - loss: 0.3004 - acc: 0.8844 - val_loss: 0.2719 - val_acc: 0.9200
Epoch 7/1000
- 4s - loss: 0.2755 - acc: 0.8990 - val_loss: 0.2502 - val_acc: 0.9326
Epoch 8/1000
- 4s - loss: 0.2560 - acc: 0.9116 - val_loss: 0.2326 - val_acc: 0.9383
Epoch 9/1000
- 4s - loss: 0.2406 - acc: 0.9180 - val_loss: 0.2184 - val_acc: 0.9400
Epoch 10/1000
- 4s - loss: 0.2257 - acc: 0.9233 - val_loss: 0.2064 - val_acc: 0.9423
Epoch 11/1000
- 4s - loss: 0.2124 - acc: 0.9278 - val_loss: 0.1955 - val_acc: 0.9423
Epoch 12/1000
- 4s - loss: 0.2035 - acc: 0.9333 - val_loss: 0.1866 - val_acc: 0.9469
Epoch 13/1000
- 4s - loss: 0.1951 - acc: 0.9349 - val_loss: 0.1787 - val_acc: 0.9491
Epoch 14/1000
- 4s - loss: 0.1870 - acc: 0.9342 - val_loss: 0.1723 - val_acc: 0.9503
Epoch 15/1000
- 4s - loss: 0.1809 - acc: 0.9382 - val_loss: 0.1664 - val_acc: 0.9509
Epoch 16/1000
- 4s - loss: 0.1760 - acc: 0.9385 - val_loss: 0.1612 - val_acc: 0.9526
Epoch 17/1000
- 4s - loss: 0.1700 - acc: 0.9423 - val_loss: 0.1572 - val_acc: 0.9514
Epoch 18/1000
- 4s - loss: 0.1654 - acc: 0.9448 - val_loss: 0.1524 - val_acc: 0.9531
Epoch 19/1000
- 4s - loss: 0.1617 - acc: 0.9438 - val_loss: 0.1491 - val_acc: 0.9543
Epoch 20/1000
- 4s - loss: 0.1576 - acc: 0.9448 - val_loss: 0.1459 - val_acc: 0.9549
Epoch 21/1000
- 4s - loss: 0.1532 - acc: 0.9462 - val_loss: 0.1424 - val_acc: 0.9543
Epoch 22/1000
- 4s - loss: 0.1501 - acc: 0.9495 - val_loss: 0.1404 - val_acc: 0.9549
Epoch 23/1000
- 4s - loss: 0.1476 - acc: 0.9474 - val_loss: 0.1371 - val_acc: 0.9543
Epoch 24/1000
- 4s - loss: 0.1454 - acc: 0.9477 - val_loss: 0.1359 - val_acc: 0.9549
Epoch 25/1000
- 4s - loss: 0.1437 - acc: 0.9486 - val_loss: 0.1330 - val_acc: 0.9549
Epoch 26/1000
- 4s - loss: 0.1424 - acc: 0.9490 - val_loss: 0.1306 - val_acc: 0.9543
Epoch 27/1000
- 4s - loss: 0.1399 - acc: 0.9501 - val_loss: 0.1291 - val_acc: 0.9554
Epoch 28/1000
- 4s - loss: 0.1371 - acc: 0.9505 - val_loss: 0.1274 - val_acc: 0.9549
Epoch 29/1000
- 4s - loss: 0.1369 - acc: 0.9503 - val_loss: 0.1262 - val_acc: 0.9566
Epoch 30/1000
- 4s - loss: 0.1338 - acc: 0.9514 - val_loss: 0.1241 - val_acc: 0.9577
Epoch 31/1000
- 4s - loss: 0.1328 - acc: 0.9514 - val_loss: 0.1224 - val_acc: 0.9577
Epoch 32/1000
- 4s - loss: 0.1324 - acc: 0.9520 - val_loss: 0.1213 - val_acc: 0.9589
Epoch 33/1000
- 4s - loss: 0.1290 - acc: 0.9540 - val_loss: 0.1202 - val_acc: 0.9589
Epoch 34/1000
- 4s - loss: 0.1287 - acc: 0.9532 - val_loss: 0.1188 - val_acc: 0.9583
Epoch 35/1000
- 4s - loss: 0.1276 - acc: 0.9528 - val_loss: 0.1177 - val_acc: 0.9583
Epoch 36/1000
- 4s - loss: 0.1277 - acc: 0.9528 - val_loss: 0.1171 - val_acc: 0.9589
Epoch 37/1000
- 4s - loss: 0.1255 - acc: 0.9545 - val_loss: 0.1158 - val_acc: 0.9589
Epoch 38/1000
- 4s - loss: 0.1227 - acc: 0.9561 - val_loss: 0.1152 - val_acc: 0.9594
Epoch 39/1000
- 4s - loss: 0.1230 - acc: 0.9554 - val_loss: 0.1137 - val_acc: 0.9583
Epoch 40/1000
- 4s - loss: 0.1209 - acc: 0.9559 - val_loss: 0.1127 - val_acc: 0.9583
Epoch 41/1000
- 4s - loss: 0.1202 - acc: 0.9563 - val_loss: 0.1126 - val_acc: 0.9606
Epoch 42/1000
- 4s - loss: 0.1198 - acc: 0.9561 - val_loss: 0.1117 - val_acc: 0.9606
Epoch 43/1000
- 4s - loss: 0.1184 - acc: 0.9565 - val_loss: 0.1101 - val_acc: 0.9589
Epoch 44/1000
- 4s - loss: 0.1165 - acc: 0.9583 - val_loss: 0.1096 - val_acc: 0.9589
Epoch 45/1000
- 4s - loss: 0.1179 - acc: 0.9574 - val_loss: 0.1086 - val_acc: 0.9594
Epoch 46/1000
- 4s - loss: 0.1160 - acc: 0.9580 - val_loss: 0.1081 - val_acc: 0.9594
Epoch 47/1000
- 4s - loss: 0.1156 - acc: 0.9573 - val_loss: 0.1073 - val_acc: 0.9600
Epoch 48/1000
- 4s - loss: 0.1144 - acc: 0.9570 - val_loss: 0.1074 - val_acc: 0.9629
Epoch 49/1000
- 4s - loss: 0.1145 - acc: 0.9575 - val_loss: 0.1058 - val_acc: 0.9594
Epoch 50/1000
- 4s - loss: 0.1135 - acc: 0.9589 - val_loss: 0.1053 - val_acc: 0.9617
Epoch 51/1000
- 4s - loss: 0.1127 - acc: 0.9590 - val_loss: 0.1047 - val_acc: 0.9611
Epoch 52/1000
- 4s - loss: 0.1119 - acc: 0.9582 - val_loss: 0.1046 - val_acc: 0.9634
Epoch 53/1000
- 4s - loss: 0.1110 - acc: 0.9596 - val_loss: 0.1039 - val_acc: 0.9629
Epoch 54/1000
- 4s - loss: 0.1108 - acc: 0.9593 - val_loss: 0.1032 - val_acc: 0.9623
Epoch 55/1000
- 4s - loss: 0.1097 - acc: 0.9593 - val_loss: 0.1024 - val_acc: 0.9617
Epoch 56/1000
- 4s - loss: 0.1089 - acc: 0.9585 - val_loss: 0.1021 - val_acc: 0.9623
Epoch 57/1000
- 4s - loss: 0.1074 - acc: 0.9611 - val_loss: 0.1018 - val_acc: 0.9640
Epoch 58/1000
- 4s - loss: 0.1082 - acc: 0.9592 - val_loss: 0.1008 - val_acc: 0.9617
Epoch 59/1000
- 4s - loss: 0.1076 - acc: 0.9608 - val_loss: 0.1006 - val_acc: 0.9623
Epoch 60/1000
- 4s - loss: 0.1065 - acc: 0.9615 - val_loss: 0.1003 - val_acc: 0.9651
Epoch 61/1000
- 4s - loss: 0.1060 - acc: 0.9605 - val_loss: 0.0995 - val_acc: 0.9623
Epoch 62/1000
- 4s - loss: 0.1051 - acc: 0.9615 - val_loss: 0.0990 - val_acc: 0.9629
Epoch 63/1000
- 4s - loss: 0.1059 - acc: 0.9609 - val_loss: 0.0991 - val_acc: 0.9657
Epoch 64/1000
- 4s - loss: 0.1042 - acc: 0.9622 - val_loss: 0.0989 - val_acc: 0.9657
Epoch 65/1000
- 4s - loss: 0.1040 - acc: 0.9619 - val_loss: 0.0981 - val_acc: 0.9646
Epoch 66/1000
- 4s - loss: 0.1040 - acc: 0.9630 - val_loss: 0.0973 - val_acc: 0.9640
Epoch 67/1000
- 4s - loss: 0.1024 - acc: 0.9624 - val_loss: 0.0973 - val_acc: 0.9646
Epoch 68/1000
- 4s - loss: 0.1019 - acc: 0.9634 - val_loss: 0.0967 - val_acc: 0.9640
Epoch 69/1000
- 4s - loss: 0.1022 - acc: 0.9634 - val_loss: 0.0966 - val_acc: 0.9651
Epoch 70/1000
- 4s - loss: 0.1004 - acc: 0.9641 - val_loss: 0.0955 - val_acc: 0.9646
Epoch 71/1000
- 4s - loss: 0.1021 - acc: 0.9622 - val_loss: 0.0952 - val_acc: 0.9640
Epoch 72/1000
- 4s - loss: 0.1025 - acc: 0.9625 - val_loss: 0.0947 - val_acc: 0.9640
Epoch 73/1000
- 4s - loss: 0.1002 - acc: 0.9627 - val_loss: 0.0943 - val_acc: 0.9646
Epoch 74/1000
- 4s - loss: 0.0998 - acc: 0.9631 - val_loss: 0.0941 - val_acc: 0.9640
Epoch 75/1000
- 4s - loss: 0.1006 - acc: 0.9632 - val_loss: 0.0943 - val_acc: 0.9640
Epoch 76/1000
- 4s - loss: 0.0991 - acc: 0.9636 - val_loss: 0.0935 - val_acc: 0.9651
Epoch 77/1000
- 4s - loss: 0.0991 - acc: 0.9650 - val_loss: 0.0931 - val_acc: 0.9646
Epoch 78/1000
- 4s - loss: 0.0983 - acc: 0.9642 - val_loss: 0.0935 - val_acc: 0.9651
Epoch 79/1000
- 4s - loss: 0.0983 - acc: 0.9636 - val_loss: 0.0926 - val_acc: 0.9651
Epoch 80/1000
- 4s - loss: 0.0965 - acc: 0.9651 - val_loss: 0.0921 - val_acc: 0.9651
Epoch 81/1000
- 4s - loss: 0.0970 - acc: 0.9630 - val_loss: 0.0920 - val_acc: 0.9646
Epoch 82/1000
- 4s - loss: 0.0962 - acc: 0.9648 - val_loss: 0.0916 - val_acc: 0.9651
Epoch 83/1000
- 4s - loss: 0.0984 - acc: 0.9632 - val_loss: 0.0915 - val_acc: 0.9646
Epoch 84/1000
- 4s - loss: 0.0980 - acc: 0.9639 - val_loss: 0.0912 - val_acc: 0.9646
Epoch 85/1000
- 4s - loss: 0.0964 - acc: 0.9655 - val_loss: 0.0910 - val_acc: 0.9651
Epoch 86/1000
- 4s - loss: 0.0956 - acc: 0.9650 - val_loss: 0.0910 - val_acc: 0.9651
Epoch 87/1000
- 4s - loss: 0.0942 - acc: 0.9643 - val_loss: 0.0904 - val_acc: 0.9651
Epoch 88/1000
- 4s - loss: 0.0953 - acc: 0.9648 - val_loss: 0.0901 - val_acc: 0.9651
Epoch 89/1000
- 4s - loss: 0.0944 - acc: 0.9658 - val_loss: 0.0898 - val_acc: 0.9651
Epoch 90/1000
- 4s - loss: 0.0945 - acc: 0.9651 - val_loss: 0.0895 - val_acc: 0.9651
Epoch 91/1000
- 4s - loss: 0.0936 - acc: 0.9657 - val_loss: 0.0888 - val_acc: 0.9657
Epoch 92/1000
- 4s - loss: 0.0924 - acc: 0.9658 - val_loss: 0.0891 - val_acc: 0.9646
Epoch 93/1000
- 4s - loss: 0.0924 - acc: 0.9658 - val_loss: 0.0889 - val_acc: 0.9657
Epoch 94/1000
- 4s - loss: 0.0935 - acc: 0.9655 - val_loss: 0.0883 - val_acc: 0.9663
Epoch 95/1000
- 4s - loss: 0.0933 - acc: 0.9653 - val_loss: 0.0882 - val_acc: 0.9663
Epoch 96/1000
- 4s - loss: 0.0923 - acc: 0.9654 - val_loss: 0.0884 - val_acc: 0.9669
Epoch 97/1000
- 4s - loss: 0.0916 - acc: 0.9648 - val_loss: 0.0876 - val_acc: 0.9663
Epoch 98/1000
- 4s - loss: 0.0920 - acc: 0.9659 - val_loss: 0.0877 - val_acc: 0.9669
Epoch 99/1000
- 4s - loss: 0.0923 - acc: 0.9661 - val_loss: 0.0872 - val_acc: 0.9669
Epoch 100/1000
- 4s - loss: 0.0909 - acc: 0.9674 - val_loss: 0.0871 - val_acc: 0.9669
Epoch 101/1000
- 4s - loss: 0.0907 - acc: 0.9661 - val_loss: 0.0866 - val_acc: 0.9669
Epoch 102/1000
- 4s - loss: 0.0906 - acc: 0.9668 - val_loss: 0.0863 - val_acc: 0.9674
Epoch 103/1000
- 4s - loss: 0.0903 - acc: 0.9674 - val_loss: 0.0861 - val_acc: 0.9669
Epoch 104/1000
- 4s - loss: 0.0893 - acc: 0.9665 - val_loss: 0.0861 - val_acc: 0.9669
Epoch 105/1000
- 4s - loss: 0.0897 - acc: 0.9663 - val_loss: 0.0858 - val_acc: 0.9669
Epoch 106/1000
- 4s - loss: 0.0894 - acc: 0.9672 - val_loss: 0.0855 - val_acc: 0.9674
Epoch 107/1000
- 4s - loss: 0.0889 - acc: 0.9678 - val_loss: 0.0856 - val_acc: 0.9669
Epoch 108/1000
- 4s - loss: 0.0886 - acc: 0.9672 - val_loss: 0.0856 - val_acc: 0.9669
Epoch 109/1000
- 4s - loss: 0.0882 - acc: 0.9681 - val_loss: 0.0854 - val_acc: 0.9669
Epoch 110/1000
- 4s - loss: 0.0889 - acc: 0.9671 - val_loss: 0.0851 - val_acc: 0.9669
Epoch 111/1000
- 4s - loss: 0.0877 - acc: 0.9693 - val_loss: 0.0846 - val_acc: 0.9686
Epoch 112/1000
- 4s - loss: 0.0876 - acc: 0.9674 - val_loss: 0.0844 - val_acc: 0.9680
Epoch 113/1000
- 4s - loss: 0.0860 - acc: 0.9681 - val_loss: 0.0848 - val_acc: 0.9686
Epoch 114/1000
- 4s - loss: 0.0866 - acc: 0.9684 - val_loss: 0.0840 - val_acc: 0.9686
Epoch 115/1000
- 4s - loss: 0.0870 - acc: 0.9681 - val_loss: 0.0837 - val_acc: 0.9686
Epoch 116/1000
- 4s - loss: 0.0861 - acc: 0.9681 - val_loss: 0.0836 - val_acc: 0.9686
Epoch 117/1000
- 4s - loss: 0.0855 - acc: 0.9681 - val_loss: 0.0838 - val_acc: 0.9686
Epoch 118/1000
- 4s - loss: 0.0859 - acc: 0.9684 - val_loss: 0.0832 - val_acc: 0.9686
Epoch 119/1000
- 4s - loss: 0.0859 - acc: 0.9688 - val_loss: 0.0830 - val_acc: 0.9686
Epoch 120/1000
- 4s - loss: 0.0858 - acc: 0.9674 - val_loss: 0.0830 - val_acc: 0.9691
Epoch 121/1000
- 4s - loss: 0.0843 - acc: 0.9691 - val_loss: 0.0829 - val_acc: 0.9686
Epoch 122/1000
- 4s - loss: 0.0853 - acc: 0.9684 - val_loss: 0.0827 - val_acc: 0.9691
Epoch 123/1000
- 4s - loss: 0.0851 - acc: 0.9688 - val_loss: 0.0826 - val_acc: 0.9691
Epoch 124/1000
- 4s - loss: 0.0845 - acc: 0.9689 - val_loss: 0.0822 - val_acc: 0.9691
Epoch 125/1000
- 4s - loss: 0.0848 - acc: 0.9684 - val_loss: 0.0820 - val_acc: 0.9691
Epoch 126/1000
- 4s - loss: 0.0831 - acc: 0.9688 - val_loss: 0.0820 - val_acc: 0.9691
Epoch 127/1000
- 4s - loss: 0.0850 - acc: 0.9677 - val_loss: 0.0817 - val_acc: 0.9691
Epoch 128/1000
- 4s - loss: 0.0846 - acc: 0.9685 - val_loss: 0.0815 - val_acc: 0.9691
Epoch 129/1000
- 4s - loss: 0.0836 - acc: 0.9694 - val_loss: 0.0817 - val_acc: 0.9691
Epoch 130/1000
- 4s - loss: 0.0828 - acc: 0.9697 - val_loss: 0.0811 - val_acc: 0.9691
Epoch 131/1000
- 4s - loss: 0.0830 - acc: 0.9698 - val_loss: 0.0809 - val_acc: 0.9691
Epoch 132/1000
- 4s - loss: 0.0825 - acc: 0.9700 - val_loss: 0.0810 - val_acc: 0.9691
Epoch 133/1000
- 4s - loss: 0.0822 - acc: 0.9699 - val_loss: 0.0805 - val_acc: 0.9691
Epoch 134/1000
- 4s - loss: 0.0823 - acc: 0.9701 - val_loss: 0.0806 - val_acc: 0.9691
Epoch 135/1000
- 4s - loss: 0.0822 - acc: 0.9694 - val_loss: 0.0802 - val_acc: 0.9691
Epoch 136/1000
- 4s - loss: 0.0816 - acc: 0.9711 - val_loss: 0.0807 - val_acc: 0.9691
Epoch 137/1000
- 4s - loss: 0.0828 - acc: 0.9690 - val_loss: 0.0804 - val_acc: 0.9691
Epoch 138/1000
- 4s - loss: 0.0820 - acc: 0.9693 - val_loss: 0.0801 - val_acc: 0.9691
Epoch 139/1000
- 4s - loss: 0.0825 - acc: 0.9697 - val_loss: 0.0798 - val_acc: 0.9691
Epoch 140/1000
- 4s - loss: 0.0820 - acc: 0.9690 - val_loss: 0.0797 - val_acc: 0.9691
Epoch 141/1000
- 4s - loss: 0.0810 - acc: 0.9697 - val_loss: 0.0796 - val_acc: 0.9691
Epoch 142/1000
- 4s - loss: 0.0804 - acc: 0.9707 - val_loss: 0.0797 - val_acc: 0.9691
Epoch 143/1000
- 4s - loss: 0.0812 - acc: 0.9697 - val_loss: 0.0794 - val_acc: 0.9691
Epoch 144/1000
- 4s - loss: 0.0805 - acc: 0.9695 - val_loss: 0.0791 - val_acc: 0.9686
Epoch 145/1000
- 4s - loss: 0.0804 - acc: 0.9709 - val_loss: 0.0794 - val_acc: 0.9691
Epoch 146/1000
- 4s - loss: 0.0799 - acc: 0.9706 - val_loss: 0.0789 - val_acc: 0.9691
Epoch 147/1000
- 4s - loss: 0.0790 - acc: 0.9714 - val_loss: 0.0788 - val_acc: 0.9691
Epoch 148/1000
- 4s - loss: 0.0794 - acc: 0.9703 - val_loss: 0.0787 - val_acc: 0.9691
Epoch 149/1000
- 4s - loss: 0.0792 - acc: 0.9702 - val_loss: 0.0785 - val_acc: 0.9691
Epoch 150/1000
- 4s - loss: 0.0802 - acc: 0.9709 - val_loss: 0.0783 - val_acc: 0.9691
Epoch 151/1000
- 4s - loss: 0.0795 - acc: 0.9711 - val_loss: 0.0781 - val_acc: 0.9691
Epoch 152/1000
- 4s - loss: 0.0778 - acc: 0.9706 - val_loss: 0.0780 - val_acc: 0.9691
Epoch 153/1000
- 4s - loss: 0.0772 - acc: 0.9716 - val_loss: 0.0782 - val_acc: 0.9691
Epoch 154/1000
- 4s - loss: 0.0783 - acc: 0.9710 - val_loss: 0.0779 - val_acc: 0.9691
Epoch 155/1000
- 4s - loss: 0.0789 - acc: 0.9704 - val_loss: 0.0779 - val_acc: 0.9691
Epoch 156/1000
- 4s - loss: 0.0782 - acc: 0.9708 - val_loss: 0.0781 - val_acc: 0.9703
Epoch 157/1000
- 4s - loss: 0.0777 - acc: 0.9712 - val_loss: 0.0774 - val_acc: 0.9691
Epoch 158/1000
- 4s - loss: 0.0775 - acc: 0.9715 - val_loss: 0.0779 - val_acc: 0.9703
Epoch 159/1000
- 4s - loss: 0.0775 - acc: 0.9722 - val_loss: 0.0775 - val_acc: 0.9697
Epoch 160/1000
- 4s - loss: 0.0767 - acc: 0.9719 - val_loss: 0.0770 - val_acc: 0.9686
Epoch 161/1000
- 4s - loss: 0.0773 - acc: 0.9712 - val_loss: 0.0772 - val_acc: 0.9703
Epoch 162/1000
- 4s - loss: 0.0770 - acc: 0.9716 - val_loss: 0.0770 - val_acc: 0.9697
Epoch 163/1000
- 4s - loss: 0.0765 - acc: 0.9714 - val_loss: 0.0770 - val_acc: 0.9703
Epoch 164/1000
- 4s - loss: 0.0779 - acc: 0.9707 - val_loss: 0.0767 - val_acc: 0.9691
Epoch 165/1000
- 4s - loss: 0.0768 - acc: 0.9712 - val_loss: 0.0767 - val_acc: 0.9697
Epoch 166/1000
- 4s - loss: 0.0765 - acc: 0.9719 - val_loss: 0.0763 - val_acc: 0.9686
Epoch 167/1000
- 4s - loss: 0.0755 - acc: 0.9714 - val_loss: 0.0765 - val_acc: 0.9703
Epoch 168/1000
- 4s - loss: 0.0755 - acc: 0.9731 - val_loss: 0.0762 - val_acc: 0.9691
Epoch 169/1000
- 4s - loss: 0.0764 - acc: 0.9712 - val_loss: 0.0763 - val_acc: 0.9703
Epoch 170/1000
- 4s - loss: 0.0759 - acc: 0.9721 - val_loss: 0.0760 - val_acc: 0.9697
Epoch 171/1000
- 4s - loss: 0.0756 - acc: 0.9723 - val_loss: 0.0761 - val_acc: 0.9703
Epoch 172/1000
- 4s - loss: 0.0753 - acc: 0.9719 - val_loss: 0.0758 - val_acc: 0.9703
Epoch 173/1000
- 4s - loss: 0.0752 - acc: 0.9721 - val_loss: 0.0761 - val_acc: 0.9703
Epoch 174/1000
- 4s - loss: 0.0757 - acc: 0.9712 - val_loss: 0.0755 - val_acc: 0.9703
Epoch 175/1000
- 4s - loss: 0.0741 - acc: 0.9728 - val_loss: 0.0755 - val_acc: 0.9703
Epoch 176/1000
- 4s - loss: 0.0753 - acc: 0.9721 - val_loss: 0.0755 - val_acc: 0.9703
Epoch 177/1000
- 4s - loss: 0.0760 - acc: 0.9722 - val_loss: 0.0752 - val_acc: 0.9697
Epoch 178/1000
- 4s - loss: 0.0748 - acc: 0.9729 - val_loss: 0.0752 - val_acc: 0.9703
Epoch 179/1000
- 4s - loss: 0.0745 - acc: 0.9725 - val_loss: 0.0751 - val_acc: 0.9697
Epoch 180/1000
- 4s - loss: 0.0744 - acc: 0.9723 - val_loss: 0.0751 - val_acc: 0.9703
Epoch 181/1000
- 4s - loss: 0.0741 - acc: 0.9725 - val_loss: 0.0750 - val_acc: 0.9703
Epoch 182/1000
- 4s - loss: 0.0739 - acc: 0.9723 - val_loss: 0.0747 - val_acc: 0.9703
Epoch 183/1000
- 4s - loss: 0.0757 - acc: 0.9714 - val_loss: 0.0749 - val_acc: 0.9703
Epoch 184/1000
- 4s - loss: 0.0739 - acc: 0.9734 - val_loss: 0.0749 - val_acc: 0.9703
Epoch 185/1000
- 4s - loss: 0.0730 - acc: 0.9722 - val_loss: 0.0746 - val_acc: 0.9697
Epoch 186/1000
- 4s - loss: 0.0739 - acc: 0.9735 - val_loss: 0.0744 - val_acc: 0.9703
Epoch 187/1000
- 4s - loss: 0.0732 - acc: 0.9728 - val_loss: 0.0743 - val_acc: 0.9703
Epoch 188/1000
- 4s - loss: 0.0735 - acc: 0.9732 - val_loss: 0.0743 - val_acc: 0.9703
Epoch 189/1000
- 4s - loss: 0.0729 - acc: 0.9721 - val_loss: 0.0744 - val_acc: 0.9703
Epoch 190/1000
- 4s - loss: 0.0739 - acc: 0.9731 - val_loss: 0.0743 - val_acc: 0.9703
Epoch 191/1000
- 4s - loss: 0.0725 - acc: 0.9733 - val_loss: 0.0741 - val_acc: 0.9697
Epoch 192/1000
- 4s - loss: 0.0731 - acc: 0.9731 - val_loss: 0.0743 - val_acc: 0.9709
Epoch 193/1000
- 4s - loss: 0.0722 - acc: 0.9731 - val_loss: 0.0741 - val_acc: 0.9703
Epoch 194/1000
- 4s - loss: 0.0717 - acc: 0.9736 - val_loss: 0.0741 - val_acc: 0.9709
Epoch 195/1000
- 4s - loss: 0.0728 - acc: 0.9735 - val_loss: 0.0738 - val_acc: 0.9703
Epoch 196/1000
- 4s - loss: 0.0722 - acc: 0.9734 - val_loss: 0.0737 - val_acc: 0.9703
Epoch 197/1000
- 4s - loss: 0.0728 - acc: 0.9727 - val_loss: 0.0739 - val_acc: 0.9709
Epoch 198/1000
- 4s - loss: 0.0720 - acc: 0.9736 - val_loss: 0.0735 - val_acc: 0.9703
Epoch 199/1000
- 4s - loss: 0.0717 - acc: 0.9742 - val_loss: 0.0734 - val_acc: 0.9697
Epoch 200/1000
- 4s - loss: 0.0712 - acc: 0.9737 - val_loss: 0.0735 - val_acc: 0.9709
Epoch 201/1000
- 4s - loss: 0.0715 - acc: 0.9742 - val_loss: 0.0733 - val_acc: 0.9697
Epoch 202/1000
- 4s - loss: 0.0716 - acc: 0.9742 - val_loss: 0.0732 - val_acc: 0.9697
Epoch 203/1000
- 4s - loss: 0.0709 - acc: 0.9742 - val_loss: 0.0734 - val_acc: 0.9709
Epoch 204/1000
- 4s - loss: 0.0719 - acc: 0.9726 - val_loss: 0.0730 - val_acc: 0.9697
Epoch 205/1000
- 4s - loss: 0.0704 - acc: 0.9725 - val_loss: 0.0729 - val_acc: 0.9697
Epoch 206/1000
- 5s - loss: 0.0710 - acc: 0.9740 - val_loss: 0.0731 - val_acc: 0.9709
Epoch 207/1000
- 4s - loss: 0.0714 - acc: 0.9731 - val_loss: 0.0730 - val_acc: 0.9709
Epoch 208/1000
- 4s - loss: 0.0707 - acc: 0.9739 - val_loss: 0.0728 - val_acc: 0.9709
Epoch 209/1000
- 4s - loss: 0.0709 - acc: 0.9732 - val_loss: 0.0726 - val_acc: 0.9709
Epoch 210/1000
- 4s - loss: 0.0693 - acc: 0.9745 - val_loss: 0.0727 - val_acc: 0.9709
Epoch 211/1000
- 4s - loss: 0.0715 - acc: 0.9731 - val_loss: 0.0728 - val_acc: 0.9709
Epoch 212/1000
- 4s - loss: 0.0696 - acc: 0.9739 - val_loss: 0.0726 - val_acc: 0.9709
Epoch 213/1000
- 4s - loss: 0.0699 - acc: 0.9743 - val_loss: 0.0725 - val_acc: 0.9709
Epoch 214/1000
- 4s - loss: 0.0698 - acc: 0.9748 - val_loss: 0.0723 - val_acc: 0.9709
Epoch 215/1000
- 4s - loss: 0.0695 - acc: 0.9742 - val_loss: 0.0724 - val_acc: 0.9709
Epoch 216/1000
- 4s - loss: 0.0694 - acc: 0.9744 - val_loss: 0.0721 - val_acc: 0.9714
Epoch 217/1000
- 4s - loss: 0.0700 - acc: 0.9739 - val_loss: 0.0722 - val_acc: 0.9714
Epoch 218/1000
- 4s - loss: 0.0679 - acc: 0.9747 - val_loss: 0.0720 - val_acc: 0.9714
Epoch 219/1000
- 4s - loss: 0.0694 - acc: 0.9749 - val_loss: 0.0720 - val_acc: 0.9709
Epoch 220/1000
- 4s - loss: 0.0698 - acc: 0.9744 - val_loss: 0.0719 - val_acc: 0.9709
Epoch 221/1000
- 4s - loss: 0.0689 - acc: 0.9745 - val_loss: 0.0717 - val_acc: 0.9720
Epoch 222/1000
- 4s - loss: 0.0680 - acc: 0.9747 - val_loss: 0.0716 - val_acc: 0.9720
Epoch 223/1000
- 4s - loss: 0.0686 - acc: 0.9751 - val_loss: 0.0715 - val_acc: 0.9720
Epoch 224/1000
- 4s - loss: 0.0678 - acc: 0.9759 - val_loss: 0.0715 - val_acc: 0.9720
Epoch 225/1000
- 4s - loss: 0.0688 - acc: 0.9752 - val_loss: 0.0714 - val_acc: 0.9720
Epoch 226/1000
- 4s - loss: 0.0680 - acc: 0.9737 - val_loss: 0.0714 - val_acc: 0.9720
Epoch 227/1000
- 4s - loss: 0.0694 - acc: 0.9745 - val_loss: 0.0714 - val_acc: 0.9720
Epoch 228/1000
- 4s - loss: 0.0694 - acc: 0.9747 - val_loss: 0.0714 - val_acc: 0.9714
Epoch 229/1000
- 4s - loss: 0.0690 - acc: 0.9754 - val_loss: 0.0712 - val_acc: 0.9720
Epoch 230/1000
- 4s - loss: 0.0682 - acc: 0.9745 - val_loss: 0.0715 - val_acc: 0.9714
Epoch 231/1000
- 4s - loss: 0.0675 - acc: 0.9756 - val_loss: 0.0710 - val_acc: 0.9720
Epoch 232/1000
- 4s - loss: 0.0690 - acc: 0.9750 - val_loss: 0.0711 - val_acc: 0.9720
Epoch 233/1000
- 4s - loss: 0.0673 - acc: 0.9752 - val_loss: 0.0709 - val_acc: 0.9720
Epoch 234/1000
- 4s - loss: 0.0670 - acc: 0.9764 - val_loss: 0.0708 - val_acc: 0.9720
Epoch 235/1000
- 4s - loss: 0.0669 - acc: 0.9752 - val_loss: 0.0708 - val_acc: 0.9726
Epoch 236/1000
- 4s - loss: 0.0673 - acc: 0.9754 - val_loss: 0.0709 - val_acc: 0.9714
Epoch 237/1000
- 4s - loss: 0.0672 - acc: 0.9752 - val_loss: 0.0707 - val_acc: 0.9720
Epoch 238/1000
- 4s - loss: 0.0672 - acc: 0.9747 - val_loss: 0.0706 - val_acc: 0.9720
Epoch 239/1000
- 4s - loss: 0.0662 - acc: 0.9754 - val_loss: 0.0706 - val_acc: 0.9720
Epoch 240/1000
- 4s - loss: 0.0669 - acc: 0.9752 - val_loss: 0.0705 - val_acc: 0.9720
Epoch 241/1000
- 4s - loss: 0.0662 - acc: 0.9756 - val_loss: 0.0704 - val_acc: 0.9726
Epoch 242/1000
- 4s - loss: 0.0667 - acc: 0.9755 - val_loss: 0.0705 - val_acc: 0.9720
Epoch 243/1000
- 4s - loss: 0.0663 - acc: 0.9750 - val_loss: 0.0705 - val_acc: 0.9714
Epoch 244/1000
- 4s - loss: 0.0654 - acc: 0.9748 - val_loss: 0.0706 - val_acc: 0.9720
Epoch 245/1000
- 4s - loss: 0.0666 - acc: 0.9757 - val_loss: 0.0702 - val_acc: 0.9726
Epoch 246/1000
- 4s - loss: 0.0662 - acc: 0.9761 - val_loss: 0.0702 - val_acc: 0.9726
Epoch 247/1000
- 4s - loss: 0.0671 - acc: 0.9749 - val_loss: 0.0701 - val_acc: 0.9731
Epoch 248/1000
- 4s - loss: 0.0660 - acc: 0.9756 - val_loss: 0.0706 - val_acc: 0.9720
Epoch 249/1000
- 4s - loss: 0.0656 - acc: 0.9759 - val_loss: 0.0700 - val_acc: 0.9731
Epoch 250/1000
- 4s - loss: 0.0670 - acc: 0.9754 - val_loss: 0.0699 - val_acc: 0.9731
Epoch 251/1000
- 4s - loss: 0.0653 - acc: 0.9757 - val_loss: 0.0698 - val_acc: 0.9726
Epoch 252/1000
- 4s - loss: 0.0667 - acc: 0.9749 - val_loss: 0.0700 - val_acc: 0.9720
Epoch 253/1000
- 4s - loss: 0.0651 - acc: 0.9756 - val_loss: 0.0699 - val_acc: 0.9731
Epoch 254/1000
- 4s - loss: 0.0667 - acc: 0.9765 - val_loss: 0.0698 - val_acc: 0.9731
Epoch 255/1000
- 4s - loss: 0.0645 - acc: 0.9761 - val_loss: 0.0699 - val_acc: 0.9720
Epoch 256/1000
- 4s - loss: 0.0657 - acc: 0.9759 - val_loss: 0.0697 - val_acc: 0.9720
Epoch 257/1000
- 4s - loss: 0.0653 - acc: 0.9760 - val_loss: 0.0695 - val_acc: 0.9731
Epoch 258/1000
- 4s - loss: 0.0640 - acc: 0.9759 - val_loss: 0.0695 - val_acc: 0.9731
Epoch 259/1000
- 4s - loss: 0.0648 - acc: 0.9766 - val_loss: 0.0697 - val_acc: 0.9726
Epoch 260/1000
- 4s - loss: 0.0641 - acc: 0.9771 - val_loss: 0.0694 - val_acc: 0.9731
Epoch 261/1000
- 4s - loss: 0.0650 - acc: 0.9756 - val_loss: 0.0693 - val_acc: 0.9726
Epoch 262/1000
- 4s - loss: 0.0648 - acc: 0.9759 - val_loss: 0.0692 - val_acc: 0.9731
Epoch 263/1000
- 4s - loss: 0.0653 - acc: 0.9754 - val_loss: 0.0691 - val_acc: 0.9731
Epoch 264/1000
- 4s - loss: 0.0651 - acc: 0.9760 - val_loss: 0.0693 - val_acc: 0.9726
Epoch 265/1000
- 4s - loss: 0.0640 - acc: 0.9763 - val_loss: 0.0691 - val_acc: 0.9731
Epoch 266/1000
- 4s - loss: 0.0640 - acc: 0.9770 - val_loss: 0.0689 - val_acc: 0.9731
Epoch 267/1000
- 4s - loss: 0.0635 - acc: 0.9756 - val_loss: 0.0690 - val_acc: 0.9726
Epoch 268/1000
- 4s - loss: 0.0637 - acc: 0.9764 - val_loss: 0.0688 - val_acc: 0.9731
Epoch 269/1000
- 4s - loss: 0.0644 - acc: 0.9768 - val_loss: 0.0688 - val_acc: 0.9726
Epoch 270/1000
- 4s - loss: 0.0637 - acc: 0.9769 - val_loss: 0.0691 - val_acc: 0.9731
Epoch 271/1000
- 4s - loss: 0.0639 - acc: 0.9771 - val_loss: 0.0688 - val_acc: 0.9726
Epoch 272/1000
- 4s - loss: 0.0622 - acc: 0.9771 - val_loss: 0.0688 - val_acc: 0.9726
Epoch 273/1000
- 4s - loss: 0.0635 - acc: 0.9768 - val_loss: 0.0688 - val_acc: 0.9731
Epoch 274/1000
- 4s - loss: 0.0635 - acc: 0.9763 - val_loss: 0.0687 - val_acc: 0.9726
Epoch 275/1000
- 4s - loss: 0.0632 - acc: 0.9771 - val_loss: 0.0685 - val_acc: 0.9726
Epoch 276/1000
- 4s - loss: 0.0643 - acc: 0.9760 - val_loss: 0.0684 - val_acc: 0.9726
Epoch 277/1000
- 4s - loss: 0.0631 - acc: 0.9770 - val_loss: 0.0686 - val_acc: 0.9737
Epoch 278/1000
- 4s - loss: 0.0627 - acc: 0.9764 - val_loss: 0.0683 - val_acc: 0.9726
Epoch 279/1000
- 4s - loss: 0.0634 - acc: 0.9771 - val_loss: 0.0685 - val_acc: 0.9726
Epoch 280/1000
- 4s - loss: 0.0631 - acc: 0.9766 - val_loss: 0.0683 - val_acc: 0.9737
Epoch 281/1000
- 4s - loss: 0.0626 - acc: 0.9771 - val_loss: 0.0683 - val_acc: 0.9726
Epoch 282/1000
- 4s - loss: 0.0628 - acc: 0.9768 - val_loss: 0.0683 - val_acc: 0.9726
Epoch 283/1000
- 4s - loss: 0.0628 - acc: 0.9771 - val_loss: 0.0682 - val_acc: 0.9726
Epoch 284/1000
- 4s - loss: 0.0621 - acc: 0.9768 - val_loss: 0.0684 - val_acc: 0.9737
Epoch 285/1000
- 4s - loss: 0.0621 - acc: 0.9768 - val_loss: 0.0682 - val_acc: 0.9731
Epoch 286/1000
- 4s - loss: 0.0623 - acc: 0.9761 - val_loss: 0.0680 - val_acc: 0.9726
Epoch 287/1000
- 4s - loss: 0.0617 - acc: 0.9778 - val_loss: 0.0681 - val_acc: 0.9726
Epoch 288/1000
- 4s - loss: 0.0627 - acc: 0.9772 - val_loss: 0.0679 - val_acc: 0.9731
Epoch 289/1000
- 4s - loss: 0.0629 - acc: 0.9773 - val_loss: 0.0681 - val_acc: 0.9731
Epoch 290/1000
- 4s - loss: 0.0620 - acc: 0.9774 - val_loss: 0.0679 - val_acc: 0.9726
Epoch 291/1000
- 4s - loss: 0.0613 - acc: 0.9775 - val_loss: 0.0679 - val_acc: 0.9731
Epoch 292/1000
- 4s - loss: 0.0627 - acc: 0.9769 - val_loss: 0.0677 - val_acc: 0.9726
Epoch 293/1000
- 4s - loss: 0.0623 - acc: 0.9770 - val_loss: 0.0677 - val_acc: 0.9726
Epoch 294/1000
- 4s - loss: 0.0613 - acc: 0.9773 - val_loss: 0.0677 - val_acc: 0.9731
Epoch 295/1000
- 4s - loss: 0.0617 - acc: 0.9771 - val_loss: 0.0679 - val_acc: 0.9737
Epoch 296/1000
- 4s - loss: 0.0621 - acc: 0.9773 - val_loss: 0.0675 - val_acc: 0.9731
Epoch 297/1000
- 4s - loss: 0.0621 - acc: 0.9772 - val_loss: 0.0676 - val_acc: 0.9731
Epoch 298/1000
- 4s - loss: 0.0607 - acc: 0.9777 - val_loss: 0.0675 - val_acc: 0.9726
Epoch 299/1000
- 4s - loss: 0.0620 - acc: 0.9764 - val_loss: 0.0676 - val_acc: 0.9731
Epoch 300/1000
- 4s - loss: 0.0608 - acc: 0.9780 - val_loss: 0.0674 - val_acc: 0.9731
Epoch 301/1000
- 4s - loss: 0.0614 - acc: 0.9768 - val_loss: 0.0676 - val_acc: 0.9737
Epoch 302/1000
- 4s - loss: 0.0620 - acc: 0.9772 - val_loss: 0.0672 - val_acc: 0.9731
Epoch 303/1000
- 4s - loss: 0.0613 - acc: 0.9776 - val_loss: 0.0675 - val_acc: 0.9743
Epoch 304/1000
- 4s - loss: 0.0596 - acc: 0.9791 - val_loss: 0.0671 - val_acc: 0.9737
Epoch 305/1000
- 4s - loss: 0.0608 - acc: 0.9777 - val_loss: 0.0673 - val_acc: 0.9731
Epoch 306/1000
- 4s - loss: 0.0598 - acc: 0.9779 - val_loss: 0.0673 - val_acc: 0.9743
Epoch 307/1000
- 4s - loss: 0.0604 - acc: 0.9782 - val_loss: 0.0671 - val_acc: 0.9737
Epoch 308/1000
- 4s - loss: 0.0603 - acc: 0.9785 - val_loss: 0.0671 - val_acc: 0.9737
Epoch 309/1000
- 4s - loss: 0.0610 - acc: 0.9784 - val_loss: 0.0670 - val_acc: 0.9749
Epoch 310/1000
- 4s - loss: 0.0603 - acc: 0.9786 - val_loss: 0.0668 - val_acc: 0.9731
Epoch 311/1000
- 4s - loss: 0.0605 - acc: 0.9777 - val_loss: 0.0671 - val_acc: 0.9743
Epoch 312/1000
- 4s - loss: 0.0605 - acc: 0.9785 - val_loss: 0.0670 - val_acc: 0.9749
Epoch 313/1000
- 4s - loss: 0.0598 - acc: 0.9783 - val_loss: 0.0670 - val_acc: 0.9743
Epoch 314/1000
- 4s - loss: 0.0602 - acc: 0.9772 - val_loss: 0.0668 - val_acc: 0.9737
Epoch 315/1000
- 4s - loss: 0.0597 - acc: 0.9782 - val_loss: 0.0669 - val_acc: 0.9749
Epoch 316/1000
- 4s - loss: 0.0602 - acc: 0.9790 - val_loss: 0.0667 - val_acc: 0.9743
Epoch 317/1000
- 4s - loss: 0.0595 - acc: 0.9796 - val_loss: 0.0668 - val_acc: 0.9743
Epoch 318/1000
- 4s - loss: 0.0599 - acc: 0.9776 - val_loss: 0.0669 - val_acc: 0.9743
Epoch 319/1000
- 4s - loss: 0.0599 - acc: 0.9783 - val_loss: 0.0669 - val_acc: 0.9743
Epoch 320/1000
- 4s - loss: 0.0596 - acc: 0.9780 - val_loss: 0.0666 - val_acc: 0.9743
Epoch 321/1000
- 4s - loss: 0.0587 - acc: 0.9781 - val_loss: 0.0667 - val_acc: 0.9743
Epoch 322/1000
- 4s - loss: 0.0598 - acc: 0.9779 - val_loss: 0.0664 - val_acc: 0.9743
Epoch 323/1000
- 4s - loss: 0.0598 - acc: 0.9792 - val_loss: 0.0665 - val_acc: 0.9743
Epoch 324/1000
- 4s - loss: 0.0588 - acc: 0.9780 - val_loss: 0.0664 - val_acc: 0.9743
Epoch 325/1000
- 4s - loss: 0.0595 - acc: 0.9782 - val_loss: 0.0664 - val_acc: 0.9743
Epoch 326/1000
- 4s - loss: 0.0588 - acc: 0.9783 - val_loss: 0.0663 - val_acc: 0.9743
Epoch 327/1000
- 4s - loss: 0.0600 - acc: 0.9771 - val_loss: 0.0663 - val_acc: 0.9743
Epoch 328/1000
- 4s - loss: 0.0591 - acc: 0.9783 - val_loss: 0.0662 - val_acc: 0.9737
Epoch 329/1000
- 4s - loss: 0.0592 - acc: 0.9777 - val_loss: 0.0663 - val_acc: 0.9749
Epoch 330/1000
- 4s - loss: 0.0588 - acc: 0.9787 - val_loss: 0.0663 - val_acc: 0.9749
Epoch 331/1000
- 4s - loss: 0.0582 - acc: 0.9802 - val_loss: 0.0663 - val_acc: 0.9743
Epoch 332/1000
- 4s - loss: 0.0579 - acc: 0.9788 - val_loss: 0.0662 - val_acc: 0.9731
Epoch 333/1000
- 4s - loss: 0.0589 - acc: 0.9784 - val_loss: 0.0662 - val_acc: 0.9731
Epoch 334/1000
- 4s - loss: 0.0593 - acc: 0.9789 - val_loss: 0.0662 - val_acc: 0.9743
Epoch 335/1000
- 4s - loss: 0.0582 - acc: 0.9793 - val_loss: 0.0661 - val_acc: 0.9754
Epoch 336/1000
- 4s - loss: 0.0579 - acc: 0.9785 - val_loss: 0.0660 - val_acc: 0.9743
Epoch 337/1000
- 4s - loss: 0.0586 - acc: 0.9788 - val_loss: 0.0662 - val_acc: 0.9737
Epoch 338/1000
- 4s - loss: 0.0582 - acc: 0.9787 - val_loss: 0.0660 - val_acc: 0.9743
Epoch 339/1000
- 4s - loss: 0.0580 - acc: 0.9797 - val_loss: 0.0659 - val_acc: 0.9743
Epoch 340/1000
- 4s - loss: 0.0580 - acc: 0.9787 - val_loss: 0.0660 - val_acc: 0.9749
Epoch 341/1000
- 4s - loss: 0.0582 - acc: 0.9782 - val_loss: 0.0660 - val_acc: 0.9737
Epoch 342/1000
- 4s - loss: 0.0581 - acc: 0.9786 - val_loss: 0.0662 - val_acc: 0.9737
Epoch 343/1000
- 4s - loss: 0.0579 - acc: 0.9790 - val_loss: 0.0658 - val_acc: 0.9737
Epoch 344/1000
- 4s - loss: 0.0565 - acc: 0.9792 - val_loss: 0.0661 - val_acc: 0.9737
Epoch 345/1000
- 4s - loss: 0.0571 - acc: 0.9786 - val_loss: 0.0659 - val_acc: 0.9749
Epoch 346/1000
- 4s - loss: 0.0578 - acc: 0.9784 - val_loss: 0.0658 - val_acc: 0.9749
Epoch 347/1000
- 4s - loss: 0.0580 - acc: 0.9784 - val_loss: 0.0656 - val_acc: 0.9737
Epoch 348/1000
- 4s - loss: 0.0565 - acc: 0.9790 - val_loss: 0.0655 - val_acc: 0.9737
Epoch 349/1000
- 4s - loss: 0.0573 - acc: 0.9796 - val_loss: 0.0655 - val_acc: 0.9737
Epoch 350/1000
- 4s - loss: 0.0573 - acc: 0.9796 - val_loss: 0.0656 - val_acc: 0.9737
Epoch 351/1000
- 4s - loss: 0.0571 - acc: 0.9785 - val_loss: 0.0655 - val_acc: 0.9731
Epoch 352/1000
- 4s - loss: 0.0569 - acc: 0.9795 - val_loss: 0.0654 - val_acc: 0.9743
Epoch 353/1000
- 4s - loss: 0.0571 - acc: 0.9793 - val_loss: 0.0654 - val_acc: 0.9743
Epoch 354/1000
- 4s - loss: 0.0566 - acc: 0.9797 - val_loss: 0.0653 - val_acc: 0.9737
Epoch 355/1000
- 4s - loss: 0.0562 - acc: 0.9787 - val_loss: 0.0653 - val_acc: 0.9737
Epoch 356/1000
- 4s - loss: 0.0566 - acc: 0.9795 - val_loss: 0.0652 - val_acc: 0.9737
Epoch 357/1000
- 4s - loss: 0.0572 - acc: 0.9800 - val_loss: 0.0654 - val_acc: 0.9749
Epoch 358/1000
- 4s - loss: 0.0564 - acc: 0.9797 - val_loss: 0.0652 - val_acc: 0.9743
Epoch 359/1000
- 4s - loss: 0.0569 - acc: 0.9795 - val_loss: 0.0651 - val_acc: 0.9737
Epoch 360/1000
- 4s - loss: 0.0564 - acc: 0.9794 - val_loss: 0.0653 - val_acc: 0.9743
Epoch 361/1000
- 4s - loss: 0.0560 - acc: 0.9808 - val_loss: 0.0651 - val_acc: 0.9743
Epoch 362/1000
- 4s - loss: 0.0563 - acc: 0.9799 - val_loss: 0.0651 - val_acc: 0.9749
Epoch 363/1000
- 4s - loss: 0.0564 - acc: 0.9790 - val_loss: 0.0653 - val_acc: 0.9737
Epoch 364/1000
- 4s - loss: 0.0564 - acc: 0.9786 - val_loss: 0.0650 - val_acc: 0.9743
Epoch 365/1000
- 4s - loss: 0.0566 - acc: 0.9800 - val_loss: 0.0650 - val_acc: 0.9743
Epoch 366/1000
- 4s - loss: 0.0558 - acc: 0.9806 - val_loss: 0.0650 - val_acc: 0.9743
Epoch 367/1000
- 4s - loss: 0.0565 - acc: 0.9799 - val_loss: 0.0650 - val_acc: 0.9743
Epoch 368/1000
- 4s - loss: 0.0555 - acc: 0.9796 - val_loss: 0.0650 - val_acc: 0.9743
Epoch 369/1000
- 4s - loss: 0.0550 - acc: 0.9798 - val_loss: 0.0650 - val_acc: 0.9737
Epoch 370/1000
- 4s - loss: 0.0557 - acc: 0.9804 - val_loss: 0.0649 - val_acc: 0.9743
Epoch 371/1000
- 4s - loss: 0.0558 - acc: 0.9799 - val_loss: 0.0649 - val_acc: 0.9743
Epoch 372/1000
- 4s - loss: 0.0553 - acc: 0.9796 - val_loss: 0.0648 - val_acc: 0.9743
Epoch 373/1000
- 4s - loss: 0.0566 - acc: 0.9795 - val_loss: 0.0651 - val_acc: 0.9743
Epoch 374/1000
- 4s - loss: 0.0555 - acc: 0.9798 - val_loss: 0.0647 - val_acc: 0.9743
Epoch 375/1000
- 4s - loss: 0.0556 - acc: 0.9802 - val_loss: 0.0648 - val_acc: 0.9743
Epoch 376/1000
- 4s - loss: 0.0555 - acc: 0.9792 - val_loss: 0.0648 - val_acc: 0.9749
Epoch 377/1000
- 4s - loss: 0.0550 - acc: 0.9793 - val_loss: 0.0648 - val_acc: 0.9743
Epoch 378/1000
- 4s - loss: 0.0558 - acc: 0.9792 - val_loss: 0.0648 - val_acc: 0.9737
Epoch 379/1000
- 4s - loss: 0.0554 - acc: 0.9799 - val_loss: 0.0650 - val_acc: 0.9743
Epoch 380/1000
- 4s - loss: 0.0548 - acc: 0.9807 - val_loss: 0.0647 - val_acc: 0.9743
Epoch 381/1000
- 4s - loss: 0.0558 - acc: 0.9796 - val_loss: 0.0646 - val_acc: 0.9737
Epoch 382/1000
- 4s - loss: 0.0546 - acc: 0.9810 - val_loss: 0.0647 - val_acc: 0.9743
Epoch 383/1000
- 4s - loss: 0.0552 - acc: 0.9798 - val_loss: 0.0646 - val_acc: 0.9737
Epoch 384/1000
- 4s - loss: 0.0549 - acc: 0.9801 - val_loss: 0.0646 - val_acc: 0.9749
Epoch 385/1000
- 4s - loss: 0.0548 - acc: 0.9804 - val_loss: 0.0645 - val_acc: 0.9749
Epoch 386/1000
- 4s - loss: 0.0542 - acc: 0.9799 - val_loss: 0.0644 - val_acc: 0.9743
Epoch 387/1000
- 4s - loss: 0.0549 - acc: 0.9801 - val_loss: 0.0645 - val_acc: 0.9743
Epoch 388/1000
- 4s - loss: 0.0543 - acc: 0.9803 - val_loss: 0.0647 - val_acc: 0.9749
Epoch 389/1000
- 4s - loss: 0.0553 - acc: 0.9792 - val_loss: 0.0648 - val_acc: 0.9749
Epoch 390/1000
- 4s - loss: 0.0556 - acc: 0.9790 - val_loss: 0.0644 - val_acc: 0.9749
Epoch 391/1000
- 4s - loss: 0.0543 - acc: 0.9798 - val_loss: 0.0642 - val_acc: 0.9743
Epoch 392/1000
- 4s - loss: 0.0539 - acc: 0.9804 - val_loss: 0.0645 - val_acc: 0.9749
Epoch 393/1000
- 4s - loss: 0.0541 - acc: 0.9805 - val_loss: 0.0642 - val_acc: 0.9743
Epoch 394/1000
- 4s - loss: 0.0539 - acc: 0.9801 - val_loss: 0.0642 - val_acc: 0.9743
Epoch 395/1000
- 4s - loss: 0.0545 - acc: 0.9803 - val_loss: 0.0642 - val_acc: 0.9743
Epoch 396/1000
- 4s - loss: 0.0532 - acc: 0.9813 - val_loss: 0.0641 - val_acc: 0.9743
Epoch 397/1000
- 4s - loss: 0.0539 - acc: 0.9802 - val_loss: 0.0644 - val_acc: 0.9743
Epoch 398/1000
- 4s - loss: 0.0538 - acc: 0.9808 - val_loss: 0.0643 - val_acc: 0.9749
Epoch 399/1000
- 4s - loss: 0.0534 - acc: 0.9813 - val_loss: 0.0644 - val_acc: 0.9749
Epoch 400/1000
- 4s - loss: 0.0548 - acc: 0.9799 - val_loss: 0.0641 - val_acc: 0.9737
Epoch 401/1000
- 4s - loss: 0.0545 - acc: 0.9798 - val_loss: 0.0641 - val_acc: 0.9743
Epoch 402/1000
- 4s - loss: 0.0540 - acc: 0.9807 - val_loss: 0.0641 - val_acc: 0.9737
Epoch 403/1000
- 4s - loss: 0.0537 - acc: 0.9808 - val_loss: 0.0641 - val_acc: 0.9749
Epoch 404/1000
- 4s - loss: 0.0535 - acc: 0.9810 - val_loss: 0.0640 - val_acc: 0.9743
Epoch 405/1000
- 4s - loss: 0.0534 - acc: 0.9801 - val_loss: 0.0639 - val_acc: 0.9743
Epoch 406/1000
- 4s - loss: 0.0535 - acc: 0.9800 - val_loss: 0.0640 - val_acc: 0.9749
Epoch 407/1000
- 4s - loss: 0.0527 - acc: 0.9808 - val_loss: 0.0640 - val_acc: 0.9749
Epoch 408/1000
- 4s - loss: 0.0533 - acc: 0.9802 - val_loss: 0.0640 - val_acc: 0.9743
Epoch 409/1000
- 4s - loss: 0.0542 - acc: 0.9801 - val_loss: 0.0639 - val_acc: 0.9749
Epoch 410/1000
- 4s - loss: 0.0532 - acc: 0.9807 - val_loss: 0.0638 - val_acc: 0.9737
Epoch 411/1000
- 4s - loss: 0.0524 - acc: 0.9810 - val_loss: 0.0638 - val_acc: 0.9749
Epoch 412/1000
- 4s - loss: 0.0521 - acc: 0.9806 - val_loss: 0.0639 - val_acc: 0.9749
Epoch 413/1000
- 4s - loss: 0.0530 - acc: 0.9813 - val_loss: 0.0637 - val_acc: 0.9737
Epoch 414/1000
- 4s - loss: 0.0525 - acc: 0.9806 - val_loss: 0.0640 - val_acc: 0.9749
Epoch 415/1000
- 4s - loss: 0.0528 - acc: 0.9806 - val_loss: 0.0638 - val_acc: 0.9743
Epoch 416/1000
- 4s - loss: 0.0533 - acc: 0.9809 - val_loss: 0.0639 - val_acc: 0.9743
Epoch 417/1000
- 4s - loss: 0.0534 - acc: 0.9804 - val_loss: 0.0637 - val_acc: 0.9749
Epoch 418/1000
- 4s - loss: 0.0525 - acc: 0.9806 - val_loss: 0.0635 - val_acc: 0.9743
Epoch 419/1000
- 4s - loss: 0.0530 - acc: 0.9811 - val_loss: 0.0638 - val_acc: 0.9754
Epoch 420/1000
- 4s - loss: 0.0522 - acc: 0.9814 - val_loss: 0.0636 - val_acc: 0.9749
Epoch 421/1000
- 4s - loss: 0.0527 - acc: 0.9805 - val_loss: 0.0636 - val_acc: 0.9743
Epoch 422/1000
- 4s - loss: 0.0522 - acc: 0.9811 - val_loss: 0.0635 - val_acc: 0.9749
Epoch 423/1000
- 4s - loss: 0.0533 - acc: 0.9807 - val_loss: 0.0635 - val_acc: 0.9749
Epoch 424/1000
- 4s - loss: 0.0520 - acc: 0.9823 - val_loss: 0.0634 - val_acc: 0.9749
Epoch 425/1000
- 4s - loss: 0.0517 - acc: 0.9812 - val_loss: 0.0634 - val_acc: 0.9749
Epoch 426/1000
- 4s - loss: 0.0518 - acc: 0.9814 - val_loss: 0.0635 - val_acc: 0.9749
Epoch 427/1000
- 4s - loss: 0.0521 - acc: 0.9811 - val_loss: 0.0635 - val_acc: 0.9749
Epoch 428/1000
- 4s - loss: 0.0526 - acc: 0.9811 - val_loss: 0.0636 - val_acc: 0.9749
Epoch 429/1000
- 4s - loss: 0.0519 - acc: 0.9813 - val_loss: 0.0635 - val_acc: 0.9749
Epoch 430/1000
- 4s - loss: 0.0517 - acc: 0.9813 - val_loss: 0.0633 - val_acc: 0.9749
Epoch 431/1000
- 4s - loss: 0.0521 - acc: 0.9806 - val_loss: 0.0632 - val_acc: 0.9743
Epoch 432/1000
- 4s - loss: 0.0523 - acc: 0.9814 - val_loss: 0.0631 - val_acc: 0.9754
Epoch 433/1000
- 4s - loss: 0.0523 - acc: 0.9813 - val_loss: 0.0632 - val_acc: 0.9749
Epoch 434/1000
- 4s - loss: 0.0512 - acc: 0.9817 - val_loss: 0.0633 - val_acc: 0.9754
Epoch 435/1000
- 4s - loss: 0.0517 - acc: 0.9808 - val_loss: 0.0636 - val_acc: 0.9754
Epoch 436/1000
- 4s - loss: 0.0523 - acc: 0.9817 - val_loss: 0.0632 - val_acc: 0.9754
Epoch 437/1000
- 4s - loss: 0.0514 - acc: 0.9820 - val_loss: 0.0631 - val_acc: 0.9749
Epoch 438/1000
- 4s - loss: 0.0519 - acc: 0.9814 - val_loss: 0.0630 - val_acc: 0.9754
Epoch 439/1000
- 4s - loss: 0.0514 - acc: 0.9815 - val_loss: 0.0629 - val_acc: 0.9754
Epoch 440/1000
- 4s - loss: 0.0519 - acc: 0.9817 - val_loss: 0.0631 - val_acc: 0.9754
Epoch 441/1000
- 4s - loss: 0.0519 - acc: 0.9804 - val_loss: 0.0629 - val_acc: 0.9754
Epoch 442/1000
- 4s - loss: 0.0513 - acc: 0.9811 - val_loss: 0.0630 - val_acc: 0.9749
Epoch 443/1000
- 4s - loss: 0.0510 - acc: 0.9809 - val_loss: 0.0629 - val_acc: 0.9754
Epoch 444/1000
- 4s - loss: 0.0513 - acc: 0.9815 - val_loss: 0.0629 - val_acc: 0.9760
Epoch 445/1000
- 4s - loss: 0.0517 - acc: 0.9813 - val_loss: 0.0630 - val_acc: 0.9749
Epoch 446/1000
- 4s - loss: 0.0512 - acc: 0.9813 - val_loss: 0.0630 - val_acc: 0.9743
Epoch 447/1000
- 4s - loss: 0.0511 - acc: 0.9830 - val_loss: 0.0628 - val_acc: 0.9754
Epoch 448/1000
- 4s - loss: 0.0512 - acc: 0.9816 - val_loss: 0.0628 - val_acc: 0.9754
Epoch 449/1000
- 4s - loss: 0.0502 - acc: 0.9817 - val_loss: 0.0629 - val_acc: 0.9760
Epoch 450/1000
- 4s - loss: 0.0509 - acc: 0.9814 - val_loss: 0.0627 - val_acc: 0.9754
Epoch 451/1000
- 4s - loss: 0.0514 - acc: 0.9804 - val_loss: 0.0627 - val_acc: 0.9754
Epoch 452/1000
- 4s - loss: 0.0511 - acc: 0.9817 - val_loss: 0.0630 - val_acc: 0.9754
Epoch 453/1000
- 4s - loss: 0.0511 - acc: 0.9817 - val_loss: 0.0631 - val_acc: 0.9754
Epoch 454/1000
- 4s - loss: 0.0505 - acc: 0.9819 - val_loss: 0.0627 - val_acc: 0.9760
Epoch 455/1000
- 4s - loss: 0.0506 - acc: 0.9814 - val_loss: 0.0626 - val_acc: 0.9760
Epoch 456/1000
- 4s - loss: 0.0503 - acc: 0.9828 - val_loss: 0.0626 - val_acc: 0.9760
Epoch 457/1000
- 4s - loss: 0.0504 - acc: 0.9820 - val_loss: 0.0627 - val_acc: 0.9754
Epoch 458/1000
- 4s - loss: 0.0512 - acc: 0.9815 - val_loss: 0.0628 - val_acc: 0.9754
Epoch 459/1000
- 4s - loss: 0.0510 - acc: 0.9808 - val_loss: 0.0626 - val_acc: 0.9754
Epoch 460/1000
- 4s - loss: 0.0500 - acc: 0.9820 - val_loss: 0.0626 - val_acc: 0.9754
Epoch 461/1000
- 4s - loss: 0.0509 - acc: 0.9820 - val_loss: 0.0625 - val_acc: 0.9754
Epoch 462/1000
- 4s - loss: 0.0499 - acc: 0.9827 - val_loss: 0.0625 - val_acc: 0.9760
Epoch 463/1000
- 4s - loss: 0.0499 - acc: 0.9823 - val_loss: 0.0623 - val_acc: 0.9760
Epoch 464/1000
- 4s - loss: 0.0501 - acc: 0.9816 - val_loss: 0.0624 - val_acc: 0.9754
Epoch 465/1000
- 4s - loss: 0.0502 - acc: 0.9809 - val_loss: 0.0624 - val_acc: 0.9754
Epoch 466/1000
- 4s - loss: 0.0498 - acc: 0.9816 - val_loss: 0.0626 - val_acc: 0.9754
Epoch 467/1000
- 4s - loss: 0.0502 - acc: 0.9817 - val_loss: 0.0626 - val_acc: 0.9754
Epoch 468/1000
- 4s - loss: 0.0504 - acc: 0.9820 - val_loss: 0.0623 - val_acc: 0.9754
Epoch 469/1000
- 4s - loss: 0.0495 - acc: 0.9819 - val_loss: 0.0628 - val_acc: 0.9760
Epoch 470/1000
- 4s - loss: 0.0506 - acc: 0.9818 - val_loss: 0.0623 - val_acc: 0.9760
Epoch 471/1000
- 4s - loss: 0.0503 - acc: 0.9822 - val_loss: 0.0622 - val_acc: 0.9749
Epoch 472/1000
- 4s - loss: 0.0496 - acc: 0.9821 - val_loss: 0.0622 - val_acc: 0.9760
Epoch 473/1000
- 4s - loss: 0.0498 - acc: 0.9827 - val_loss: 0.0623 - val_acc: 0.9754
Epoch 474/1000
- 4s - loss: 0.0481 - acc: 0.9821 - val_loss: 0.0623 - val_acc: 0.9760
Epoch 475/1000
- 4s - loss: 0.0490 - acc: 0.9813 - val_loss: 0.0624 - val_acc: 0.9754
Epoch 476/1000
- 4s - loss: 0.0492 - acc: 0.9822 - val_loss: 0.0624 - val_acc: 0.9749
Epoch 477/1000
- 4s - loss: 0.0488 - acc: 0.9822 - val_loss: 0.0623 - val_acc: 0.9754
Epoch 478/1000
- 4s - loss: 0.0489 - acc: 0.9827 - val_loss: 0.0623 - val_acc: 0.9760
Epoch 479/1000
- 4s - loss: 0.0490 - acc: 0.9821 - val_loss: 0.0623 - val_acc: 0.9760
Epoch 480/1000
- 4s - loss: 0.0496 - acc: 0.9815 - val_loss: 0.0621 - val_acc: 0.9754
Epoch 481/1000
- 4s - loss: 0.0490 - acc: 0.9819 - val_loss: 0.0621 - val_acc: 0.9766
Epoch 482/1000
- 4s - loss: 0.0495 - acc: 0.9818 - val_loss: 0.0621 - val_acc: 0.9754
Epoch 483/1000
- 4s - loss: 0.0483 - acc: 0.9827 - val_loss: 0.0620 - val_acc: 0.9760
Epoch 484/1000
- 4s - loss: 0.0494 - acc: 0.9812 - val_loss: 0.0620 - val_acc: 0.9760
Epoch 485/1000
- 4s - loss: 0.0488 - acc: 0.9816 - val_loss: 0.0620 - val_acc: 0.9760
Epoch 486/1000
- 4s - loss: 0.0493 - acc: 0.9818 - val_loss: 0.0620 - val_acc: 0.9760
Epoch 487/1000
- 4s - loss: 0.0485 - acc: 0.9821 - val_loss: 0.0620 - val_acc: 0.9760
Epoch 488/1000
- 4s - loss: 0.0485 - acc: 0.9827 - val_loss: 0.0618 - val_acc: 0.9760
Epoch 489/1000
- 4s - loss: 0.0491 - acc: 0.9824 - val_loss: 0.0618 - val_acc: 0.9760
Epoch 490/1000
- 4s - loss: 0.0486 - acc: 0.9825 - val_loss: 0.0620 - val_acc: 0.9766
Epoch 491/1000
- 4s - loss: 0.0479 - acc: 0.9827 - val_loss: 0.0619 - val_acc: 0.9760
Epoch 492/1000
- 4s - loss: 0.0473 - acc: 0.9830 - val_loss: 0.0619 - val_acc: 0.9766
Epoch 493/1000
- 4s - loss: 0.0480 - acc: 0.9828 - val_loss: 0.0617 - val_acc: 0.9754
Epoch 494/1000
- 4s - loss: 0.0484 - acc: 0.9829 - val_loss: 0.0617 - val_acc: 0.9760
Epoch 495/1000
- 4s - loss: 0.0483 - acc: 0.9829 - val_loss: 0.0617 - val_acc: 0.9766
Epoch 496/1000
- 4s - loss: 0.0483 - acc: 0.9833 - val_loss: 0.0617 - val_acc: 0.9754
Epoch 497/1000
- 4s - loss: 0.0485 - acc: 0.9825 - val_loss: 0.0617 - val_acc: 0.9760
Epoch 498/1000
- 4s - loss: 0.0486 - acc: 0.9823 - val_loss: 0.0619 - val_acc: 0.9760
Epoch 499/1000
- 4s - loss: 0.0479 - acc: 0.9829 - val_loss: 0.0616 - val_acc: 0.9760
Epoch 500/1000
- 4s - loss: 0.0482 - acc: 0.9818 - val_loss: 0.0616 - val_acc: 0.9760
Epoch 501/1000
- 4s - loss: 0.0482 - acc: 0.9820 - val_loss: 0.0614 - val_acc: 0.9754
Epoch 502/1000
- 4s - loss: 0.0486 - acc: 0.9821 - val_loss: 0.0617 - val_acc: 0.9760
Epoch 503/1000
- 4s - loss: 0.0481 - acc: 0.9817 - val_loss: 0.0614 - val_acc: 0.9760
Epoch 504/1000
- 4s - loss: 0.0487 - acc: 0.9823 - val_loss: 0.0613 - val_acc: 0.9760
Epoch 505/1000
- 4s - loss: 0.0483 - acc: 0.9828 - val_loss: 0.0615 - val_acc: 0.9766
Epoch 506/1000
- 4s - loss: 0.0474 - acc: 0.9831 - val_loss: 0.0614 - val_acc: 0.9766
Epoch 507/1000
- 4s - loss: 0.0480 - acc: 0.9829 - val_loss: 0.0615 - val_acc: 0.9760
Epoch 508/1000
- 4s - loss: 0.0474 - acc: 0.9829 - val_loss: 0.0614 - val_acc: 0.9766
Epoch 509/1000
- 4s - loss: 0.0476 - acc: 0.9830 - val_loss: 0.0615 - val_acc: 0.9766
Epoch 510/1000
- 4s - loss: 0.0483 - acc: 0.9827 - val_loss: 0.0612 - val_acc: 0.9754
Epoch 511/1000
- 4s - loss: 0.0473 - acc: 0.9829 - val_loss: 0.0618 - val_acc: 0.9754
Epoch 512/1000
- 4s - loss: 0.0468 - acc: 0.9837 - val_loss: 0.0614 - val_acc: 0.9754
Epoch 513/1000
- 4s - loss: 0.0470 - acc: 0.9835 - val_loss: 0.0613 - val_acc: 0.9754
Epoch 514/1000
- 4s - loss: 0.0470 - acc: 0.9823 - val_loss: 0.0614 - val_acc: 0.9766
Epoch 515/1000
- 4s - loss: 0.0471 - acc: 0.9824 - val_loss: 0.0612 - val_acc: 0.9749
Epoch 516/1000
- 4s - loss: 0.0470 - acc: 0.9827 - val_loss: 0.0614 - val_acc: 0.9760
Epoch 517/1000
- 4s - loss: 0.0466 - acc: 0.9820 - val_loss: 0.0617 - val_acc: 0.9760
Epoch 518/1000
- 4s - loss: 0.0466 - acc: 0.9836 - val_loss: 0.0611 - val_acc: 0.9760
Epoch 519/1000
- 4s - loss: 0.0471 - acc: 0.9836 - val_loss: 0.0611 - val_acc: 0.9760
Epoch 520/1000
- 4s - loss: 0.0482 - acc: 0.9825 - val_loss: 0.0611 - val_acc: 0.9766
Epoch 521/1000
- 4s - loss: 0.0464 - acc: 0.9830 - val_loss: 0.0611 - val_acc: 0.9749
Epoch 522/1000
- 4s - loss: 0.0466 - acc: 0.9828 - val_loss: 0.0611 - val_acc: 0.9760
Epoch 523/1000
- 4s - loss: 0.0473 - acc: 0.9829 - val_loss: 0.0611 - val_acc: 0.9760
Epoch 524/1000
- 4s - loss: 0.0470 - acc: 0.9828 - val_loss: 0.0610 - val_acc: 0.9749
Epoch 525/1000
- 4s - loss: 0.0472 - acc: 0.9827 - val_loss: 0.0612 - val_acc: 0.9754
Epoch 526/1000
- 4s - loss: 0.0471 - acc: 0.9829 - val_loss: 0.0612 - val_acc: 0.9754
Epoch 527/1000
- 4s - loss: 0.0466 - acc: 0.9839 - val_loss: 0.0610 - val_acc: 0.9754
Epoch 528/1000
- 4s - loss: 0.0457 - acc: 0.9844 - val_loss: 0.0612 - val_acc: 0.9754
Epoch 529/1000
- 4s - loss: 0.0471 - acc: 0.9834 - val_loss: 0.0609 - val_acc: 0.9760
Epoch 530/1000
- 4s - loss: 0.0472 - acc: 0.9839 - val_loss: 0.0608 - val_acc: 0.9760
Epoch 531/1000
- 4s - loss: 0.0467 - acc: 0.9823 - val_loss: 0.0608 - val_acc: 0.9766
Epoch 532/1000
- 4s - loss: 0.0473 - acc: 0.9828 - val_loss: 0.0610 - val_acc: 0.9749
Epoch 533/1000
- 4s - loss: 0.0449 - acc: 0.9837 - val_loss: 0.0609 - val_acc: 0.9749
Epoch 534/1000
- 4s - loss: 0.0467 - acc: 0.9827 - val_loss: 0.0608 - val_acc: 0.9754
Epoch 535/1000
- 4s - loss: 0.0459 - acc: 0.9836 - val_loss: 0.0608 - val_acc: 0.9754
Epoch 536/1000
- 4s - loss: 0.0464 - acc: 0.9829 - val_loss: 0.0608 - val_acc: 0.9766
Epoch 537/1000
- 4s - loss: 0.0461 - acc: 0.9831 - val_loss: 0.0608 - val_acc: 0.9754
Epoch 538/1000
- 4s - loss: 0.0456 - acc: 0.9843 - val_loss: 0.0608 - val_acc: 0.9760
Epoch 539/1000
- 4s - loss: 0.0472 - acc: 0.9830 - val_loss: 0.0608 - val_acc: 0.9749
Epoch 540/1000
- 4s - loss: 0.0457 - acc: 0.9837 - val_loss: 0.0609 - val_acc: 0.9754
Epoch 541/1000
- 4s - loss: 0.0452 - acc: 0.9846 - val_loss: 0.0608 - val_acc: 0.9754
Epoch 542/1000
- 4s - loss: 0.0457 - acc: 0.9833 - val_loss: 0.0608 - val_acc: 0.9760
Epoch 543/1000
- 4s - loss: 0.0466 - acc: 0.9838 - val_loss: 0.0607 - val_acc: 0.9754
Epoch 544/1000
- 4s - loss: 0.0457 - acc: 0.9834 - val_loss: 0.0608 - val_acc: 0.9754
Epoch 545/1000
- 4s - loss: 0.0460 - acc: 0.9834 - val_loss: 0.0605 - val_acc: 0.9766
Epoch 546/1000
- 4s - loss: 0.0460 - acc: 0.9841 - val_loss: 0.0607 - val_acc: 0.9760
Epoch 547/1000
- 4s - loss: 0.0454 - acc: 0.9834 - val_loss: 0.0607 - val_acc: 0.9760
Epoch 548/1000
- 4s - loss: 0.0462 - acc: 0.9834 - val_loss: 0.0607 - val_acc: 0.9760
Epoch 549/1000
- 4s - loss: 0.0455 - acc: 0.9839 - val_loss: 0.0606 - val_acc: 0.9754
Epoch 550/1000
- 4s - loss: 0.0463 - acc: 0.9834 - val_loss: 0.0606 - val_acc: 0.9754
Epoch 551/1000
- 4s - loss: 0.0460 - acc: 0.9839 - val_loss: 0.0606 - val_acc: 0.9760
Epoch 552/1000
- 4s - loss: 0.0453 - acc: 0.9836 - val_loss: 0.0605 - val_acc: 0.9760
Epoch 553/1000
- 4s - loss: 0.0451 - acc: 0.9837 - val_loss: 0.0603 - val_acc: 0.9766
Epoch 554/1000
- 4s - loss: 0.0451 - acc: 0.9835 - val_loss: 0.0605 - val_acc: 0.9754
Epoch 555/1000
- 4s - loss: 0.0453 - acc: 0.9832 - val_loss: 0.0606 - val_acc: 0.9760
Epoch 556/1000
- 4s - loss: 0.0445 - acc: 0.9827 - val_loss: 0.0607 - val_acc: 0.9754
Epoch 557/1000
- 4s - loss: 0.0444 - acc: 0.9834 - val_loss: 0.0607 - val_acc: 0.9760
Epoch 558/1000
- 4s - loss: 0.0456 - acc: 0.9843 - val_loss: 0.0606 - val_acc: 0.9760
Epoch 559/1000
- 4s - loss: 0.0450 - acc: 0.9839 - val_loss: 0.0603 - val_acc: 0.9760
Epoch 560/1000
- 4s - loss: 0.0454 - acc: 0.9836 - val_loss: 0.0605 - val_acc: 0.9754
Epoch 561/1000
- 4s - loss: 0.0448 - acc: 0.9844 - val_loss: 0.0602 - val_acc: 0.9760
Epoch 562/1000
- 4s - loss: 0.0453 - acc: 0.9832 - val_loss: 0.0603 - val_acc: 0.9760
Epoch 563/1000
- 4s - loss: 0.0454 - acc: 0.9833 - val_loss: 0.0602 - val_acc: 0.9754
Epoch 564/1000
- 4s - loss: 0.0449 - acc: 0.9830 - val_loss: 0.0601 - val_acc: 0.9749
Epoch 565/1000
- 4s - loss: 0.0449 - acc: 0.9842 - val_loss: 0.0604 - val_acc: 0.9760
Epoch 566/1000
- 4s - loss: 0.0446 - acc: 0.9842 - val_loss: 0.0603 - val_acc: 0.9749
Epoch 567/1000
- 4s - loss: 0.0458 - acc: 0.9836 - val_loss: 0.0604 - val_acc: 0.9754
Epoch 568/1000
- 4s - loss: 0.0446 - acc: 0.9841 - val_loss: 0.0606 - val_acc: 0.9760
Epoch 569/1000
- 4s - loss: 0.0441 - acc: 0.9843 - val_loss: 0.0604 - val_acc: 0.9754
Epoch 570/1000
- 4s - loss: 0.0447 - acc: 0.9836 - val_loss: 0.0604 - val_acc: 0.9754
Epoch 571/1000
- 4s - loss: 0.0439 - acc: 0.9844 - val_loss: 0.0604 - val_acc: 0.9749
Epoch 572/1000
- 4s - loss: 0.0460 - acc: 0.9836 - val_loss: 0.0604 - val_acc: 0.9754
Epoch 573/1000
- 4s - loss: 0.0440 - acc: 0.9838 - val_loss: 0.0603 - val_acc: 0.9749
Epoch 574/1000
- 4s - loss: 0.0445 - acc: 0.9834 - val_loss: 0.0605 - val_acc: 0.9754
Epoch 575/1000
- 4s - loss: 0.0446 - acc: 0.9835 - val_loss: 0.0603 - val_acc: 0.9754
Epoch 576/1000
- 4s - loss: 0.0447 - acc: 0.9837 - val_loss: 0.0602 - val_acc: 0.9754
Epoch 577/1000
- 4s - loss: 0.0437 - acc: 0.9837 - val_loss: 0.0601 - val_acc: 0.9754
Epoch 578/1000
- 4s - loss: 0.0441 - acc: 0.9846 - val_loss: 0.0600 - val_acc: 0.9754
Epoch 579/1000
- 4s - loss: 0.0447 - acc: 0.9841 - val_loss: 0.0601 - val_acc: 0.9754
Epoch 580/1000
- 4s - loss: 0.0440 - acc: 0.9841 - val_loss: 0.0602 - val_acc: 0.9749
Epoch 581/1000
- 4s - loss: 0.0439 - acc: 0.9842 - val_loss: 0.0601 - val_acc: 0.9749
Epoch 582/1000
- 4s - loss: 0.0440 - acc: 0.9843 - val_loss: 0.0600 - val_acc: 0.9754
Epoch 583/1000
- 4s - loss: 0.0439 - acc: 0.9842 - val_loss: 0.0600 - val_acc: 0.9760
Epoch 584/1000
- 4s - loss: 0.0446 - acc: 0.9835 - val_loss: 0.0600 - val_acc: 0.9749
Epoch 585/1000
- 4s - loss: 0.0443 - acc: 0.9839 - val_loss: 0.0600 - val_acc: 0.9754
Epoch 586/1000
- 4s - loss: 0.0446 - acc: 0.9834 - val_loss: 0.0600 - val_acc: 0.9749
Epoch 587/1000
- 4s - loss: 0.0439 - acc: 0.9841 - val_loss: 0.0600 - val_acc: 0.9754
Epoch 588/1000
- 4s - loss: 0.0439 - acc: 0.9844 - val_loss: 0.0601 - val_acc: 0.9749
Epoch 589/1000
- 4s - loss: 0.0438 - acc: 0.9845 - val_loss: 0.0600 - val_acc: 0.9760
Epoch 590/1000
- 4s - loss: 0.0435 - acc: 0.9843 - val_loss: 0.0605 - val_acc: 0.9766
Epoch 591/1000
- 4s - loss: 0.0444 - acc: 0.9836 - val_loss: 0.0599 - val_acc: 0.9749
Epoch 592/1000
- 4s - loss: 0.0438 - acc: 0.9839 - val_loss: 0.0600 - val_acc: 0.9754
Epoch 593/1000
- 4s - loss: 0.0437 - acc: 0.9846 - val_loss: 0.0599 - val_acc: 0.9749
Epoch 594/1000
- 4s - loss: 0.0435 - acc: 0.9849 - val_loss: 0.0598 - val_acc: 0.9760
Epoch 595/1000
- 4s - loss: 0.0432 - acc: 0.9850 - val_loss: 0.0599 - val_acc: 0.9749
Epoch 596/1000
- 4s - loss: 0.0428 - acc: 0.9846 - val_loss: 0.0598 - val_acc: 0.9743
Epoch 597/1000
- 4s - loss: 0.0434 - acc: 0.9841 - val_loss: 0.0597 - val_acc: 0.9749
Epoch 598/1000
- 4s - loss: 0.0435 - acc: 0.9846 - val_loss: 0.0597 - val_acc: 0.9749
Epoch 599/1000
- 4s - loss: 0.0435 - acc: 0.9842 - val_loss: 0.0598 - val_acc: 0.9749
Epoch 600/1000
- 4s - loss: 0.0443 - acc: 0.9846 - val_loss: 0.0598 - val_acc: 0.9743
Epoch 601/1000
- 4s - loss: 0.0426 - acc: 0.9855 - val_loss: 0.0598 - val_acc: 0.9743
Epoch 602/1000
- 4s - loss: 0.0427 - acc: 0.9842 - val_loss: 0.0597 - val_acc: 0.9749
Epoch 603/1000
- 4s - loss: 0.0433 - acc: 0.9844 - val_loss: 0.0598 - val_acc: 0.9749
Epoch 604/1000
- 4s - loss: 0.0436 - acc: 0.9846 - val_loss: 0.0598 - val_acc: 0.9743
Epoch 605/1000
- 4s - loss: 0.0434 - acc: 0.9843 - val_loss: 0.0597 - val_acc: 0.9749
Epoch 606/1000
- 4s - loss: 0.0441 - acc: 0.9839 - val_loss: 0.0598 - val_acc: 0.9749
Epoch 607/1000
- 4s - loss: 0.0431 - acc: 0.9843 - val_loss: 0.0598 - val_acc: 0.9749
Epoch 608/1000
- 4s - loss: 0.0431 - acc: 0.9844 - val_loss: 0.0596 - val_acc: 0.9749
Epoch 609/1000
- 4s - loss: 0.0420 - acc: 0.9849 - val_loss: 0.0596 - val_acc: 0.9749
Epoch 610/1000
- 4s - loss: 0.0425 - acc: 0.9854 - val_loss: 0.0600 - val_acc: 0.9760
Epoch 611/1000
- 4s - loss: 0.0429 - acc: 0.9844 - val_loss: 0.0595 - val_acc: 0.9737
Epoch 612/1000
- 4s - loss: 0.0439 - acc: 0.9837 - val_loss: 0.0596 - val_acc: 0.9743
Epoch 613/1000
- 4s - loss: 0.0421 - acc: 0.9851 - val_loss: 0.0597 - val_acc: 0.9749
Epoch 614/1000
- 4s - loss: 0.0429 - acc: 0.9843 - val_loss: 0.0597 - val_acc: 0.9749
Epoch 615/1000
- 4s - loss: 0.0432 - acc: 0.9848 - val_loss: 0.0596 - val_acc: 0.9749
Epoch 616/1000
- 4s - loss: 0.0422 - acc: 0.9851 - val_loss: 0.0597 - val_acc: 0.9737
Epoch 617/1000
- 4s - loss: 0.0433 - acc: 0.9846 - val_loss: 0.0597 - val_acc: 0.9749
Epoch 618/1000
- 4s - loss: 0.0425 - acc: 0.9838 - val_loss: 0.0594 - val_acc: 0.9754
Epoch 619/1000
- 4s - loss: 0.0417 - acc: 0.9850 - val_loss: 0.0595 - val_acc: 0.9754
Epoch 620/1000
- 4s - loss: 0.0431 - acc: 0.9841 - val_loss: 0.0594 - val_acc: 0.9754
Epoch 621/1000
- 4s - loss: 0.0418 - acc: 0.9858 - val_loss: 0.0595 - val_acc: 0.9754
Epoch 622/1000
- 4s - loss: 0.0424 - acc: 0.9839 - val_loss: 0.0594 - val_acc: 0.9743
Epoch 623/1000
- 4s - loss: 0.0428 - acc: 0.9848 - val_loss: 0.0594 - val_acc: 0.9737
Epoch 624/1000
- 4s - loss: 0.0424 - acc: 0.9848 - val_loss: 0.0595 - val_acc: 0.9749
Epoch 625/1000
- 4s - loss: 0.0419 - acc: 0.9847 - val_loss: 0.0595 - val_acc: 0.9743
Epoch 626/1000
- 4s - loss: 0.0417 - acc: 0.9861 - val_loss: 0.0594 - val_acc: 0.9749
Epoch 627/1000
- 4s - loss: 0.0420 - acc: 0.9846 - val_loss: 0.0596 - val_acc: 0.9749
Epoch 628/1000
- 4s - loss: 0.0421 - acc: 0.9851 - val_loss: 0.0594 - val_acc: 0.9743
Epoch 629/1000
- 4s - loss: 0.0417 - acc: 0.9850 - val_loss: 0.0595 - val_acc: 0.9743
Epoch 630/1000
- 4s - loss: 0.0428 - acc: 0.9846 - val_loss: 0.0600 - val_acc: 0.9766
Epoch 631/1000
- 4s - loss: 0.0423 - acc: 0.9849 - val_loss: 0.0593 - val_acc: 0.9743
Epoch 632/1000
- 4s - loss: 0.0418 - acc: 0.9844 - val_loss: 0.0594 - val_acc: 0.9754
Epoch 633/1000
- 4s - loss: 0.0421 - acc: 0.9852 - val_loss: 0.0593 - val_acc: 0.9743
Epoch 634/1000
- 4s - loss: 0.0410 - acc: 0.9847 - val_loss: 0.0593 - val_acc: 0.9743
Epoch 635/1000
- 4s - loss: 0.0420 - acc: 0.9848 - val_loss: 0.0593 - val_acc: 0.9737
Epoch 636/1000
- 4s - loss: 0.0422 - acc: 0.9846 - val_loss: 0.0594 - val_acc: 0.9749
Epoch 637/1000
- 4s - loss: 0.0415 - acc: 0.9853 - val_loss: 0.0593 - val_acc: 0.9737
Epoch 638/1000
- 4s - loss: 0.0420 - acc: 0.9851 - val_loss: 0.0593 - val_acc: 0.9749
Epoch 639/1000
- 4s - loss: 0.0421 - acc: 0.9848 - val_loss: 0.0593 - val_acc: 0.9743
Epoch 640/1000
- 4s - loss: 0.0421 - acc: 0.9845 - val_loss: 0.0593 - val_acc: 0.9737
Epoch 641/1000
- 4s - loss: 0.0413 - acc: 0.9853 - val_loss: 0.0594 - val_acc: 0.9754
Epoch 642/1000
- 4s - loss: 0.0420 - acc: 0.9853 - val_loss: 0.0593 - val_acc: 0.9754
Epoch 643/1000
- 4s - loss: 0.0409 - acc: 0.9859 - val_loss: 0.0594 - val_acc: 0.9766
Epoch 644/1000
- 4s - loss: 0.0412 - acc: 0.9854 - val_loss: 0.0592 - val_acc: 0.9754
Epoch 645/1000
- 4s - loss: 0.0418 - acc: 0.9846 - val_loss: 0.0592 - val_acc: 0.9743
Epoch 646/1000
- 4s - loss: 0.0416 - acc: 0.9855 - val_loss: 0.0592 - val_acc: 0.9743
Epoch 647/1000
- 4s - loss: 0.0408 - acc: 0.9857 - val_loss: 0.0592 - val_acc: 0.9749
Epoch 648/1000
- 4s - loss: 0.0409 - acc: 0.9851 - val_loss: 0.0590 - val_acc: 0.9743
Epoch 649/1000
- 4s - loss: 0.0411 - acc: 0.9853 - val_loss: 0.0594 - val_acc: 0.9754
Epoch 650/1000
- 4s - loss: 0.0428 - acc: 0.9843 - val_loss: 0.0591 - val_acc: 0.9749
Epoch 651/1000
- 4s - loss: 0.0414 - acc: 0.9850 - val_loss: 0.0592 - val_acc: 0.9754
Epoch 652/1000
- 4s - loss: 0.0405 - acc: 0.9856 - val_loss: 0.0591 - val_acc: 0.9766
Epoch 653/1000
- 4s - loss: 0.0407 - acc: 0.9850 - val_loss: 0.0590 - val_acc: 0.9743
Epoch 654/1000
- 4s - loss: 0.0413 - acc: 0.9849 - val_loss: 0.0592 - val_acc: 0.9754
Epoch 655/1000
- 4s - loss: 0.0410 - acc: 0.9844 - val_loss: 0.0592 - val_acc: 0.9749
Epoch 656/1000
- 4s - loss: 0.0411 - acc: 0.9851 - val_loss: 0.0590 - val_acc: 0.9749
Epoch 657/1000
- 4s - loss: 0.0408 - acc: 0.9858 - val_loss: 0.0590 - val_acc: 0.9749
Epoch 658/1000
- 4s - loss: 0.0416 - acc: 0.9845 - val_loss: 0.0590 - val_acc: 0.9743
Epoch 659/1000
- 4s - loss: 0.0408 - acc: 0.9860 - val_loss: 0.0589 - val_acc: 0.9743
Epoch 660/1000
- 4s - loss: 0.0409 - acc: 0.9862 - val_loss: 0.0589 - val_acc: 0.9749
Epoch 661/1000
- 4s - loss: 0.0406 - acc: 0.9853 - val_loss: 0.0589 - val_acc: 0.9749
Epoch 662/1000
- 4s - loss: 0.0406 - acc: 0.9859 - val_loss: 0.0590 - val_acc: 0.9743
Epoch 663/1000
- 4s - loss: 0.0407 - acc: 0.9858 - val_loss: 0.0591 - val_acc: 0.9760
Epoch 664/1000
- 4s - loss: 0.0407 - acc: 0.9848 - val_loss: 0.0589 - val_acc: 0.9743
Epoch 665/1000
- 4s - loss: 0.0399 - acc: 0.9853 - val_loss: 0.0591 - val_acc: 0.9737
Epoch 666/1000
- 4s - loss: 0.0405 - acc: 0.9848 - val_loss: 0.0588 - val_acc: 0.9743
Epoch 667/1000
- 4s - loss: 0.0405 - acc: 0.9855 - val_loss: 0.0588 - val_acc: 0.9749
Epoch 668/1000
- 4s - loss: 0.0407 - acc: 0.9849 - val_loss: 0.0588 - val_acc: 0.9743
Epoch 669/1000
- 4s - loss: 0.0385 - acc: 0.9873 - val_loss: 0.0588 - val_acc: 0.9749
Epoch 670/1000
- 4s - loss: 0.0395 - acc: 0.9852 - val_loss: 0.0588 - val_acc: 0.9743
Epoch 671/1000
- 4s - loss: 0.0407 - acc: 0.9850 - val_loss: 0.0586 - val_acc: 0.9743
Epoch 672/1000
- 4s - loss: 0.0399 - acc: 0.9853 - val_loss: 0.0586 - val_acc: 0.9743
Epoch 673/1000
- 4s - loss: 0.0413 - acc: 0.9849 - val_loss: 0.0587 - val_acc: 0.9737
Epoch 674/1000
- 4s - loss: 0.0404 - acc: 0.9858 - val_loss: 0.0587 - val_acc: 0.9743
Epoch 675/1000
- 4s - loss: 0.0399 - acc: 0.9861 - val_loss: 0.0587 - val_acc: 0.9749
Epoch 676/1000
- 4s - loss: 0.0397 - acc: 0.9860 - val_loss: 0.0588 - val_acc: 0.9743
Epoch 677/1000
- 4s - loss: 0.0402 - acc: 0.9858 - val_loss: 0.0588 - val_acc: 0.9743
Epoch 678/1000
- 4s - loss: 0.0398 - acc: 0.9853 - val_loss: 0.0588 - val_acc: 0.9743
Epoch 679/1000
- 4s - loss: 0.0402 - acc: 0.9846 - val_loss: 0.0587 - val_acc: 0.9743
Epoch 680/1000
- 4s - loss: 0.0402 - acc: 0.9853 - val_loss: 0.0586 - val_acc: 0.9749
Epoch 681/1000
- 4s - loss: 0.0399 - acc: 0.9860 - val_loss: 0.0586 - val_acc: 0.9743
Epoch 682/1000
- 4s - loss: 0.0396 - acc: 0.9867 - val_loss: 0.0586 - val_acc: 0.9749
Epoch 683/1000
- 4s - loss: 0.0406 - acc: 0.9856 - val_loss: 0.0586 - val_acc: 0.9754
Epoch 684/1000
- 4s - loss: 0.0402 - acc: 0.9856 - val_loss: 0.0585 - val_acc: 0.9749
Epoch 685/1000
- 4s - loss: 0.0395 - acc: 0.9867 - val_loss: 0.0585 - val_acc: 0.9743
Epoch 686/1000
- 4s - loss: 0.0395 - acc: 0.9853 - val_loss: 0.0586 - val_acc: 0.9754
Epoch 687/1000
- 4s - loss: 0.0398 - acc: 0.9851 - val_loss: 0.0587 - val_acc: 0.9760
Epoch 688/1000
- 4s - loss: 0.0396 - acc: 0.9857 - val_loss: 0.0587 - val_acc: 0.9743
Epoch 689/1000
- 4s - loss: 0.0393 - acc: 0.9863 - val_loss: 0.0585 - val_acc: 0.9743
Epoch 690/1000
- 4s - loss: 0.0392 - acc: 0.9855 - val_loss: 0.0587 - val_acc: 0.9749
Epoch 691/1000
- 4s - loss: 0.0392 - acc: 0.9861 - val_loss: 0.0586 - val_acc: 0.9743
Epoch 692/1000
- 4s - loss: 0.0400 - acc: 0.9857 - val_loss: 0.0586 - val_acc: 0.9743
Epoch 693/1000
- 4s - loss: 0.0397 - acc: 0.9855 - val_loss: 0.0584 - val_acc: 0.9749
Epoch 694/1000
- 4s - loss: 0.0403 - acc: 0.9857 - val_loss: 0.0584 - val_acc: 0.9749
Epoch 695/1000
- 4s - loss: 0.0392 - acc: 0.9874 - val_loss: 0.0584 - val_acc: 0.9754
Epoch 696/1000
- 4s - loss: 0.0390 - acc: 0.9868 - val_loss: 0.0585 - val_acc: 0.9743
Epoch 697/1000
- 4s - loss: 0.0396 - acc: 0.9858 - val_loss: 0.0590 - val_acc: 0.9771
Epoch 698/1000
- 4s - loss: 0.0387 - acc: 0.9862 - val_loss: 0.0587 - val_acc: 0.9760
Epoch 699/1000
- 4s - loss: 0.0386 - acc: 0.9855 - val_loss: 0.0588 - val_acc: 0.9766
Epoch 700/1000
- 4s - loss: 0.0392 - acc: 0.9854 - val_loss: 0.0585 - val_acc: 0.9754
Epoch 701/1000
- 4s - loss: 0.0391 - acc: 0.9860 - val_loss: 0.0585 - val_acc: 0.9754
Epoch 702/1000
- 4s - loss: 0.0389 - acc: 0.9860 - val_loss: 0.0584 - val_acc: 0.9743
Epoch 703/1000
- 4s - loss: 0.0390 - acc: 0.9863 - val_loss: 0.0583 - val_acc: 0.9766
Epoch 704/1000
- 4s - loss: 0.0389 - acc: 0.9857 - val_loss: 0.0585 - val_acc: 0.9766
Epoch 705/1000
- 4s - loss: 0.0396 - acc: 0.9855 - val_loss: 0.0586 - val_acc: 0.9766
Epoch 706/1000
- 4s - loss: 0.0395 - acc: 0.9863 - val_loss: 0.0589 - val_acc: 0.9766
Epoch 707/1000
- 4s - loss: 0.0391 - acc: 0.9869 - val_loss: 0.0584 - val_acc: 0.9749
Epoch 708/1000
- 4s - loss: 0.0395 - acc: 0.9853 - val_loss: 0.0586 - val_acc: 0.9760
Epoch 709/1000
- 4s - loss: 0.0396 - acc: 0.9862 - val_loss: 0.0582 - val_acc: 0.9754
Epoch 710/1000
- 4s - loss: 0.0390 - acc: 0.9857 - val_loss: 0.0582 - val_acc: 0.9749
Epoch 711/1000
- 4s - loss: 0.0386 - acc: 0.9865 - val_loss: 0.0583 - val_acc: 0.9766
Epoch 712/1000
- 4s - loss: 0.0395 - acc: 0.9858 - val_loss: 0.0582 - val_acc: 0.9749
Epoch 713/1000
- 4s - loss: 0.0389 - acc: 0.9854 - val_loss: 0.0585 - val_acc: 0.9743
Epoch 714/1000
- 4s - loss: 0.0388 - acc: 0.9865 - val_loss: 0.0582 - val_acc: 0.9743
Epoch 715/1000
- 4s - loss: 0.0393 - acc: 0.9851 - val_loss: 0.0584 - val_acc: 0.9749
Epoch 716/1000
- 4s - loss: 0.0382 - acc: 0.9862 - val_loss: 0.0585 - val_acc: 0.9743
Epoch 717/1000
- 4s - loss: 0.0382 - acc: 0.9868 - val_loss: 0.0585 - val_acc: 0.9760
Epoch 718/1000
- 4s - loss: 0.0389 - acc: 0.9856 - val_loss: 0.0584 - val_acc: 0.9749
Epoch 719/1000
- 4s - loss: 0.0383 - acc: 0.9859 - val_loss: 0.0584 - val_acc: 0.9766
Epoch 720/1000
- 4s - loss: 0.0382 - acc: 0.9869 - val_loss: 0.0580 - val_acc: 0.9749
Epoch 721/1000
- 4s - loss: 0.0385 - acc: 0.9868 - val_loss: 0.0582 - val_acc: 0.9760
Epoch 722/1000
- 4s - loss: 0.0380 - acc: 0.9870 - val_loss: 0.0581 - val_acc: 0.9743
Epoch 723/1000
- 4s - loss: 0.0387 - acc: 0.9866 - val_loss: 0.0581 - val_acc: 0.9749
Epoch 724/1000
- 4s - loss: 0.0382 - acc: 0.9861 - val_loss: 0.0583 - val_acc: 0.9777
Epoch 725/1000
- 4s - loss: 0.0377 - acc: 0.9868 - val_loss: 0.0584 - val_acc: 0.9760
Epoch 726/1000
- 4s - loss: 0.0387 - acc: 0.9860 - val_loss: 0.0581 - val_acc: 0.9760
Epoch 727/1000
- 4s - loss: 0.0376 - acc: 0.9858 - val_loss: 0.0582 - val_acc: 0.9749
Epoch 728/1000
- 4s - loss: 0.0384 - acc: 0.9860 - val_loss: 0.0582 - val_acc: 0.9749
Epoch 729/1000
- 4s - loss: 0.0377 - acc: 0.9859 - val_loss: 0.0581 - val_acc: 0.9743
Epoch 730/1000
- 4s - loss: 0.0383 - acc: 0.9867 - val_loss: 0.0581 - val_acc: 0.9743
Epoch 731/1000
- 4s - loss: 0.0387 - acc: 0.9854 - val_loss: 0.0581 - val_acc: 0.9760
Epoch 732/1000
- 4s - loss: 0.0379 - acc: 0.9870 - val_loss: 0.0583 - val_acc: 0.9760
Epoch 733/1000
- 4s - loss: 0.0379 - acc: 0.9863 - val_loss: 0.0582 - val_acc: 0.9766
Epoch 734/1000
- 4s - loss: 0.0379 - acc: 0.9867 - val_loss: 0.0580 - val_acc: 0.9754
Epoch 735/1000
- 4s - loss: 0.0398 - acc: 0.9856 - val_loss: 0.0579 - val_acc: 0.9754
Epoch 736/1000
- 4s - loss: 0.0376 - acc: 0.9869 - val_loss: 0.0580 - val_acc: 0.9754
Epoch 737/1000
- 4s - loss: 0.0375 - acc: 0.9867 - val_loss: 0.0579 - val_acc: 0.9754
Epoch 738/1000
- 4s - loss: 0.0377 - acc: 0.9865 - val_loss: 0.0581 - val_acc: 0.9760
Epoch 739/1000
- 4s - loss: 0.0384 - acc: 0.9867 - val_loss: 0.0580 - val_acc: 0.9749
Epoch 740/1000
- 4s - loss: 0.0364 - acc: 0.9876 - val_loss: 0.0584 - val_acc: 0.9766
Epoch 741/1000
- 4s - loss: 0.0380 - acc: 0.9857 - val_loss: 0.0581 - val_acc: 0.9766
Epoch 742/1000
- 4s - loss: 0.0378 - acc: 0.9865 - val_loss: 0.0578 - val_acc: 0.9760
Epoch 743/1000
- 4s - loss: 0.0371 - acc: 0.9870 - val_loss: 0.0578 - val_acc: 0.9754
Epoch 744/1000
- 4s - loss: 0.0378 - acc: 0.9870 - val_loss: 0.0576 - val_acc: 0.9760
Epoch 745/1000
- 4s - loss: 0.0388 - acc: 0.9866 - val_loss: 0.0575 - val_acc: 0.9754
Epoch 746/1000
- 4s - loss: 0.0376 - acc: 0.9869 - val_loss: 0.0584 - val_acc: 0.9766
Epoch 747/1000
- 4s - loss: 0.0374 - acc: 0.9871 - val_loss: 0.0583 - val_acc: 0.9749
Epoch 748/1000
- 4s - loss: 0.0376 - acc: 0.9863 - val_loss: 0.0579 - val_acc: 0.9743
Epoch 749/1000
- 4s - loss: 0.0371 - acc: 0.9871 - val_loss: 0.0579 - val_acc: 0.9749
Epoch 750/1000
- 4s - loss: 0.0378 - acc: 0.9865 - val_loss: 0.0578 - val_acc: 0.9749
Epoch 751/1000
- 4s - loss: 0.0375 - acc: 0.9872 - val_loss: 0.0580 - val_acc: 0.9743
Epoch 752/1000
- 4s - loss: 0.0375 - acc: 0.9862 - val_loss: 0.0579 - val_acc: 0.9749
Epoch 753/1000
- 4s - loss: 0.0369 - acc: 0.9865 - val_loss: 0.0579 - val_acc: 0.9754
Epoch 754/1000
- 4s - loss: 0.0375 - acc: 0.9867 - val_loss: 0.0577 - val_acc: 0.9754
Epoch 755/1000
- 4s - loss: 0.0374 - acc: 0.9872 - val_loss: 0.0576 - val_acc: 0.9749
Epoch 756/1000
- 4s - loss: 0.0377 - acc: 0.9865 - val_loss: 0.0577 - val_acc: 0.9766
Epoch 757/1000
- 4s - loss: 0.0374 - acc: 0.9870 - val_loss: 0.0579 - val_acc: 0.9743
Epoch 758/1000
- 4s - loss: 0.0377 - acc: 0.9870 - val_loss: 0.0580 - val_acc: 0.9749
Epoch 759/1000
- 4s - loss: 0.0368 - acc: 0.9872 - val_loss: 0.0580 - val_acc: 0.9749
Epoch 760/1000
- 4s - loss: 0.0373 - acc: 0.9863 - val_loss: 0.0578 - val_acc: 0.9771
Epoch 761/1000
- 4s - loss: 0.0371 - acc: 0.9872 - val_loss: 0.0581 - val_acc: 0.9760
Epoch 762/1000
- 4s - loss: 0.0369 - acc: 0.9867 - val_loss: 0.0578 - val_acc: 0.9766
Epoch 763/1000
- 4s - loss: 0.0369 - acc: 0.9864 - val_loss: 0.0577 - val_acc: 0.9754
Epoch 764/1000
- 4s - loss: 0.0374 - acc: 0.9867 - val_loss: 0.0581 - val_acc: 0.9760
Epoch 765/1000
- 4s - loss: 0.0360 - acc: 0.9869 - val_loss: 0.0578 - val_acc: 0.9766
Epoch 766/1000
- 4s - loss: 0.0362 - acc: 0.9876 - val_loss: 0.0575 - val_acc: 0.9760
Epoch 767/1000
- 4s - loss: 0.0364 - acc: 0.9875 - val_loss: 0.0576 - val_acc: 0.9760
Epoch 768/1000
- 4s - loss: 0.0367 - acc: 0.9873 - val_loss: 0.0577 - val_acc: 0.9760
Epoch 769/1000
- 4s - loss: 0.0364 - acc: 0.9876 - val_loss: 0.0578 - val_acc: 0.9766
Epoch 770/1000
- 4s - loss: 0.0372 - acc: 0.9867 - val_loss: 0.0575 - val_acc: 0.9754
Epoch 771/1000
- 4s - loss: 0.0360 - acc: 0.9870 - val_loss: 0.0578 - val_acc: 0.9749
Epoch 772/1000
- 4s - loss: 0.0364 - acc: 0.9879 - val_loss: 0.0579 - val_acc: 0.9749
Epoch 773/1000
- 4s - loss: 0.0371 - acc: 0.9866 - val_loss: 0.0580 - val_acc: 0.9760
Epoch 774/1000
- 4s - loss: 0.0371 - acc: 0.9863 - val_loss: 0.0576 - val_acc: 0.9760
Epoch 775/1000
- 4s - loss: 0.0363 - acc: 0.9874 - val_loss: 0.0578 - val_acc: 0.9754
Epoch 776/1000
- 4s - loss: 0.0368 - acc: 0.9875 - val_loss: 0.0579 - val_acc: 0.9760
Epoch 777/1000
- 4s - loss: 0.0370 - acc: 0.9867 - val_loss: 0.0577 - val_acc: 0.9760
Epoch 778/1000
- 4s - loss: 0.0353 - acc: 0.9879 - val_loss: 0.0578 - val_acc: 0.9749
Epoch 779/1000
- 4s - loss: 0.0362 - acc: 0.9878 - val_loss: 0.0577 - val_acc: 0.9760
Epoch 780/1000
- 4s - loss: 0.0366 - acc: 0.9870 - val_loss: 0.0576 - val_acc: 0.9760
Epoch 781/1000
- 4s - loss: 0.0353 - acc: 0.9884 - val_loss: 0.0575 - val_acc: 0.9760
Epoch 782/1000
- 4s - loss: 0.0360 - acc: 0.9881 - val_loss: 0.0576 - val_acc: 0.9760
Epoch 783/1000
- 4s - loss: 0.0369 - acc: 0.9870 - val_loss: 0.0577 - val_acc: 0.9766
Epoch 784/1000
- 4s - loss: 0.0359 - acc: 0.9868 - val_loss: 0.0578 - val_acc: 0.9754
Epoch 785/1000
- 4s - loss: 0.0363 - acc: 0.9872 - val_loss: 0.0575 - val_acc: 0.9766
Epoch 786/1000
- 4s - loss: 0.0356 - acc: 0.9883 - val_loss: 0.0576 - val_acc: 0.9749
Epoch 787/1000
- 4s - loss: 0.0367 - acc: 0.9876 - val_loss: 0.0575 - val_acc: 0.9754
Epoch 788/1000
- 4s - loss: 0.0356 - acc: 0.9875 - val_loss: 0.0577 - val_acc: 0.9749
Epoch 789/1000
- 4s - loss: 0.0362 - acc: 0.9873 - val_loss: 0.0577 - val_acc: 0.9760
Epoch 790/1000
- 4s - loss: 0.0363 - acc: 0.9870 - val_loss: 0.0576 - val_acc: 0.9766
Epoch 791/1000
- 4s - loss: 0.0365 - acc: 0.9874 - val_loss: 0.0577 - val_acc: 0.9754
Epoch 792/1000
- 4s - loss: 0.0355 - acc: 0.9877 - val_loss: 0.0579 - val_acc: 0.9766
Epoch 793/1000
- 4s - loss: 0.0353 - acc: 0.9878 - val_loss: 0.0576 - val_acc: 0.9760
Epoch 794/1000
- 4s - loss: 0.0365 - acc: 0.9866 - val_loss: 0.0576 - val_acc: 0.9760
Epoch 795/1000
- 4s - loss: 0.0352 - acc: 0.9888 - val_loss: 0.0578 - val_acc: 0.9760
Epoch 796/1000
- 4s - loss: 0.0364 - acc: 0.9875 - val_loss: 0.0577 - val_acc: 0.9760
Epoch 797/1000
- 4s - loss: 0.0361 - acc: 0.9876 - val_loss: 0.0577 - val_acc: 0.9760
Epoch 798/1000
- 4s - loss: 0.0351 - acc: 0.9875 - val_loss: 0.0576 - val_acc: 0.9760
Epoch 799/1000
- 4s - loss: 0.0359 - acc: 0.9863 - val_loss: 0.0576 - val_acc: 0.9760
Epoch 800/1000
- 4s - loss: 0.0364 - acc: 0.9868 - val_loss: 0.0574 - val_acc: 0.9760
Epoch 801/1000
- 4s - loss: 0.0353 - acc: 0.9879 - val_loss: 0.0575 - val_acc: 0.9754
Epoch 802/1000
- 4s - loss: 0.0366 - acc: 0.9870 - val_loss: 0.0573 - val_acc: 0.9760
Epoch 803/1000
- 4s - loss: 0.0360 - acc: 0.9879 - val_loss: 0.0574 - val_acc: 0.9771
Epoch 804/1000
- 4s - loss: 0.0352 - acc: 0.9876 - val_loss: 0.0574 - val_acc: 0.9766
Epoch 805/1000
- 4s - loss: 0.0356 - acc: 0.9879 - val_loss: 0.0573 - val_acc: 0.9760
Epoch 806/1000
- 4s - loss: 0.0361 - acc: 0.9872 - val_loss: 0.0578 - val_acc: 0.9760
Epoch 807/1000
- 4s - loss: 0.0349 - acc: 0.9884 - val_loss: 0.0576 - val_acc: 0.9749
Epoch 808/1000
- 4s - loss: 0.0357 - acc: 0.9880 - val_loss: 0.0579 - val_acc: 0.9760
Epoch 809/1000
- 4s - loss: 0.0353 - acc: 0.9876 - val_loss: 0.0575 - val_acc: 0.9766
Epoch 810/1000
- 4s - loss: 0.0353 - acc: 0.9884 - val_loss: 0.0576 - val_acc: 0.9766
Epoch 811/1000
- 4s - loss: 0.0360 - acc: 0.9880 - val_loss: 0.0575 - val_acc: 0.9760
Epoch 812/1000
- 4s - loss: 0.0344 - acc: 0.9888 - val_loss: 0.0575 - val_acc: 0.9766
Epoch 813/1000
- 4s - loss: 0.0358 - acc: 0.9871 - val_loss: 0.0576 - val_acc: 0.9760
Epoch 814/1000
- 4s - loss: 0.0354 - acc: 0.9872 - val_loss: 0.0575 - val_acc: 0.9760
Epoch 815/1000
- 4s - loss: 0.0362 - acc: 0.9874 - val_loss: 0.0570 - val_acc: 0.9771
Epoch 816/1000
- 4s - loss: 0.0346 - acc: 0.9883 - val_loss: 0.0571 - val_acc: 0.9771
Epoch 817/1000
- 4s - loss: 0.0354 - acc: 0.9872 - val_loss: 0.0572 - val_acc: 0.9771
Epoch 818/1000
- 4s - loss: 0.0352 - acc: 0.9875 - val_loss: 0.0574 - val_acc: 0.9760
Epoch 819/1000
- 4s - loss: 0.0347 - acc: 0.9880 - val_loss: 0.0572 - val_acc: 0.9766
Epoch 820/1000
- 4s - loss: 0.0349 - acc: 0.9879 - val_loss: 0.0573 - val_acc: 0.9766
Epoch 821/1000
- 4s - loss: 0.0337 - acc: 0.9888 - val_loss: 0.0573 - val_acc: 0.9760
Epoch 822/1000
- 4s - loss: 0.0340 - acc: 0.9883 - val_loss: 0.0574 - val_acc: 0.9771
Epoch 823/1000
- 4s - loss: 0.0348 - acc: 0.9884 - val_loss: 0.0573 - val_acc: 0.9766
Epoch 824/1000
- 4s - loss: 0.0353 - acc: 0.9877 - val_loss: 0.0575 - val_acc: 0.9766
Epoch 825/1000
- 4s - loss: 0.0345 - acc: 0.9877 - val_loss: 0.0575 - val_acc: 0.9766
Epoch 826/1000
- 4s - loss: 0.0357 - acc: 0.9872 - val_loss: 0.0575 - val_acc: 0.9760
Epoch 827/1000
- 4s - loss: 0.0348 - acc: 0.9878 - val_loss: 0.0573 - val_acc: 0.9766
Epoch 828/1000
- 4s - loss: 0.0349 - acc: 0.9876 - val_loss: 0.0576 - val_acc: 0.9766
Epoch 829/1000
- 4s - loss: 0.0343 - acc: 0.9879 - val_loss: 0.0573 - val_acc: 0.9760
Epoch 830/1000
- 4s - loss: 0.0353 - acc: 0.9881 - val_loss: 0.0572 - val_acc: 0.9777
Epoch 831/1000
- 4s - loss: 0.0352 - acc: 0.9878 - val_loss: 0.0571 - val_acc: 0.9766
Epoch 832/1000
- 4s - loss: 0.0353 - acc: 0.9880 - val_loss: 0.0573 - val_acc: 0.9766
Epoch 833/1000
- 4s - loss: 0.0344 - acc: 0.9882 - val_loss: 0.0574 - val_acc: 0.9766
Epoch 834/1000
- 4s - loss: 0.0344 - acc: 0.9878 - val_loss: 0.0576 - val_acc: 0.9760
Epoch 835/1000
- 4s - loss: 0.0350 - acc: 0.9876 - val_loss: 0.0572 - val_acc: 0.9766
Epoch 836/1000
- 4s - loss: 0.0338 - acc: 0.9884 - val_loss: 0.0574 - val_acc: 0.9766
Epoch 837/1000
- 4s - loss: 0.0347 - acc: 0.9877 - val_loss: 0.0573 - val_acc: 0.9760
Epoch 838/1000
- 4s - loss: 0.0344 - acc: 0.9882 - val_loss: 0.0573 - val_acc: 0.9766
Epoch 839/1000
- 4s - loss: 0.0338 - acc: 0.9883 - val_loss: 0.0571 - val_acc: 0.9766
Epoch 840/1000
- 4s - loss: 0.0333 - acc: 0.9884 - val_loss: 0.0571 - val_acc: 0.9766
Epoch 841/1000
- 4s - loss: 0.0345 - acc: 0.9881 - val_loss: 0.0574 - val_acc: 0.9766
Epoch 842/1000
- 4s - loss: 0.0341 - acc: 0.9883 - val_loss: 0.0572 - val_acc: 0.9777
Epoch 843/1000
- 4s - loss: 0.0353 - acc: 0.9869 - val_loss: 0.0573 - val_acc: 0.9766
Epoch 844/1000
- 4s - loss: 0.0331 - acc: 0.9884 - val_loss: 0.0572 - val_acc: 0.9766
Epoch 845/1000
- 4s - loss: 0.0352 - acc: 0.9881 - val_loss: 0.0571 - val_acc: 0.9766
Epoch 846/1000
- 4s - loss: 0.0340 - acc: 0.9883 - val_loss: 0.0572 - val_acc: 0.9771
Epoch 847/1000
- 4s - loss: 0.0343 - acc: 0.9883 - val_loss: 0.0571 - val_acc: 0.9766
Epoch 848/1000
- 4s - loss: 0.0341 - acc: 0.9881 - val_loss: 0.0571 - val_acc: 0.9766
Epoch 849/1000
- 4s - loss: 0.0338 - acc: 0.9882 - val_loss: 0.0574 - val_acc: 0.9760
Epoch 850/1000
- 4s - loss: 0.0337 - acc: 0.9885 - val_loss: 0.0573 - val_acc: 0.9766
Epoch 851/1000
- 4s - loss: 0.0347 - acc: 0.9877 - val_loss: 0.0569 - val_acc: 0.9771
Epoch 852/1000
- 4s - loss: 0.0335 - acc: 0.9885 - val_loss: 0.0571 - val_acc: 0.9766
Epoch 853/1000
- 4s - loss: 0.0342 - acc: 0.9883 - val_loss: 0.0570 - val_acc: 0.9783
Epoch 854/1000
- 4s - loss: 0.0329 - acc: 0.9884 - val_loss: 0.0572 - val_acc: 0.9766
Epoch 855/1000
- 4s - loss: 0.0345 - acc: 0.9883 - val_loss: 0.0570 - val_acc: 0.9766
Epoch 856/1000
- 4s - loss: 0.0341 - acc: 0.9880 - val_loss: 0.0573 - val_acc: 0.9771
Epoch 857/1000
- 4s - loss: 0.0338 - acc: 0.9874 - val_loss: 0.0569 - val_acc: 0.9766
Epoch 858/1000
- 4s - loss: 0.0337 - acc: 0.9879 - val_loss: 0.0570 - val_acc: 0.9777
Epoch 859/1000
- 4s - loss: 0.0340 - acc: 0.9876 - val_loss: 0.0571 - val_acc: 0.9760
Epoch 860/1000
- 4s - loss: 0.0342 - acc: 0.9879 - val_loss: 0.0570 - val_acc: 0.9766
Epoch 861/1000
- 4s - loss: 0.0338 - acc: 0.9878 - val_loss: 0.0572 - val_acc: 0.9760
Epoch 862/1000
- 4s - loss: 0.0344 - acc: 0.9876 - val_loss: 0.0573 - val_acc: 0.9766
Epoch 863/1000
- 4s - loss: 0.0339 - acc: 0.9884 - val_loss: 0.0570 - val_acc: 0.9766
Epoch 864/1000
- 4s - loss: 0.0331 - acc: 0.9891 - val_loss: 0.0569 - val_acc: 0.9771
Epoch 865/1000
- 4s - loss: 0.0329 - acc: 0.9890 - val_loss: 0.0574 - val_acc: 0.9754
Epoch 866/1000
- 4s - loss: 0.0333 - acc: 0.9883 - val_loss: 0.0570 - val_acc: 0.9760
Epoch 867/1000
- 4s - loss: 0.0336 - acc: 0.9884 - val_loss: 0.0573 - val_acc: 0.9766
Epoch 868/1000
- 4s - loss: 0.0329 - acc: 0.9879 - val_loss: 0.0568 - val_acc: 0.9771
Epoch 869/1000
- 4s - loss: 0.0338 - acc: 0.9883 - val_loss: 0.0568 - val_acc: 0.9771
Epoch 870/1000
- 4s - loss: 0.0323 - acc: 0.9885 - val_loss: 0.0571 - val_acc: 0.9754
Epoch 871/1000
- 4s - loss: 0.0324 - acc: 0.9893 - val_loss: 0.0573 - val_acc: 0.9771
Epoch 872/1000
- 4s - loss: 0.0320 - acc: 0.9889 - val_loss: 0.0570 - val_acc: 0.9771
Epoch 873/1000
- 4s - loss: 0.0330 - acc: 0.9876 - val_loss: 0.0571 - val_acc: 0.9771
Epoch 874/1000
- 4s - loss: 0.0338 - acc: 0.9888 - val_loss: 0.0569 - val_acc: 0.9760
Epoch 875/1000
- 4s - loss: 0.0339 - acc: 0.9883 - val_loss: 0.0575 - val_acc: 0.9760
Epoch 876/1000
- 4s - loss: 0.0336 - acc: 0.9890 - val_loss: 0.0570 - val_acc: 0.9777
Epoch 877/1000
- 4s - loss: 0.0330 - acc: 0.9890 - val_loss: 0.0571 - val_acc: 0.9777
Epoch 878/1000
- 4s - loss: 0.0338 - acc: 0.9886 - val_loss: 0.0570 - val_acc: 0.9760
Epoch 879/1000
- 4s - loss: 0.0335 - acc: 0.9884 - val_loss: 0.0569 - val_acc: 0.9760
Epoch 880/1000
- 4s - loss: 0.0334 - acc: 0.9883 - val_loss: 0.0570 - val_acc: 0.9771
Epoch 881/1000
- 4s - loss: 0.0337 - acc: 0.9890 - val_loss: 0.0570 - val_acc: 0.9771
Epoch 882/1000
- 4s - loss: 0.0330 - acc: 0.9885 - val_loss: 0.0568 - val_acc: 0.9777
Epoch 883/1000
- 4s - loss: 0.0332 - acc: 0.9887 - val_loss: 0.0569 - val_acc: 0.9766
Epoch 884/1000
- 4s - loss: 0.0329 - acc: 0.9884 - val_loss: 0.0569 - val_acc: 0.9771
Epoch 885/1000
- 4s - loss: 0.0328 - acc: 0.9883 - val_loss: 0.0570 - val_acc: 0.9777
Epoch 886/1000
- 4s - loss: 0.0329 - acc: 0.9889 - val_loss: 0.0569 - val_acc: 0.9771
Epoch 887/1000
- 4s - loss: 0.0329 - acc: 0.9874 - val_loss: 0.0569 - val_acc: 0.9771
Epoch 888/1000
- 4s - loss: 0.0334 - acc: 0.9878 - val_loss: 0.0569 - val_acc: 0.9771
Epoch 889/1000
- 4s - loss: 0.0321 - acc: 0.9889 - val_loss: 0.0571 - val_acc: 0.9771
Epoch 890/1000
- 4s - loss: 0.0327 - acc: 0.9889 - val_loss: 0.0567 - val_acc: 0.9766
Epoch 891/1000
- 4s - loss: 0.0325 - acc: 0.9883 - val_loss: 0.0569 - val_acc: 0.9771
Epoch 892/1000
- 4s - loss: 0.0331 - acc: 0.9879 - val_loss: 0.0568 - val_acc: 0.9777
Epoch 893/1000
- 4s - loss: 0.0319 - acc: 0.9890 - val_loss: 0.0567 - val_acc: 0.9783
Epoch 894/1000
- 4s - loss: 0.0318 - acc: 0.9895 - val_loss: 0.0573 - val_acc: 0.9766
Epoch 895/1000
- 4s - loss: 0.0326 - acc: 0.9891 - val_loss: 0.0569 - val_acc: 0.9777
Epoch 896/1000
- 4s - loss: 0.0322 - acc: 0.9885 - val_loss: 0.0569 - val_acc: 0.9771
Epoch 897/1000
- 4s - loss: 0.0329 - acc: 0.9889 - val_loss: 0.0568 - val_acc: 0.9777
Epoch 898/1000
- 4s - loss: 0.0332 - acc: 0.9886 - val_loss: 0.0569 - val_acc: 0.9771
Epoch 899/1000
- 4s - loss: 0.0327 - acc: 0.9886 - val_loss: 0.0567 - val_acc: 0.9777
Epoch 900/1000
- 4s - loss: 0.0324 - acc: 0.9890 - val_loss: 0.0569 - val_acc: 0.9777
Epoch 901/1000
- 4s - loss: 0.0327 - acc: 0.9891 - val_loss: 0.0565 - val_acc: 0.9783
Epoch 902/1000
- 4s - loss: 0.0315 - acc: 0.9891 - val_loss: 0.0568 - val_acc: 0.9771
Epoch 903/1000
- 4s - loss: 0.0326 - acc: 0.9893 - val_loss: 0.0568 - val_acc: 0.9771
Epoch 904/1000
- 4s - loss: 0.0324 - acc: 0.9882 - val_loss: 0.0569 - val_acc: 0.9766
Epoch 905/1000
- 4s - loss: 0.0320 - acc: 0.9890 - val_loss: 0.0569 - val_acc: 0.9771
Epoch 906/1000
- 4s - loss: 0.0333 - acc: 0.9889 - val_loss: 0.0570 - val_acc: 0.9771
Epoch 907/1000
- 4s - loss: 0.0328 - acc: 0.9890 - val_loss: 0.0570 - val_acc: 0.9771
Epoch 908/1000
- 4s - loss: 0.0321 - acc: 0.9891 - val_loss: 0.0568 - val_acc: 0.9766
Epoch 909/1000
- 4s - loss: 0.0318 - acc: 0.9893 - val_loss: 0.0567 - val_acc: 0.9789
Epoch 910/1000
- 4s - loss: 0.0317 - acc: 0.9900 - val_loss: 0.0568 - val_acc: 0.9777
Epoch 911/1000
- 4s - loss: 0.0323 - acc: 0.9886 - val_loss: 0.0568 - val_acc: 0.9777
Epoch 912/1000
- 4s - loss: 0.0320 - acc: 0.9897 - val_loss: 0.0569 - val_acc: 0.9766
Epoch 913/1000
- 4s - loss: 0.0330 - acc: 0.9887 - val_loss: 0.0567 - val_acc: 0.9771
Epoch 914/1000
- 4s - loss: 0.0323 - acc: 0.9897 - val_loss: 0.0564 - val_acc: 0.9777
Epoch 915/1000
- 4s - loss: 0.0325 - acc: 0.9885 - val_loss: 0.0565 - val_acc: 0.9777
Epoch 916/1000
- 4s - loss: 0.0317 - acc: 0.9893 - val_loss: 0.0568 - val_acc: 0.9777
Epoch 917/1000
- 4s - loss: 0.0325 - acc: 0.9891 - val_loss: 0.0566 - val_acc: 0.9777
Epoch 918/1000
- 4s - loss: 0.0318 - acc: 0.9895 - val_loss: 0.0568 - val_acc: 0.9771
Epoch 919/1000
- 4s - loss: 0.0303 - acc: 0.9901 - val_loss: 0.0572 - val_acc: 0.9771
Epoch 920/1000
- 4s - loss: 0.0326 - acc: 0.9879 - val_loss: 0.0565 - val_acc: 0.9777
Epoch 921/1000
- 4s - loss: 0.0320 - acc: 0.9888 - val_loss: 0.0567 - val_acc: 0.9771
Epoch 922/1000
- 4s - loss: 0.0319 - acc: 0.9892 - val_loss: 0.0566 - val_acc: 0.9777
Epoch 923/1000
- 4s - loss: 0.0320 - acc: 0.9887 - val_loss: 0.0566 - val_acc: 0.9771
Epoch 924/1000
- 4s - loss: 0.0313 - acc: 0.9896 - val_loss: 0.0570 - val_acc: 0.9771
Epoch 925/1000
- 4s - loss: 0.0320 - acc: 0.9893 - val_loss: 0.0567 - val_acc: 0.9777
Epoch 926/1000
- 4s - loss: 0.0316 - acc: 0.9891 - val_loss: 0.0567 - val_acc: 0.9777
Epoch 927/1000
- 4s - loss: 0.0316 - acc: 0.9886 - val_loss: 0.0568 - val_acc: 0.9777
Epoch 928/1000
- 4s - loss: 0.0310 - acc: 0.9891 - val_loss: 0.0568 - val_acc: 0.9777
Epoch 929/1000
- 4s - loss: 0.0311 - acc: 0.9897 - val_loss: 0.0569 - val_acc: 0.9771
Epoch 930/1000
- 4s - loss: 0.0312 - acc: 0.9890 - val_loss: 0.0571 - val_acc: 0.9766
Epoch 931/1000
- 4s - loss: 0.0322 - acc: 0.9892 - val_loss: 0.0566 - val_acc: 0.9777
Epoch 932/1000
- 4s - loss: 0.0308 - acc: 0.9890 - val_loss: 0.0567 - val_acc: 0.9771
Epoch 933/1000
- 4s - loss: 0.0317 - acc: 0.9889 - val_loss: 0.0564 - val_acc: 0.9777
Epoch 934/1000
- 4s - loss: 0.0319 - acc: 0.9888 - val_loss: 0.0567 - val_acc: 0.9771
Epoch 935/1000
- 4s - loss: 0.0310 - acc: 0.9898 - val_loss: 0.0562 - val_acc: 0.9771
Epoch 936/1000
- 4s - loss: 0.0306 - acc: 0.9905 - val_loss: 0.0565 - val_acc: 0.9771
Epoch 937/1000
- 4s - loss: 0.0307 - acc: 0.9890 - val_loss: 0.0566 - val_acc: 0.9777
Epoch 938/1000
- 4s - loss: 0.0321 - acc: 0.9888 - val_loss: 0.0565 - val_acc: 0.9777
Epoch 939/1000
- 4s - loss: 0.0312 - acc: 0.9894 - val_loss: 0.0567 - val_acc: 0.9777
Epoch 940/1000
- 4s - loss: 0.0306 - acc: 0.9903 - val_loss: 0.0566 - val_acc: 0.9777
Epoch 941/1000
- 4s - loss: 0.0310 - acc: 0.9891 - val_loss: 0.0567 - val_acc: 0.9777
Epoch 942/1000
- 4s - loss: 0.0313 - acc: 0.9897 - val_loss: 0.0567 - val_acc: 0.9771
Epoch 943/1000
- 4s - loss: 0.0311 - acc: 0.9895 - val_loss: 0.0566 - val_acc: 0.9777
Epoch 944/1000
- 4s - loss: 0.0321 - acc: 0.9885 - val_loss: 0.0565 - val_acc: 0.9766
Epoch 945/1000
- 4s - loss: 0.0314 - acc: 0.9897 - val_loss: 0.0568 - val_acc: 0.9777
Epoch 946/1000
- 4s - loss: 0.0312 - acc: 0.9895 - val_loss: 0.0564 - val_acc: 0.9771
Epoch 947/1000
- 4s - loss: 0.0311 - acc: 0.9895 - val_loss: 0.0565 - val_acc: 0.9777
Epoch 948/1000
- 4s - loss: 0.0310 - acc: 0.9896 - val_loss: 0.0567 - val_acc: 0.9777
Epoch 949/1000
- 4s - loss: 0.0318 - acc: 0.9886 - val_loss: 0.0565 - val_acc: 0.9777
Epoch 950/1000
- 4s - loss: 0.0311 - acc: 0.9895 - val_loss: 0.0566 - val_acc: 0.9777
Epoch 951/1000
- 4s - loss: 0.0319 - acc: 0.9892 - val_loss: 0.0565 - val_acc: 0.9771
Epoch 952/1000
- 4s - loss: 0.0306 - acc: 0.9896 - val_loss: 0.0567 - val_acc: 0.9777
Epoch 953/1000
- 4s - loss: 0.0308 - acc: 0.9896 - val_loss: 0.0567 - val_acc: 0.9766
Epoch 954/1000
- 4s - loss: 0.0306 - acc: 0.9900 - val_loss: 0.0566 - val_acc: 0.9777
Epoch 955/1000
- 4s - loss: 0.0311 - acc: 0.9893 - val_loss: 0.0568 - val_acc: 0.9771
Epoch 956/1000
- 4s - loss: 0.0308 - acc: 0.9902 - val_loss: 0.0566 - val_acc: 0.9777
Epoch 957/1000
- 4s - loss: 0.0302 - acc: 0.9900 - val_loss: 0.0565 - val_acc: 0.9771
Epoch 958/1000
- 4s - loss: 0.0305 - acc: 0.9899 - val_loss: 0.0565 - val_acc: 0.9771
Epoch 959/1000
- 4s - loss: 0.0308 - acc: 0.9897 - val_loss: 0.0566 - val_acc: 0.9760
Epoch 960/1000
- 4s - loss: 0.0306 - acc: 0.9892 - val_loss: 0.0568 - val_acc: 0.9766
Epoch 961/1000
- 4s - loss: 0.0313 - acc: 0.9888 - val_loss: 0.0567 - val_acc: 0.9777
Epoch 962/1000
- 4s - loss: 0.0307 - acc: 0.9890 - val_loss: 0.0565 - val_acc: 0.9777
Epoch 963/1000
- 4s - loss: 0.0306 - acc: 0.9895 - val_loss: 0.0566 - val_acc: 0.9777
Epoch 964/1000
- 4s - loss: 0.0305 - acc: 0.9897 - val_loss: 0.0569 - val_acc: 0.9766
Epoch 965/1000
- 4s - loss: 0.0309 - acc: 0.9887 - val_loss: 0.0564 - val_acc: 0.9771
Epoch 966/1000
- 4s - loss: 0.0310 - acc: 0.9895 - val_loss: 0.0565 - val_acc: 0.9771
Epoch 967/1000
- 4s - loss: 0.0293 - acc: 0.9902 - val_loss: 0.0565 - val_acc: 0.9777
Epoch 968/1000
- 4s - loss: 0.0306 - acc: 0.9891 - val_loss: 0.0563 - val_acc: 0.9777
Epoch 969/1000
- 4s - loss: 0.0298 - acc: 0.9900 - val_loss: 0.0565 - val_acc: 0.9771
Epoch 970/1000
- 4s - loss: 0.0303 - acc: 0.9897 - val_loss: 0.0572 - val_acc: 0.9766
Epoch 971/1000
- 4s - loss: 0.0303 - acc: 0.9895 - val_loss: 0.0564 - val_acc: 0.9777
Epoch 972/1000
- 4s - loss: 0.0304 - acc: 0.9895 - val_loss: 0.0563 - val_acc: 0.9771
Epoch 973/1000
- 4s - loss: 0.0298 - acc: 0.9902 - val_loss: 0.0564 - val_acc: 0.9771
Epoch 974/1000
- 4s - loss: 0.0298 - acc: 0.9893 - val_loss: 0.0563 - val_acc: 0.9777
Epoch 975/1000
- 4s - loss: 0.0305 - acc: 0.9900 - val_loss: 0.0564 - val_acc: 0.9777
Epoch 976/1000
- 4s - loss: 0.0297 - acc: 0.9903 - val_loss: 0.0565 - val_acc: 0.9777
Epoch 977/1000
- 4s - loss: 0.0306 - acc: 0.9893 - val_loss: 0.0563 - val_acc: 0.9777
Epoch 978/1000
- 4s - loss: 0.0308 - acc: 0.9897 - val_loss: 0.0568 - val_acc: 0.9760
Epoch 979/1000
- 4s - loss: 0.0302 - acc: 0.9894 - val_loss: 0.0566 - val_acc: 0.9771
Epoch 980/1000
- 4s - loss: 0.0305 - acc: 0.9892 - val_loss: 0.0564 - val_acc: 0.9783
Epoch 981/1000
- 4s - loss: 0.0293 - acc: 0.9899 - val_loss: 0.0567 - val_acc: 0.9766
Epoch 982/1000
- 4s - loss: 0.0309 - acc: 0.9891 - val_loss: 0.0565 - val_acc: 0.9777
Epoch 983/1000
- 4s - loss: 0.0315 - acc: 0.9892 - val_loss: 0.0563 - val_acc: 0.9783
Epoch 984/1000
- 4s - loss: 0.0306 - acc: 0.9897 - val_loss: 0.0563 - val_acc: 0.9789
Epoch 985/1000
- 4s - loss: 0.0291 - acc: 0.9905 - val_loss: 0.0565 - val_acc: 0.9771
Epoch 986/1000
- 4s - loss: 0.0306 - acc: 0.9895 - val_loss: 0.0566 - val_acc: 0.9766
Epoch 987/1000
- 4s - loss: 0.0303 - acc: 0.9895 - val_loss: 0.0565 - val_acc: 0.9777
Epoch 988/1000
- 4s - loss: 0.0307 - acc: 0.9890 - val_loss: 0.0565 - val_acc: 0.9777
Epoch 989/1000
- 4s - loss: 0.0299 - acc: 0.9898 - val_loss: 0.0567 - val_acc: 0.9771
Epoch 990/1000
- 4s - loss: 0.0297 - acc: 0.9895 - val_loss: 0.0563 - val_acc: 0.9783
Epoch 991/1000
- 4s - loss: 0.0295 - acc: 0.9906 - val_loss: 0.0562 - val_acc: 0.9777
Epoch 992/1000
- 4s - loss: 0.0296 - acc: 0.9899 - val_loss: 0.0562 - val_acc: 0.9783
Epoch 993/1000
- 4s - loss: 0.0294 - acc: 0.9906 - val_loss: 0.0563 - val_acc: 0.9777
Epoch 994/1000
- 4s - loss: 0.0301 - acc: 0.9904 - val_loss: 0.0563 - val_acc: 0.9777
Epoch 995/1000
- 4s - loss: 0.0298 - acc: 0.9898 - val_loss: 0.0563 - val_acc: 0.9783
Epoch 996/1000
- 4s - loss: 0.0292 - acc: 0.9905 - val_loss: 0.0564 - val_acc: 0.9783
Epoch 997/1000
- 4s - loss: 0.0312 - acc: 0.9899 - val_loss: 0.0565 - val_acc: 0.9783
Epoch 998/1000
- 4s - loss: 0.0292 - acc: 0.9900 - val_loss: 0.0564 - val_acc: 0.9777
Epoch 999/1000
- 4s - loss: 0.0305 - acc: 0.9899 - val_loss: 0.0563 - val_acc: 0.9777
Epoch 1000/1000
- 4s - loss: 0.0301 - acc: 0.9896 - val_loss: 0.0565 - val_acc: 0.9777
CPU times: user 46min 37s, sys: 4min 35s, total: 51min 12s
Wall time: 1h 10min 44s
```
In [11]:
```py
with open('history.json', 'w') as f:
json.dump(history.history, f)
history_df = pd.DataFrame(history.history)
history_df[['loss', 'val_loss']].plot()
history_df[['acc', 'val_acc']].plot()
```
Out[11]:
```
```
In [12]:
```py
%%time
X_tst = []
Test_imgs = []
for img_id in tqdm_notebook(os.listdir(test_dir)):
X_tst.append(cv2.imread(test_dir + img_id))
Test_imgs.append(img_id)
X_tst = np.asarray(X_tst)
X_tst = X_tst.astype('float32')
X_tst /= 255
```
```
CPU times: user 788 ms, sys: 544 ms, total: 1.33 s
Wall time: 5.92 s
```
In [13]:
```py
# Prediction
test_predictions = model.predict(X_tst)
```
In [14]:
```py
sub_df = pd.DataFrame(test_predictions, columns=['has_cactus'])
sub_df['has_cactus'] = sub_df['has_cactus'].apply(lambda x: 1 if x > 0.75 else 0)
```
In [15]:
```py
sub_df['id'] = ''
cols = sub_df.columns.tolist()
cols = cols[-1:] + cols[:-1]
sub_df=sub_df[cols]
```
In [16]:
```py
for i, img in enumerate(Test_imgs):
sub_df.set_value(i,'id',img)
```
```
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead
```
In [17]:
```py
sub_df.head()
```
Out[17]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 79ac4cc3b082e0a1defe1be601806efd.jpg | 1 |
| --- | --- | --- |
| 1 | e880364d6521c6f3a27748ec62b0e335.jpg | 1 |
| --- | --- | --- |
| 2 | 74912492b6cdf28c4bfb9c8e1d35af3e.jpg | 1 |
| --- | --- | --- |
| 3 | 078cfa961183b30693ea2f13f5ff6d17.jpg | 1 |
| --- | --- | --- |
| 4 | 7fd729184ef182899ce3e7a174fb9bc0.jpg | 1 |
| --- | --- | --- |
In [18]:
```py
sub_df.to_csv('submission.csv',index=False)
```
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16.md
================================================
# PCA-MLP VS PCA-CNN , Focal Loss resnet50 vs Vgg16
> Author: https://www.kaggle.com/rohandx1996
> From: https://www.kaggle.com/rohandx1996/pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
In [1]:
```py
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
# Any results you write to the current directory are saved as output.
```
```
['test', 'sample_submission.csv', 'train.csv', 'train']
```
# Introduction
In this short notebook I wanted to experiment with PCA and neural network implemented with Keras library. Also I would like to ilustrate how PCA decomposition works on data from this dataset. Most important thing, I would like hopefully to get some feedback to improve this method or find completely different approach.
Thanks to other authors for publishing their notebooks, I reused some parts of the code when I was looking for fiding nice solutions for problems I had on a way.
# let's first start analyzing the data how much classes we are dealing , data is imbalance or not
In [2]:
```py
train_df=pd.read_csv("../input/train.csv")
```
In [3]:
```py
train_df.shape
```
Out[3]:
```
(17500, 2)
```
In [4]:
```py
train_df.head()
```
Out[4]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |
| --- | --- | --- |
| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |
| --- | --- | --- |
| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |
| --- | --- | --- |
| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |
| --- | --- | --- |
| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |
| --- | --- | --- |
In [5]:
```py
import seaborn as sns
import matplotlib.pyplot as plt
```
In [6]:
```py
sns.countplot(train_df["has_cactus"])
```
Out[6]:
```
```
# so again dealing with imbalance classifacition we have to use some dice loss or focal loss or in keras we have to use logits_v2 with categorical cross entropy
In [7]:
```py
import cv2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import os
from tqdm import tqdm, tqdm_notebook
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.applications import VGG16
from keras.optimizers import Adam
```
```
Using TensorFlow backend.
```
In [8]:
```py
train_dir = "../input/train/train/"
test_dir = "../input/test/test/"
```
In [9]:
```py
X_tr = []
Y_tr = []
imges = train_df['id'].values
for img_id in tqdm_notebook(imges):
X_tr.append(cv2.imread(train_dir + img_id,0))
Y_tr.append(train_df[train_df['id'] == img_id]['has_cactus'].values[0])
X_tr = np.asarray(X_tr)
X_tr = X_tr.astype('float32')
X_tr /= 255
Y_tr = np.asarray(Y_tr)
```
In [10]:
```py
X_tr=X_tr.reshape(-1,32,32,1)
```
In [11]:
```py
Y_tr.shape
```
Out[11]:
```
(17500,)
```
# First for applying PCA we need to convert rgb images to grayscale , same as well for test images we have to do
Image has 32x32 pixels, so data has 1024 features. Now for computers such amount of data isn't that big, but there can be cases, when dimensional reduction can be important. It is better for further processing to have 625 features containing most of the data than 1000 features.
Before I use PCA to reduce dimensionality of the data I will standardize it using sklearn StandartScaler. It is fitted to train data, because I assume that I know nothing about test data. Both datasets are transformed.
In [12]:
```py
X_tr.shape
```
Out[12]:
```
(17500, 32, 32, 1)
```
# so now we need pixel wise values after that only we can apply PCA to see that with less features can we get same accuracy
In [13]:
```py
target=train_df["has_cactus"]
```
In [14]:
```py
train_df=train_df.drop("has_cactus",axis=1)
```
In [15]:
```py
import os,array
import pandas as pd
import time
import dask as dd
from PIL import Image
def pixelconv(file_list,img_height,img_width,pixels):
columnNames = list()
for i in range(pixels):
pixel = 'pixel'
pixel += str(i)
columnNames.append(pixel)
train_data = pd.DataFrame(columns = columnNames)
start_time = time.time()
for i in tqdm_notebook(file_list):
t = i
img_name = t
img = Image.open('../input/train/train/'+img_name)
rawData = img.load()
#print rawData
data = []
for y in range(img_height):
for x in range(img_width):
data.append(rawData[x,y][0])
print (i)
k = 0
#print data
train_data.loc[i] = [data[k] for k in range(pixels)]
#print train_data.loc[0]
print ("Done pixel values conversion")
print (time.time()-start_time)
print (train_data)
train_data.to_csv("train_converted_new.csv",index = False)
print ("Done data frame conversion")
print (time.time()-start_time)
pixelconv(train_df.id,32,32,1024) # pass pandas dataframe in which path of images only as column
# in return csv file will save in working directory
```
```
0004be2cfeaba1c0361d39e2b000257b.jpg
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000d1e9a533f62e55c289303b072733d.jpg
0011485b40695e9138e92d0b3fb55128.jpg
0014d7a11e90b62848904c1418fc8cf2.jpg
0017c3c18ddd57a2ea6f9848c79d83d2.jpg
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0024320f43bdd490562246435af4f90b.jpg
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00351838ebf6dff6e53056e00a1e307c.jpg
003519dd841a97ed16481fa0657df04d.jpg
003bb64852016d9c87871ddd8e25ab03.jpg
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0045d0f2aec739370eaefac79ee5b96c.jpg
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9a8bc9a4ce38d84ba60b866f06aaed73.jpg
```
In [16]:
```py
new_data=pd.read_csv("../working/train_converted_new.csv")
```
In [17]:
```py
new_data.head()
```
Out[17]:
| | pixel0 | pixel1 | pixel2 | pixel3 | pixel4 | pixel5 | pixel6 | pixel7 | pixel8 | pixel9 | pixel10 | pixel11 | pixel12 | pixel13 | pixel14 | pixel15 | pixel16 | pixel17 | pixel18 | pixel19 | pixel20 | pixel21 | pixel22 | pixel23 | pixel24 | pixel25 | pixel26 | pixel27 | pixel28 | pixel29 | pixel30 | pixel31 | pixel32 | pixel33 | pixel34 | pixel35 | pixel36 | pixel37 | pixel38 | pixel39 | ... | pixel984 | pixel985 | pixel986 | pixel987 | pixel988 | pixel989 | pixel990 | pixel991 | pixel992 | pixel993 | pixel994 | pixel995 | pixel996 | pixel997 | pixel998 | pixel999 | pixel1000 | pixel1001 | pixel1002 | pixel1003 | pixel1004 | pixel1005 | pixel1006 | pixel1007 | pixel1008 | pixel1009 | pixel1010 | pixel1011 | pixel1012 | pixel1013 | pixel1014 | pixel1015 | pixel1016 | pixel1017 | pixel1018 | pixel1019 | pixel1020 | pixel1021 | pixel1022 | pixel1023 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | 136 | 134 | 147 | 136 | 116 | 100 | 100 | 116 | 102 | 125 | 120 | 120 | 126 | 134 | 136 | 137 | 132 | 93 | 137 | 149 | 100 | 128 | 144 | 171 | 163 | 162 | 146 | 144 | 147 | 152 | 147 | 157 | 124 | 161 | 158 | 96 | 122 | 114 | 121 | 94 | ... | 165 | 163 | 167 | 161 | 168 | 167 | 152 | 152 | 183 | 115 | 165 | 161 | 184 | 146 | 113 | 188 | 177 | 153 | 146 | 157 | 167 | 176 | 169 | 175 | 174 | 179 | 170 | 171 | 169 | 162 | 158 | 161 | 176 | 162 | 156 | 167 | 172 | 179 | 142 | 139 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | 118 | 108 | 104 | 110 | 85 | 55 | 86 | 82 | 109 | 134 | 126 | 124 | 128 | 128 | 133 | 121 | 121 | 139 | 143 | 125 | 117 | 107 | 112 | 151 | 170 | 155 | 139 | 161 | 167 | 158 | 138 | 123 | 133 | 123 | 117 | 121 | 96 | 66 | 93 | 93 | ... | 106 | 104 | 127 | 141 | 127 | 113 | 140 | 183 | 93 | 65 | 60 | 61 | 69 | 119 | 121 | 93 | 100 | 151 | 206 | 246 | 216 | 177 | 154 | 90 | 54 | 89 | 107 | 128 | 148 | 147 | 105 | 94 | 121 | 141 | 124 | 154 | 143 | 104 | 97 | 126 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 2 | 158 | 158 | 159 | 168 | 145 | 121 | 142 | 134 | 155 | 137 | 168 | 168 | 96 | 141 | 184 | 95 | 160 | 195 | 178 | 132 | 104 | 153 | 149 | 130 | 124 | 121 | 122 | 124 | 125 | 166 | 142 | 133 | 169 | 145 | 173 | 163 | 147 | 124 | 133 | 140 | ... | 169 | 144 | 153 | 109 | 174 | 139 | 145 | 136 | 144 | 143 | 146 | 147 | 111 | 141 | 171 | 154 | 162 | 156 | 157 | 169 | 154 | 147 | 139 | 164 | 163 | 146 | 143 | 158 | 115 | 162 | 149 | 154 | 145 | 171 | 130 | 156 | 158 | 163 | 161 | 152 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 3 | 147 | 122 | 101 | 126 | 126 | 131 | 133 | 112 | 157 | 165 | 160 | 136 | 135 | 140 | 97 | 161 | 157 | 138 | 132 | 130 | 133 | 102 | 142 | 148 | 156 | 155 | 167 | 166 | 169 | 148 | 151 | 116 | 136 | 148 | 132 | 153 | 98 | 124 | 142 | 127 | ... | 167 | 151 | 99 | 153 | 183 | 93 | 165 | 146 | 152 | 124 | 136 | 145 | 139 | 157 | 148 | 158 | 119 | 138 | 137 | 102 | 154 | 85 | 139 | 196 | 55 | 171 | 117 | 182 | 141 | 143 | 139 | 145 | 67 | 134 | 181 | 148 | 108 | 139 | 133 | 138 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 4 | 113 | 109 | 123 | 156 | 154 | 128 | 119 | 119 | 101 | 79 | 129 | 147 | 125 | 139 | 136 | 131 | 141 | 141 | 141 | 135 | 146 | 131 | 139 | 143 | 141 | 129 | 129 | 142 | 146 | 138 | 133 | 168 | 131 | 128 | 132 | 149 | 152 | 134 | 114 | 125 | ... | 75 | 130 | 160 | 137 | 123 | 75 | 88 | 139 | 107 | 120 | 89 | 134 | 138 | 148 | 140 | 128 | 137 | 171 | 164 | 178 | 163 | 116 | 123 | 125 | 139 | 145 | 158 | 172 | 159 | 147 | 125 | 81 | 94 | 155 | 133 | 151 | 134 | 74 | 50 | 128 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
# so finally we have dataset of pixel values now we can apply PCA with pixeld data
In [18]:
```py
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from keras.models import Sequential
from keras.utils import np_utils
from keras.layers import Dense, Dropout, GaussianNoise, Conv1D
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
```
In [19]:
```py
pca = PCA(n_components=500)
pca.fit(new_data)
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('Number of components')
plt.ylabel('Cumulative explained variance')
```
Out[19]:
```
Text(0, 0.5, 'Cumulative explained variance')
```
In plot above we can see that cumulative explained variance is very high near 500 and then it increases very slowly. That means that data describing changes is mostly contained in i guess 625 components. We need to evaluate trade-offs before we choose number of components we use further. I choose 625 to check how it will work as it seems to have most of the data.
# so we come to know that with less number features we can explain the dataset
In [20]:
```py
NCOMPONENTS = 625
pca = PCA(n_components=NCOMPONENTS)
X_pca_train = pca.fit_transform(new_data)
pca_std = np.std(X_pca_train)
print(X_pca_train.shape)
```
```
(17500, 625)
```
In [21]:
```py
inv_pca = pca.inverse_transform(X_pca_train)
#inv_sc = scaler.inverse_transform(inv_pca)
```
In [22]:
```py
X_pca_train_new=X_pca_train.reshape(X_pca_train.shape[0],25,25,1)
```
In [23]:
```py
X_pca_train_new.shape
```
Out[23]:
```
(17500, 25, 25, 1)
```
# Apply MLP With PCA
In [24]:
```py
X_pca_train.shape ### this shape will be used in MLP
```
Out[24]:
```
(17500, 625)
```
# Neural network with Keras
I implemened simple model of multilayer perceptron (MLP) neural network using Keras and experimented with it.
Using library is simple. First you need to create model instance and then add layers using models.add() method. First layer need to be set up for proper input dimension. Output layer needs to have proper output dimension and activation function. In between hidden layers can be added.
During compilation parameters of loss function, optimizer and metrics need to be set depanding on problem.
In [25]:
```py
import keras
model = Sequential()
layers = 1
units = 128
model.add(Dense(units, input_dim=NCOMPONENTS, activation='relu'))
model.add(GaussianNoise(pca_std))
model.add(Dense(units, activation='relu'))
model.add(GaussianNoise(pca_std))
model.add(Dropout(0.1))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(lr=1e-5), metrics=['acc'])
history = model.fit(X_pca_train,target,
batch_size=32,
epochs=10,
verbose=1,
validation_split=0.15)
#model.fit(X_pca_train, Y_train, epochs=100, batch_size=256, validation_split=0.15, verbose=2)
```
```
WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Train on 14875 samples, validate on 2625 samples
Epoch 1/10
14875/14875 [==============================] - 3s 180us/step - loss: 7.6863 - acc: 0.4876 - val_loss: 5.4468 - val_acc: 0.5684
Epoch 2/10
14875/14875 [==============================] - 1s 97us/step - loss: 6.6398 - acc: 0.5578 - val_loss: 4.1218 - val_acc: 0.6728
Epoch 3/10
14875/14875 [==============================] - 1s 98us/step - loss: 5.7543 - acc: 0.6112 - val_loss: 3.5392 - val_acc: 0.7181
Epoch 4/10
14875/14875 [==============================] - 1s 97us/step - loss: 5.2455 - acc: 0.6477 - val_loss: 3.6776 - val_acc: 0.7406
Epoch 5/10
14875/14875 [==============================] - 1s 97us/step - loss: 4.9131 - acc: 0.6711 - val_loss: 3.8335 - val_acc: 0.7501
Epoch 6/10
14875/14875 [==============================] - 1s 97us/step - loss: 4.7023 - acc: 0.6859 - val_loss: 3.9065 - val_acc: 0.7516
Epoch 7/10
14875/14875 [==============================] - 1s 97us/step - loss: 4.5000 - acc: 0.6994 - val_loss: 3.9499 - val_acc: 0.7505
Epoch 8/10
14875/14875 [==============================] - 1s 97us/step - loss: 4.2819 - acc: 0.7166 - val_loss: 3.9731 - val_acc: 0.7505
Epoch 9/10
14875/14875 [==============================] - 1s 98us/step - loss: 4.2099 - acc: 0.7234 - val_loss: 3.9770 - val_acc: 0.7505
Epoch 10/10
14875/14875 [==============================] - 1s 98us/step - loss: 4.1711 - acc: 0.7273 - val_loss: 3.9780 - val_acc: 0.7505
```
In [26]:
```py
import matplotlib.pyplot as plt
%matplotlib inline
accuracy = history.history['acc']
val_accuracy = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(accuracy))
plt.plot(epochs, accuracy, 'bo', label='Training accuracy')
plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
```
# Applied CNN with PCA
In [27]:
```py
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
batch_size = 256
num_classes = 1
epochs = 200
#input image dimensions
img_rows, img_cols = 25, 25
model = Sequential()
model.add(Conv2D(64, kernel_size=(3, 3),
activation='relu',
input_shape=(25,25,1)))
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.1))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(num_classes, activation='sigmoid'))
model.compile(loss=keras.losses.binary_crossentropy,
optimizer=keras.optimizers.Adam(lr=1e-5),
metrics=['accuracy'])
```
In [28]:
```py
history1 = model.fit(X_pca_train_new,target,
batch_size=batch_size,
epochs=200,
verbose=1,
validation_split=0.15)
```
```
Train on 14875 samples, validate on 2625 samples
Epoch 1/200
14875/14875 [==============================] - 2s 121us/step - loss: 0.7758 - acc: 0.7275 - val_loss: 0.4472 - val_acc: 0.7794
Epoch 2/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.5467 - acc: 0.7747 - val_loss: 0.3774 - val_acc: 0.8057
Epoch 3/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.4593 - acc: 0.7954 - val_loss: 0.3489 - val_acc: 0.8210
Epoch 4/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.4079 - acc: 0.8056 - val_loss: 0.3348 - val_acc: 0.8389
Epoch 5/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.3790 - acc: 0.8185 - val_loss: 0.3184 - val_acc: 0.8411
Epoch 6/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.3582 - acc: 0.8282 - val_loss: 0.3175 - val_acc: 0.8556
Epoch 7/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.3467 - acc: 0.8296 - val_loss: 0.3079 - val_acc: 0.8571
Epoch 8/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.3296 - acc: 0.8407 - val_loss: 0.3022 - val_acc: 0.8663
Epoch 9/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.3195 - acc: 0.8453 - val_loss: 0.2826 - val_acc: 0.8670
Epoch 10/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.3097 - acc: 0.8514 - val_loss: 0.2763 - val_acc: 0.8728
Epoch 11/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.3043 - acc: 0.8536 - val_loss: 0.2751 - val_acc: 0.8758
Epoch 12/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.2907 - acc: 0.8616 - val_loss: 0.2669 - val_acc: 0.8815
Epoch 13/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.2877 - acc: 0.8648 - val_loss: 0.2596 - val_acc: 0.8792
Epoch 14/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.2812 - acc: 0.8651 - val_loss: 0.2613 - val_acc: 0.8815
Epoch 15/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.2781 - acc: 0.8703 - val_loss: 0.2552 - val_acc: 0.8850
Epoch 16/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.2691 - acc: 0.8764 - val_loss: 0.2486 - val_acc: 0.8869
Epoch 17/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.2649 - acc: 0.8771 - val_loss: 0.2439 - val_acc: 0.8930
Epoch 18/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.2593 - acc: 0.8818 - val_loss: 0.2462 - val_acc: 0.8937
Epoch 19/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.2554 - acc: 0.8837 - val_loss: 0.2382 - val_acc: 0.8941
Epoch 20/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.2561 - acc: 0.8848 - val_loss: 0.2353 - val_acc: 0.8949
Epoch 21/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.2450 - acc: 0.8893 - val_loss: 0.2355 - val_acc: 0.8960
Epoch 22/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.2468 - acc: 0.8879 - val_loss: 0.2300 - val_acc: 0.8987
Epoch 23/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2402 - acc: 0.8924 - val_loss: 0.2333 - val_acc: 0.8983
Epoch 24/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2407 - acc: 0.8946 - val_loss: 0.2261 - val_acc: 0.8983
Epoch 25/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2354 - acc: 0.8953 - val_loss: 0.2283 - val_acc: 0.8998
Epoch 26/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2289 - acc: 0.8985 - val_loss: 0.2222 - val_acc: 0.9010
Epoch 27/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2272 - acc: 0.8986 - val_loss: 0.2201 - val_acc: 0.8987
Epoch 28/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2252 - acc: 0.8995 - val_loss: 0.2190 - val_acc: 0.9025
Epoch 29/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2163 - acc: 0.9051 - val_loss: 0.2176 - val_acc: 0.9032
Epoch 30/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.2167 - acc: 0.9053 - val_loss: 0.2171 - val_acc: 0.9048
Epoch 31/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2159 - acc: 0.9036 - val_loss: 0.2171 - val_acc: 0.9029
Epoch 32/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.2152 - acc: 0.9061 - val_loss: 0.2148 - val_acc: 0.9063
Epoch 33/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2103 - acc: 0.9076 - val_loss: 0.2201 - val_acc: 0.9029
Epoch 34/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2080 - acc: 0.9110 - val_loss: 0.2146 - val_acc: 0.9040
Epoch 35/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2119 - acc: 0.9054 - val_loss: 0.2110 - val_acc: 0.9093
Epoch 36/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2083 - acc: 0.9116 - val_loss: 0.2111 - val_acc: 0.9067
Epoch 37/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2038 - acc: 0.9121 - val_loss: 0.2123 - val_acc: 0.9070
Epoch 38/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2014 - acc: 0.9120 - val_loss: 0.2102 - val_acc: 0.9086
Epoch 39/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2005 - acc: 0.9129 - val_loss: 0.2090 - val_acc: 0.9109
Epoch 40/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.2006 - acc: 0.9123 - val_loss: 0.2121 - val_acc: 0.9078
Epoch 41/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1957 - acc: 0.9156 - val_loss: 0.2082 - val_acc: 0.9097
Epoch 42/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1966 - acc: 0.9133 - val_loss: 0.2063 - val_acc: 0.9124
Epoch 43/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1917 - acc: 0.9166 - val_loss: 0.2065 - val_acc: 0.9093
Epoch 44/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1866 - acc: 0.9193 - val_loss: 0.2039 - val_acc: 0.9116
Epoch 45/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.1895 - acc: 0.9205 - val_loss: 0.2044 - val_acc: 0.9086
Epoch 46/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1865 - acc: 0.9197 - val_loss: 0.2036 - val_acc: 0.9097
Epoch 47/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1834 - acc: 0.9226 - val_loss: 0.2044 - val_acc: 0.9097
Epoch 48/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1822 - acc: 0.9224 - val_loss: 0.2055 - val_acc: 0.9097
Epoch 49/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1809 - acc: 0.9226 - val_loss: 0.2009 - val_acc: 0.9124
Epoch 50/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.1798 - acc: 0.9235 - val_loss: 0.2024 - val_acc: 0.9101
Epoch 51/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.1778 - acc: 0.9241 - val_loss: 0.1998 - val_acc: 0.9128
Epoch 52/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1766 - acc: 0.9252 - val_loss: 0.2009 - val_acc: 0.9109
Epoch 53/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1765 - acc: 0.9235 - val_loss: 0.1984 - val_acc: 0.9124
Epoch 54/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1709 - acc: 0.9269 - val_loss: 0.1983 - val_acc: 0.9112
Epoch 55/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1739 - acc: 0.9254 - val_loss: 0.2020 - val_acc: 0.9086
Epoch 56/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1700 - acc: 0.9293 - val_loss: 0.2006 - val_acc: 0.9116
Epoch 57/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1642 - acc: 0.9310 - val_loss: 0.1996 - val_acc: 0.9120
Epoch 58/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1631 - acc: 0.9294 - val_loss: 0.1978 - val_acc: 0.9112
Epoch 59/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.1651 - acc: 0.9316 - val_loss: 0.1987 - val_acc: 0.9124
Epoch 60/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1609 - acc: 0.9316 - val_loss: 0.2037 - val_acc: 0.9086
Epoch 61/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1633 - acc: 0.9329 - val_loss: 0.1996 - val_acc: 0.9131
Epoch 62/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1589 - acc: 0.9347 - val_loss: 0.1978 - val_acc: 0.9139
Epoch 63/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1598 - acc: 0.9325 - val_loss: 0.1950 - val_acc: 0.9143
Epoch 64/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1549 - acc: 0.9358 - val_loss: 0.1966 - val_acc: 0.9143
Epoch 65/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1513 - acc: 0.9365 - val_loss: 0.1959 - val_acc: 0.9150
Epoch 66/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1542 - acc: 0.9354 - val_loss: 0.2022 - val_acc: 0.9109
Epoch 67/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1505 - acc: 0.9368 - val_loss: 0.1941 - val_acc: 0.9150
Epoch 68/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1505 - acc: 0.9382 - val_loss: 0.1938 - val_acc: 0.9143
Epoch 69/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1484 - acc: 0.9387 - val_loss: 0.1984 - val_acc: 0.9158
Epoch 70/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1451 - acc: 0.9387 - val_loss: 0.1955 - val_acc: 0.9143
Epoch 71/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1431 - acc: 0.9392 - val_loss: 0.1941 - val_acc: 0.9162
Epoch 72/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1452 - acc: 0.9418 - val_loss: 0.1933 - val_acc: 0.9170
Epoch 73/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1417 - acc: 0.9437 - val_loss: 0.1951 - val_acc: 0.9147
Epoch 74/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1426 - acc: 0.9423 - val_loss: 0.1929 - val_acc: 0.9181
Epoch 75/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1418 - acc: 0.9439 - val_loss: 0.1933 - val_acc: 0.9181
Epoch 76/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1373 - acc: 0.9425 - val_loss: 0.1915 - val_acc: 0.9185
Epoch 77/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1322 - acc: 0.9455 - val_loss: 0.1903 - val_acc: 0.9196
Epoch 78/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1352 - acc: 0.9455 - val_loss: 0.1967 - val_acc: 0.9147
Epoch 79/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1309 - acc: 0.9486 - val_loss: 0.1917 - val_acc: 0.9192
Epoch 80/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1330 - acc: 0.9475 - val_loss: 0.1959 - val_acc: 0.9170
Epoch 81/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1309 - acc: 0.9476 - val_loss: 0.1912 - val_acc: 0.9196
Epoch 82/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1331 - acc: 0.9457 - val_loss: 0.1903 - val_acc: 0.9215
Epoch 83/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1296 - acc: 0.9464 - val_loss: 0.1896 - val_acc: 0.9208
Epoch 84/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1320 - acc: 0.9449 - val_loss: 0.1960 - val_acc: 0.9154
Epoch 85/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1227 - acc: 0.9509 - val_loss: 0.1906 - val_acc: 0.9196
Epoch 86/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1274 - acc: 0.9474 - val_loss: 0.1929 - val_acc: 0.9170
Epoch 87/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1228 - acc: 0.9498 - val_loss: 0.1896 - val_acc: 0.9166
Epoch 88/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1225 - acc: 0.9497 - val_loss: 0.1930 - val_acc: 0.9166
Epoch 89/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1230 - acc: 0.9508 - val_loss: 0.1893 - val_acc: 0.9208
Epoch 90/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1195 - acc: 0.9525 - val_loss: 0.1931 - val_acc: 0.9181
Epoch 91/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1166 - acc: 0.9542 - val_loss: 0.1943 - val_acc: 0.9158
Epoch 92/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.1158 - acc: 0.9545 - val_loss: 0.1920 - val_acc: 0.9185
Epoch 93/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.1170 - acc: 0.9527 - val_loss: 0.1922 - val_acc: 0.9181
Epoch 94/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1170 - acc: 0.9529 - val_loss: 0.1890 - val_acc: 0.9211
Epoch 95/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1147 - acc: 0.9550 - val_loss: 0.1904 - val_acc: 0.9196
Epoch 96/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1112 - acc: 0.9548 - val_loss: 0.1887 - val_acc: 0.9211
Epoch 97/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1078 - acc: 0.9567 - val_loss: 0.1897 - val_acc: 0.9211
Epoch 98/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1099 - acc: 0.9569 - val_loss: 0.1969 - val_acc: 0.9173
Epoch 99/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1076 - acc: 0.9573 - val_loss: 0.1946 - val_acc: 0.9192
Epoch 100/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1060 - acc: 0.9583 - val_loss: 0.1964 - val_acc: 0.9173
Epoch 101/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1043 - acc: 0.9569 - val_loss: 0.1944 - val_acc: 0.9196
Epoch 102/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1069 - acc: 0.9572 - val_loss: 0.2014 - val_acc: 0.9147
Epoch 103/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1045 - acc: 0.9578 - val_loss: 0.1926 - val_acc: 0.9196
Epoch 104/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1050 - acc: 0.9574 - val_loss: 0.1903 - val_acc: 0.9219
Epoch 105/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1019 - acc: 0.9600 - val_loss: 0.1929 - val_acc: 0.9211
Epoch 106/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.1012 - acc: 0.9591 - val_loss: 0.1933 - val_acc: 0.9211
Epoch 107/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.1001 - acc: 0.9609 - val_loss: 0.2018 - val_acc: 0.9181
Epoch 108/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.1014 - acc: 0.9609 - val_loss: 0.1948 - val_acc: 0.9211
Epoch 109/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.0987 - acc: 0.9601 - val_loss: 0.1924 - val_acc: 0.9192
Epoch 110/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.0963 - acc: 0.9613 - val_loss: 0.1921 - val_acc: 0.9192
Epoch 111/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.0950 - acc: 0.9630 - val_loss: 0.1944 - val_acc: 0.9192
Epoch 112/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0936 - acc: 0.9619 - val_loss: 0.1962 - val_acc: 0.9211
Epoch 113/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0941 - acc: 0.9634 - val_loss: 0.1896 - val_acc: 0.9211
Epoch 114/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0929 - acc: 0.9616 - val_loss: 0.1948 - val_acc: 0.9204
Epoch 115/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0924 - acc: 0.9624 - val_loss: 0.1944 - val_acc: 0.9219
Epoch 116/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0920 - acc: 0.9634 - val_loss: 0.1917 - val_acc: 0.9238
Epoch 117/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0922 - acc: 0.9644 - val_loss: 0.1939 - val_acc: 0.9211
Epoch 118/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0852 - acc: 0.9675 - val_loss: 0.1962 - val_acc: 0.9185
Epoch 119/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0902 - acc: 0.9642 - val_loss: 0.1942 - val_acc: 0.9204
Epoch 120/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0891 - acc: 0.9655 - val_loss: 0.1904 - val_acc: 0.9227
Epoch 121/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0877 - acc: 0.9656 - val_loss: 0.2054 - val_acc: 0.9147
Epoch 122/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0857 - acc: 0.9676 - val_loss: 0.1937 - val_acc: 0.9234
Epoch 123/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0842 - acc: 0.9680 - val_loss: 0.1928 - val_acc: 0.9230
Epoch 124/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0863 - acc: 0.9671 - val_loss: 0.1925 - val_acc: 0.9215
Epoch 125/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0823 - acc: 0.9697 - val_loss: 0.1910 - val_acc: 0.9238
Epoch 126/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0846 - acc: 0.9664 - val_loss: 0.1920 - val_acc: 0.9215
Epoch 127/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0821 - acc: 0.9671 - val_loss: 0.1918 - val_acc: 0.9211
Epoch 128/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0833 - acc: 0.9673 - val_loss: 0.1917 - val_acc: 0.9227
Epoch 129/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0788 - acc: 0.9702 - val_loss: 0.1929 - val_acc: 0.9246
Epoch 130/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0828 - acc: 0.9672 - val_loss: 0.1901 - val_acc: 0.9234
Epoch 131/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0774 - acc: 0.9711 - val_loss: 0.2004 - val_acc: 0.9185
Epoch 132/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0797 - acc: 0.9695 - val_loss: 0.1898 - val_acc: 0.9242
Epoch 133/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0760 - acc: 0.9717 - val_loss: 0.1976 - val_acc: 0.9230
Epoch 134/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0745 - acc: 0.9718 - val_loss: 0.2008 - val_acc: 0.9204
Epoch 135/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0780 - acc: 0.9699 - val_loss: 0.2017 - val_acc: 0.9215
Epoch 136/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0750 - acc: 0.9722 - val_loss: 0.1935 - val_acc: 0.9238
Epoch 137/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0701 - acc: 0.9749 - val_loss: 0.1954 - val_acc: 0.9219
Epoch 138/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0762 - acc: 0.9706 - val_loss: 0.1961 - val_acc: 0.9211
Epoch 139/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0722 - acc: 0.9737 - val_loss: 0.1951 - val_acc: 0.9246
Epoch 140/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0717 - acc: 0.9739 - val_loss: 0.1961 - val_acc: 0.9242
Epoch 141/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0735 - acc: 0.9724 - val_loss: 0.1946 - val_acc: 0.9215
Epoch 142/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0722 - acc: 0.9737 - val_loss: 0.1926 - val_acc: 0.9250
Epoch 143/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0678 - acc: 0.9763 - val_loss: 0.1975 - val_acc: 0.9215
Epoch 144/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.0693 - acc: 0.9741 - val_loss: 0.1965 - val_acc: 0.9230
Epoch 145/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0672 - acc: 0.9754 - val_loss: 0.2021 - val_acc: 0.9223
Epoch 146/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0700 - acc: 0.9747 - val_loss: 0.1967 - val_acc: 0.9227
Epoch 147/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0672 - acc: 0.9745 - val_loss: 0.1922 - val_acc: 0.9284
Epoch 148/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0660 - acc: 0.9748 - val_loss: 0.1976 - val_acc: 0.9230
Epoch 149/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0684 - acc: 0.9754 - val_loss: 0.1989 - val_acc: 0.9223
Epoch 150/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.0681 - acc: 0.9759 - val_loss: 0.1914 - val_acc: 0.9250
Epoch 151/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0647 - acc: 0.9765 - val_loss: 0.1919 - val_acc: 0.9242
Epoch 152/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0618 - acc: 0.9779 - val_loss: 0.1962 - val_acc: 0.9234
Epoch 153/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0614 - acc: 0.9764 - val_loss: 0.1996 - val_acc: 0.9238
Epoch 154/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0646 - acc: 0.9757 - val_loss: 0.2007 - val_acc: 0.9253
Epoch 155/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.0639 - acc: 0.9776 - val_loss: 0.1942 - val_acc: 0.9253
Epoch 156/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0611 - acc: 0.9776 - val_loss: 0.1960 - val_acc: 0.9234
Epoch 157/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0613 - acc: 0.9772 - val_loss: 0.1993 - val_acc: 0.9219
Epoch 158/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0611 - acc: 0.9779 - val_loss: 0.1948 - val_acc: 0.9246
Epoch 159/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0557 - acc: 0.9808 - val_loss: 0.2034 - val_acc: 0.9211
Epoch 160/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0595 - acc: 0.9777 - val_loss: 0.2054 - val_acc: 0.9215
Epoch 161/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0610 - acc: 0.9771 - val_loss: 0.1947 - val_acc: 0.9238
Epoch 162/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0560 - acc: 0.9797 - val_loss: 0.1975 - val_acc: 0.9211
Epoch 163/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0593 - acc: 0.9794 - val_loss: 0.1953 - val_acc: 0.9227
Epoch 164/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0554 - acc: 0.9811 - val_loss: 0.1993 - val_acc: 0.9211
Epoch 165/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0541 - acc: 0.9816 - val_loss: 0.1977 - val_acc: 0.9230
Epoch 166/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0563 - acc: 0.9808 - val_loss: 0.2029 - val_acc: 0.9215
Epoch 167/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0570 - acc: 0.9794 - val_loss: 0.1997 - val_acc: 0.9230
Epoch 168/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0576 - acc: 0.9792 - val_loss: 0.1982 - val_acc: 0.9227
Epoch 169/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0574 - acc: 0.9788 - val_loss: 0.2032 - val_acc: 0.9230
Epoch 170/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.0556 - acc: 0.9798 - val_loss: 0.1965 - val_acc: 0.9246
Epoch 171/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0537 - acc: 0.9806 - val_loss: 0.2014 - val_acc: 0.9234
Epoch 172/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0535 - acc: 0.9818 - val_loss: 0.1966 - val_acc: 0.9269
Epoch 173/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.0532 - acc: 0.9806 - val_loss: 0.1973 - val_acc: 0.9284
Epoch 174/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.0527 - acc: 0.9820 - val_loss: 0.2018 - val_acc: 0.9227
Epoch 175/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.0513 - acc: 0.9822 - val_loss: 0.2034 - val_acc: 0.9219
Epoch 176/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.0482 - acc: 0.9833 - val_loss: 0.1983 - val_acc: 0.9253
Epoch 177/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.0519 - acc: 0.9807 - val_loss: 0.1973 - val_acc: 0.9272
Epoch 178/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.0521 - acc: 0.9814 - val_loss: 0.2012 - val_acc: 0.9234
Epoch 179/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.0487 - acc: 0.9828 - val_loss: 0.1944 - val_acc: 0.9265
Epoch 180/200
14875/14875 [==============================] - 1s 38us/step - loss: 0.0491 - acc: 0.9835 - val_loss: 0.2000 - val_acc: 0.9246
Epoch 181/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.0468 - acc: 0.9847 - val_loss: 0.1972 - val_acc: 0.9280
Epoch 182/200
14875/14875 [==============================] - 1s 37us/step - loss: 0.0514 - acc: 0.9832 - val_loss: 0.2061 - val_acc: 0.9227
Epoch 183/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0496 - acc: 0.9822 - val_loss: 0.1966 - val_acc: 0.9280
Epoch 184/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0482 - acc: 0.9834 - val_loss: 0.2000 - val_acc: 0.9234
Epoch 185/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0460 - acc: 0.9834 - val_loss: 0.1973 - val_acc: 0.9261
Epoch 186/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0466 - acc: 0.9845 - val_loss: 0.1998 - val_acc: 0.9253
Epoch 187/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0475 - acc: 0.9835 - val_loss: 0.2099 - val_acc: 0.9223
Epoch 188/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0457 - acc: 0.9850 - val_loss: 0.2061 - val_acc: 0.9257
Epoch 189/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0434 - acc: 0.9857 - val_loss: 0.2045 - val_acc: 0.9250
Epoch 190/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0471 - acc: 0.9843 - val_loss: 0.2005 - val_acc: 0.9257
Epoch 191/200
14875/14875 [==============================] - 1s 36us/step - loss: 0.0433 - acc: 0.9855 - val_loss: 0.2001 - val_acc: 0.9261
Epoch 192/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0463 - acc: 0.9836 - val_loss: 0.1993 - val_acc: 0.9276
Epoch 193/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0446 - acc: 0.9843 - val_loss: 0.1996 - val_acc: 0.9265
Epoch 194/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0429 - acc: 0.9866 - val_loss: 0.1992 - val_acc: 0.9257
Epoch 195/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0415 - acc: 0.9864 - val_loss: 0.1980 - val_acc: 0.9269
Epoch 196/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0405 - acc: 0.9861 - val_loss: 0.2056 - val_acc: 0.9265
Epoch 197/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0424 - acc: 0.9860 - val_loss: 0.2027 - val_acc: 0.9246
Epoch 198/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0427 - acc: 0.9864 - val_loss: 0.2046 - val_acc: 0.9238
Epoch 199/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0391 - acc: 0.9877 - val_loss: 0.2002 - val_acc: 0.9257
Epoch 200/200
14875/14875 [==============================] - 1s 35us/step - loss: 0.0382 - acc: 0.9891 - val_loss: 0.2061 - val_acc: 0.9246
```
In [29]:
```py
import matplotlib.pyplot as plt
%matplotlib inline
accuracy = history1.history['acc']
val_accuracy = history1.history['val_acc']
loss = history1.history['loss']
val_loss = history1.history['val_loss']
epochs = range(len(accuracy))
plt.plot(epochs, accuracy, 'bo', label='Training accuracy')
plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')
plt.title('CNN result Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('cnn Training and validation loss')
plt.legend()
plt.show()
```
# so we have pca with MLP as well as with CNN
In [30]:
```py
%%time
X_tst = []
Test_imgs = []
for img_id in tqdm_notebook(os.listdir(test_dir)):
X_tst.append(cv2.imread(test_dir + img_id,0))
Test_imgs.append(img_id)
X_tst = np.asarray(X_tst)
X_tst = X_tst.astype('float32')
X_tst /= 255
```
```
CPU times: user 620 ms, sys: 624 ms, total: 1.24 s
Wall time: 8.4 s
```
# so now we will be again applyig PCA on test set and predict with cnn model let's how much accuracy we can bring
In [31]:
```py
X_tst.shape
```
Out[31]:
```
(4000, 32, 32)
```
In [32]:
```py
X_tst=X_tst.reshape(-1,32,32,1)
```
In [33]:
```py
test_path=[]
for i in os.listdir(test_dir):
test_path.append(i)
```
In [34]:
```py
test_dataframe=pd.DataFrame(data=test_path,columns=["id"])
```
In [35]:
```py
test_dataframe.head()
```
Out[35]:
| | id |
| --- | --- |
| 0 | 79ac4cc3b082e0a1defe1be601806efd.jpg |
| --- | --- |
| 1 | e880364d6521c6f3a27748ec62b0e335.jpg |
| --- | --- |
| 2 | 74912492b6cdf28c4bfb9c8e1d35af3e.jpg |
| --- | --- |
| 3 | 078cfa961183b30693ea2f13f5ff6d17.jpg |
| --- | --- |
| 4 | 7fd729184ef182899ce3e7a174fb9bc0.jpg |
| --- | --- |
In [36]:
```py
import os,array
import pandas as pd
import time
import dask as dd
from PIL import Image
def pixelconv(file_list,img_height,img_width,pixels):
columnNames = list()
for i in range(pixels):
pixel = 'pixel'
pixel += str(i)
columnNames.append(pixel)
train_data = pd.DataFrame(columns = columnNames)
start_time = time.time()
for i in file_list:
t = i
img_name = t
img = Image.open('../input/test/test/'+img_name)
rawData = img.load()
#print rawData
data = []
for y in range(img_height):
for x in range(img_width):
data.append(rawData[x,y][0])
print (i)
k = 0
#print data
train_data.loc[i] = [data[k] for k in range(pixels)]
#print train_data.loc[0]
print ("Done pixel values conversion")
print (time.time()-start_time)
print (train_data)
train_data.to_csv("test_converted_new.csv",index = False)
print ("Done data frame conversion")
print (time.time()-start_time)
pixelconv(test_dataframe.id,32,32,1024) # pass pandas dataframe in which path of images only as column
# in return csv file will save in working directory
```
```
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c1a0f4eee85933112caefe7037586fc3.jpg 159 145 ... 196 169
28f1df01c205dede3924ad332bc8849c.jpg 101 91 ... 124 107
36ea7ab27d0d631bd68c93cec8ddb6aa.jpg 99 128 ... 105 104
346ad07f2436ff12e653e50c3df48a4a.jpg 78 89 ... 107 75
82b50e7749c74ca307c7874efdac1ce0.jpg 129 123 ... 104 104
e6a8b275dc191bd9a277c261c593a640.jpg 91 96 ... 96 100
96ad3d3813ea8ead501bb262d90e2a44.jpg 111 100 ... 115 106
1288d1689880a9f0ad17c047575d85b8.jpg 72 91 ... 113 102
2c696471585dffd4c73e5ab728bc095c.jpg 60 67 ... 146 150
a6c04a43ba6b9052c7373f43e943141b.jpg 118 130 ... 189 159
8e6888ec1ad25a9d1826eff6dda21409.jpg 93 99 ... 75 59
47567975e3a8075bdc3d65015a386442.jpg 158 149 ... 106 133
97655c74928a8ba0e01a88401b552622.jpg 51 47 ... 93 47
0a2339bcf84c99c90c95ff956f9245b4.jpg 68 69 ... 133 128
518287d102fcb154fc3e487083b9c9f1.jpg 86 98 ... 56 108
474ed48e38c6b36dfb19a539b9ece947.jpg 144 142 ... 196 186
a21f856603a60ec285ace0a722b533eb.jpg 10 47 ... 94 110
d3c9c3a2e07ad3414e6c1bbde1ced2b0.jpg 100 102 ... 83 79
69e4778a6a1da28b7ba84ed8d0e2e6cc.jpg 134 128 ... 145 136
8b9e4e9898b36311d315db3ee8adddac.jpg 75 71 ... 37 51
051d0b6becb641f28c99c7d9b9cc8bd8.jpg 83 87 ... 178 224
a715480e25a3178372affa70f34612d6.jpg 89 76 ... 90 86
b1cc76011c5432d57156c5da95d6cee4.jpg 131 137 ... 210 216
df4f6a2f51e27b71089117a916d9d395.jpg 67 68 ... 148 151
[4000 rows x 1024 columns]
Done data frame conversion
50.40800619125366
```
In [37]:
```py
new_test=pd.read_csv("../working/test_converted_new.csv")
```
In [38]:
```py
X_tst=pca.transform(new_test)
```
In [39]:
```py
X_tst.shape
```
Out[39]:
```
(4000, 625)
```
In [40]:
```py
X_tst=X_tst.reshape(-1,25,25,1)
```
In [41]:
```py
X_tst.shape
```
Out[41]:
```
(4000, 25, 25, 1)
```
# CNN with Focal Loss , hey man i should foucs tuff example to classify
The focal loss was proposed for dense object detection task early this year. It enables training highly accurate dense object detectors with an imbalance between foreground and background classes at 1:1000 scale. This tutorial will show you how to apply focal loss to train a multi-class classifier model given highly imbalanced datasets.
Background Let's first take a look at other treatments for imbalanced datasets, and how focal loss comes to solve the issue.
In multi-class classification, a balanced dataset has target labels that are evenly distributed. If one class has overwhelmingly more samples than another, it can be seen as an imbalanced dataset. This imbalance causes two problems:
Training is inefficient as most samples are easy examples that contribute no useful learning signal; The easy examples can overwhelm training and lead to degenerate models. A common solution is to perform some form of hard negative mining that samples hard examples during training or more complex sampling/reweighing schemes.
For image classification specific, data augmentation techniques are also variable to create synthetic data for under-represented classes.
The focal loss is designed to address class imbalance by down-weighting inliers (easy examples) such that their contribution to the total loss is small even if their number is large. It focuses on training a sparse set of hard examples.
In [42]:
```py
train_df["image_location"]=train_dir+train_df["id"]
```
In [43]:
```py
import tensorflow as tf
from keras import backend as K
```
In [44]:
```py
def binary_focal_loss(gamma=2., alpha=.25):
"""
Binary form of focal loss.
FL(p_t) = -alpha * (1 - p_t)**gamma * log(p_t)
where p = sigmoid(x), p_t = p or 1 - p depending on if the label is 1 or 0, respectively.
References:
https://arxiv.org/pdf/1708.02002.pdf
Usage:
model.compile(loss=[binary_focal_loss(alpha=.25, gamma=2)], metrics=["accuracy"], optimizer=adam)
"""
def binary_focal_loss_fixed(y_true, y_pred):
"""
:param y_true: A tensor of the same shape as `y_pred`
:param y_pred: A tensor resulting from a sigmoid
:return: Output tensor.
"""
pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
epsilon = K.epsilon()
# clip to prevent NaN's and Inf's
pt_1 = K.clip(pt_1, epsilon, 1. - epsilon)
pt_0 = K.clip(pt_0, epsilon, 1. - epsilon)
return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) \
-K.sum((1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0))
return binary_focal_loss_fixed
```
# image augmentation we are using at runtime but to do that we need few things , i'm using a script to pass respective images in their respective label folder
# so that script will work in smooth process , make unique set of labels folder first then image mapping we will using and pass every image in their respective folder
In [45]:
```py
train_df.head()
```
Out[45]:
| | id | image_location |
| --- | --- | --- |
| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | ../input/train/train/0004be2cfeaba1c0361d39e2b... |
| --- | --- | --- |
| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | ../input/train/train/000c8a36845c0208e833c79c1... |
| --- | --- | --- |
| 2 | 000d1e9a533f62e55c289303b072733d.jpg | ../input/train/train/000d1e9a533f62e55c289303b... |
| --- | --- | --- |
| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | ../input/train/train/0011485b40695e9138e92d0b3... |
| --- | --- | --- |
| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | ../input/train/train/0014d7a11e90b62848904c141... |
| --- | --- | --- |
In [46]:
```py
#from console_progressbar import ProgressBar
import shutil
import tqdm
import os
filenames=list(train_df["image_location"].values)
labels=list(train_df["has_cactus"].values)
folders_to_be_created = np.unique(list(train_df['has_cactus'].values))
files=[]
path="../working/trainset/"
for i in folders_to_be_created:
if not os.path.exists(path+str(i)):
os.makedirs(path+str(i))
#pb = ProgressBar(total=100, prefix='Save valid data', suffix='', decimals=3, length=50, fill='=')
for f in tqdm_notebook(range(len(filenames))):
current_image=filenames[f]
current_label=labels[f]
src_path=current_image
dst_path =path+str(current_label)
try :
shutil.copy(src_path, dst_path)
#pb.print_progress_bar((f + 1) * 100 / 4000)
except Exception as e :
files.append(src_path)
```
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3077 try:
-> 3078 return self._engine.get_loc(key)
3079 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'has_cactus'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
in ()
4 import os
5 filenames=list(train_df["image_location"].values)
----> 6 labels=list(train_df["has_cactus"].values)
7 folders_to_be_created = np.unique(list(train_df['has_cactus'].values))
8 files=[]
/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py in __getitem__(self, key)
2686 return self._getitem_multilevel(key)
2687 else:
-> 2688 return self._getitem_column(key)
2689
2690 def _getitem_column(self, key):
/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py in _getitem_column(self, key)
2693 # get column
2694 if self.columns.is_unique:
-> 2695 return self._get_item_cache(key)
2696
2697 # duplicate columns & possible reduce dimensionality
/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
2487 res = cache.get(item)
2488 if res is None:
-> 2489 values = self._data.get(item)
2490 res = self._box_item_values(item, values)
2491 cache[item] = res
/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py in get(self, item, fastpath)
4113
4114 if not isna(item):
-> 4115 loc = self.items.get_loc(item)
4116 else:
4117 indexer = np.arange(len(self.items))[isna(self.items)]
/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3078 return self._engine.get_loc(key)
3079 except KeyError:
-> 3080 return self._engine.get_loc(self._maybe_cast_indexer(key))
3081
3082 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'has_cactus'
```
# image augumentation begins
# we are using subset in imagedatagenerator therefore whatever augumentation we are applying will not get introduced in validation
In [47]:
```py
import keras
from keras_preprocessing.image import ImageDataGenerator
from keras.applications.vgg16 import preprocess_input
train_datagen = ImageDataGenerator(
rescale = 1./255,
# preprocessing_function= preprocess_input,
#shear_range=0.2,
zoom_range=0.2,
fill_mode = 'reflect',
#cval = 1,
rotation_range = 30,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,validation_split=.20)
valid_datagen = ImageDataGenerator(rescale=1./255)#,preprocessing_function=preprocess_input)
train_generator = train_datagen.flow_from_directory(
directory='../working/trainset/',
target_size=(32, 32),
batch_size=32,
class_mode='binary',subset="training")
validation_generator = train_datagen.flow_from_directory(
directory='../working/trainset/',
target_size=(32,32),
batch_size=32,
class_mode='binary',subset="validation")
```
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
in ()
20 target_size=(32, 32),
21 batch_size=32,
---> 22 class_mode='binary',subset="training") 23
24 validation_generator = train_datagen.flow_from_directory(
/opt/conda/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py in flow_from_directory(self, directory, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation)
536 follow_links=follow_links,
537 subset=subset,
--> 538 interpolation=interpolation
539 )
540
/opt/conda/lib/python3.6/site-packages/keras_preprocessing/image/directory_iterator.py in __init__(self, directory, image_data_generator, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, data_format, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, dtype)
103 if not classes:
104 classes = []
--> 105 for subdir in sorted(os.listdir(directory)):
106 if os.path.isdir(os.path.join(directory, subdir)):
107 classes.append(subdir)
FileNotFoundError: [Errno 2] No such file or directory: '../working/trainset/'
```
# Applying VGG-16 First
In [48]:
```py
import cv2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import os
from tqdm import tqdm, tqdm_notebook
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.applications import VGG16
from keras.optimizers import Adam
```
In [49]:
```py
vgg16_net = VGG16(weights='imagenet',
include_top=False,
input_shape=(32, 32, 3))
```
```
Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
58892288/58889256 [==============================] - 2s 0us/step
```
In [50]:
```py
vgg16_net.trainable = False
vgg16_net.summary()
```
```
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 32, 32, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 32, 32, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 32, 32, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 16, 16, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 16, 16, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 16, 16, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 8, 8, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 8, 8, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 8, 8, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 8, 8, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 4, 4, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 4, 4, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 4, 4, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 4, 4, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 2, 2, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 2, 2, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 2, 2, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 2, 2, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 1, 1, 512) 0
=================================================================
Total params: 14,714,688
Trainable params: 0
Non-trainable params: 14,714,688
_________________________________________________________________
```
In [51]:
```py
model = Sequential()
model.add(vgg16_net)
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
```
In [52]:
```py
model.compile(loss=[binary_focal_loss(alpha=.25, gamma=2)], metrics=["accuracy"], optimizer=Adam(lr=1e-5))
```
In [53]:
```py
history=model.fit_generator(train_generator,
steps_per_epoch = 14001//32,
epochs=200,
validation_data = validation_generator,validation_steps=3499//32)
```
```
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
in ()
----> 1 history=model.fit_generator(train_generator, 2 steps_per_epoch = 14001//32,
3 epochs=200,
4 validation_data = validation_generator,validation_steps=3499//32)
NameError: name 'train_generator' is not defined
```
In [54]:
```py
import matplotlib.pyplot as plt
import numpy as np
#plot of model epochs what has been happened within 100 epochs baseline with vgg16 training 135 million params
# freezing first layer
fig = plt.figure(figsize=(12,8))
plt.plot(history.history['acc'],'blue')
plt.plot(history.history['val_acc'],'orange')
plt.xticks(np.arange(0, 100, 10))
plt.yticks(np.arange(0,1,.1))
plt.rcParams['figure.figsize'] = (10, 10)
plt.xlabel("Num of Epochs")
plt.ylabel("Accuracy")
plt.title("Training Accuracy vs Validation Accuracy")
plt.grid(True)
plt.gray()
plt.legend(['train','validation'])
plt.show()
plt.figure(1)
plt.plot(history.history['loss'],'blue')
plt.plot(history.history['val_loss'],'orange')
plt.xticks(np.arange(0, 100, 10))
plt.rcParams['figure.figsize'] = (10, 10)
plt.xlabel("Num of Epochs")
plt.ylabel("Loss")
plt.title("Training Loss vs Validation Loss")
plt.grid(True)
plt.gray()
plt.legend(['train','validation'])
plt.show()
```
In [55]:
```py
%%time
X_tst = []
Test_imgs = []
for img_id in tqdm_notebook(os.listdir(test_dir)):
X_tst.append(cv2.imread(test_dir + img_id))
Test_imgs.append(img_id)
X_tst = np.asarray(X_tst)
X_tst = X_tst.astype('float32')
X_tst /= 255
```
```
CPU times: user 500 ms, sys: 368 ms, total: 868 ms
Wall time: 3.5 s
```
In [56]:
```py
# Prediction
test_predictions = model.predict(X_tst)
```
In [57]:
```py
sub_df = pd.DataFrame(test_predictions, columns=['has_cactus'])
sub_df['has_cactus'] = sub_df['has_cactus'].apply(lambda x: 1 if x > 0.5 else 0)
```
In [58]:
```py
sub_df['id'] = ''
cols = sub_df.columns.tolist()
cols = cols[-1:] + cols[:-1]
sub_df=sub_df[cols]
```
In [59]:
```py
for i, img in enumerate(Test_imgs):
sub_df.set_value(i,'id',img)
```
```
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead
```
In [60]:
```py
sub_df.head()
```
Out[60]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 79ac4cc3b082e0a1defe1be601806efd.jpg | 1 |
| --- | --- | --- |
| 1 | e880364d6521c6f3a27748ec62b0e335.jpg | 1 |
| --- | --- | --- |
| 2 | 74912492b6cdf28c4bfb9c8e1d35af3e.jpg | 1 |
| --- | --- | --- |
| 3 | 078cfa961183b30693ea2f13f5ff6d17.jpg | 1 |
| --- | --- | --- |
| 4 | 7fd729184ef182899ce3e7a174fb9bc0.jpg | 1 |
| --- | --- | --- |
In [61]:
```py
sub_df.to_csv('submission.csv',index=False)
```
# Applying Resnet-50 and then after that we can compare results what we get from these models
In [62]:
```py
from tensorflow.python.keras.applications import ResNet50
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten, GlobalAveragePooling2D, BatchNormalization
from tensorflow.python.keras.applications.resnet50 import preprocess_input
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.preprocessing.image import load_img, img_to_array
```
In [63]:
```py
model = Sequential()
model.add(ResNet50(include_top=False, pooling='avg', weights='imagenet'))
model.add(Flatten())
model.add(BatchNormalization())
model.add(Dense(2048, activation='relu'))
model.add(BatchNormalization())
model.add(Dense(1024, activation='relu'))
model.add(BatchNormalization())
model.add(Dense(1, activation='sigmoid'))
model.layers[0].trainable = False
```
```
Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
94658560/94653016 [==============================] - 2s 0us/step
```
In [64]:
```py
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
```
In [65]:
```py
history1=model.fit_generator(train_generator,
steps_per_epoch = 14001//32,
epochs=200,
validation_data = validation_generator,validation_steps=3499//32)
```
```
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
in ()
----> 1 history1=model.fit_generator(train_generator, 2 steps_per_epoch = 14001//32,
3 epochs=200,
4 validation_data = validation_generator,validation_steps=3499//32)
NameError: name 'train_generator' is not defined
```
# Conclusion
After experiments with parameters of the used models I came to the result of 0.89+ accuracy on the part of the test set, but I am unable to improve it.
Because dataset is build with images of handwritten digits getting bigger train set could help. Maybe I need to change approach, ignore PCA decomposition at all and use ImageDataGenerator to generate more images or use convolution layers.
If you know how above result can be (easily?) improved, please leave comment with suggestion. As data science newbie I would be grateful for any suggestions :)
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/simple-cnn-on-pytorch-for-beginers.md
================================================
# SImple CNN on PyTorch for beginers
> Author: https://www.kaggle.com/bonhart
> From: https://www.kaggle.com/bonhart/simple-cnn-on-pytorch-for-beginers
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
**EDA** (Exploratory Data Analysis).
The purpose of EDA is:
* Look at the data
* Understand the distribution of two classes (hasn't cactus / has cactus)
* Look at some features of the image (distribution of RGB channels, average brightness, etc.)
In [1]:
```py
# Libreries
import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
```
In [2]:
```py
# Data path
labels = pd.read_csv('../input/train.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train_path = '../input/train/train/'
test_path = '../input/test/test/'
```
In [3]:
```py
print('Num train samples:{0}'.format(len(os.listdir(train_path))))
print('Num test samples:{0}'.format(len(os.listdir(test_path))))
```
```
Num train samples:17500
Num test samples:4000
```
In [4]:
```py
labels.head()
```
Out[4]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |
| --- | --- | --- |
| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |
| --- | --- | --- |
| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |
| --- | --- | --- |
| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |
| --- | --- | --- |
| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |
| --- | --- | --- |
In [5]:
```py
labels['has_cactus'].value_counts()
```
Out[5]:
```
1 13136
0 4364
Name: has_cactus, dtype: int64
```
In [6]:
```py
lab = 'Has cactus','Hasn\'t cactus'
colors=['green','brown']
plt.figure(figsize=(7,7))
plt.pie(labels.groupby('has_cactus').size(), labels=lab,
labeldistance=1.1, autopct='%1.1f%%',
colors=colors,shadow=True, startangle=140)
plt.show()
```
**Has cactus**
In [7]:
```py
fig,ax = plt.subplots(1,5,figsize=(15,3))
for i, idx in enumerate(labels[labels['has_cactus']==1]['id'][-5:]):
path = os.path.join(train_path,idx)
ax[i].imshow(cv2.imread(path)) # [...,[2,1,0]]
```
**Hasn't cactus**
In [8]:
```py
fig,ax = plt.subplots(1,5,figsize=(15,3))
for i, idx in enumerate(labels[labels['has_cactus']==0]['id'][-5:]):
path = os.path.join(train_path,idx)
ax[i].imshow(cv2.imread(path)) # [...,[2,1,0]]
```
**convolutional neural network on pytorch from scratch**
In [9]:
```py
# Libreries
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import TensorDataset, DataLoader, Dataset
import torchvision
import torchvision.transforms as transforms
from sklearn.model_selection import train_test_split
```
In [10]:
```py
## Parameters for model
# Hyper parameters
num_epochs = 25
num_classes = 2
batch_size = 128
learning_rate = 0.002
# Device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
```
In [11]:
```py
# data splitting
train, val = train_test_split(labels, stratify=labels.has_cactus, test_size=0.1)
train.shape, val.shape
```
Out[11]:
```
((15750, 2), (1750, 2))
```
Checking label distribution(must be 1:3)
In [12]:
```py
train['has_cactus'].value_counts()
```
Out[12]:
```
1 11822
0 3928
Name: has_cactus, dtype: int64
```
In [13]:
```py
val['has_cactus'].value_counts()
```
Out[13]:
```
1 1314
0 436
Name: has_cactus, dtype: int64
```
**Simple custom generator**
In [14]:
```py
# NOTE: class is inherited from Dataset
class MyDataset(Dataset):
def __init__(self, df_data, data_dir = './', transform=None):
super().__init__()
self.df = df_data.values
self.data_dir = data_dir
self.transform = transform
def __len__(self):
return len(self.df)
def __getitem__(self, index):
img_name,label = self.df[index]
img_path = os.path.join(self.data_dir, img_name)
image = cv2.imread(img_path)
if self.transform is not None:
image = self.transform(image)
return image, label
```
In [15]:
```py
# Image preprocessing
trans_train = transforms.Compose([transforms.ToPILImage(),
transforms.Pad(32, padding_mode='reflect'),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5])])
trans_valid = transforms.Compose([transforms.ToPILImage(),
transforms.Pad(32, padding_mode='reflect'),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5])])
# Data generators
dataset_train = MyDataset(df_data=train, data_dir=train_path, transform=trans_train)
dataset_valid = MyDataset(df_data=val, data_dir=train_path, transform=trans_valid)
loader_train = DataLoader(dataset = dataset_train, batch_size=batch_size, shuffle=True, num_workers=0)
loader_valid = DataLoader(dataset = dataset_valid, batch_size=batch_size//2, shuffle=False, num_workers=0)
```
**Model**
In [16]:
```py
# NOTE: class is inherited from nn.Module
class SimpleCNN(nn.Module):
def __init__(self):
# ancestor constructor call
super(SimpleCNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=2)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=2)
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=2)
self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=2)
self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=2)
self.bn1 = nn.BatchNorm2d(32)
self.bn2 = nn.BatchNorm2d(64)
self.bn3 = nn.BatchNorm2d(128)
self.bn4 = nn.BatchNorm2d(256)
self.bn5 = nn.BatchNorm2d(512)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.avg = nn.AvgPool2d(4)
self.fc = nn.Linear(512 * 1 * 1, 2) # !!!
def forward(self, x):
x = self.pool(F.leaky_relu(self.bn1(self.conv1(x)))) # first convolutional layer then batchnorm, then activation then pooling layer.
x = self.pool(F.leaky_relu(self.bn2(self.conv2(x))))
x = self.pool(F.leaky_relu(self.bn3(self.conv3(x))))
x = self.pool(F.leaky_relu(self.bn4(self.conv4(x))))
x = self.pool(F.leaky_relu(self.bn5(self.conv5(x))))
x = self.avg(x)
#print(x.shape) # lifehack to find out the correct dimension for the Linear Layer
x = x.view(-1, 512 * 1 * 1) # !!!
x = self.fc(x)
return x
```
**Important note:** You may notice that in lines with # !!! there is not very clear 128 * 11 * 11\. This is the dimension of the picture before the FC layers (H x W x C), then you have to calculate it manually (in Keras, for example, .Flatten () does everything for you). However, there is one life hack — just make print (x.shape) in forward () (commented out line). You will see the size (batch_size, C, H, W) - you need to multiply everything except the first (batch_size), this will be the first dimension of Linear (), and it is in C H W that you need to "expand" x before feeding to Linear ().
In [17]:
```py
model = SimpleCNN().to(device)
```
In [18]:
```py
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adamax(model.parameters(), lr=learning_rate)
```
In [19]:
```py
# Train the model
total_step = len(loader_train)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(loader_train):
images = images.to(device)
labels = labels.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
```
```
Epoch [1/25], Step [100/124], Loss: 0.0584
Epoch [2/25], Step [100/124], Loss: 0.0095
Epoch [3/25], Step [100/124], Loss: 0.0156
Epoch [4/25], Step [100/124], Loss: 0.0050
Epoch [5/25], Step [100/124], Loss: 0.0051
Epoch [6/25], Step [100/124], Loss: 0.0047
Epoch [7/25], Step [100/124], Loss: 0.0112
Epoch [8/25], Step [100/124], Loss: 0.0156
Epoch [9/25], Step [100/124], Loss: 0.0078
Epoch [10/25], Step [100/124], Loss: 0.0294
Epoch [11/25], Step [100/124], Loss: 0.0055
Epoch [12/25], Step [100/124], Loss: 0.0600
Epoch [13/25], Step [100/124], Loss: 0.0016
Epoch [14/25], Step [100/124], Loss: 0.0073
Epoch [15/25], Step [100/124], Loss: 0.0053
Epoch [16/25], Step [100/124], Loss: 0.0014
Epoch [17/25], Step [100/124], Loss: 0.0036
Epoch [18/25], Step [100/124], Loss: 0.0011
Epoch [19/25], Step [100/124], Loss: 0.0030
Epoch [20/25], Step [100/124], Loss: 0.0025
Epoch [21/25], Step [100/124], Loss: 0.0012
Epoch [22/25], Step [100/124], Loss: 0.0034
Epoch [23/25], Step [100/124], Loss: 0.0060
Epoch [24/25], Step [100/124], Loss: 0.0007
Epoch [25/25], Step [100/124], Loss: 0.0010
```
**Accuracy Check**
In [20]:
```py
# Test the model
model.eval() # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
with torch.no_grad():
correct = 0
total = 0
for images, labels in loader_valid:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Test Accuracy of the model on the 1750 validation images: {} %'.format(100 * correct / total))
# Save the model checkpoint
torch.save(model.state_dict(), 'model.ckpt')
```
```
Test Accuracy of the model on the 1750 validation images: 99.25714285714285 %
```
**CSV submission**
In [21]:
```py
# generator for test data
dataset_valid = MyDataset(df_data=sub, data_dir=test_path, transform=trans_valid)
loader_test = DataLoader(dataset = dataset_valid, batch_size=32, shuffle=False, num_workers=0)
```
In [22]:
```py
model.eval()
preds = []
for batch_i, (data, target) in enumerate(loader_test):
data, target = data.cuda(), target.cuda()
output = model(data)
pr = output[:,1].detach().cpu().numpy()
for i in pr:
preds.append(i)
sub['has_cactus'] = preds
sub.to_csv('sub.csv', index=False)
```
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/simple-cnn.md
================================================
# Simple CNN
> Author: https://www.kaggle.com/mariammohamed
> From: https://www.kaggle.com/mariammohamed/simple-cnn
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
> Score: 0.9995
In [1]:
```py
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
# Any results you write to the current directory are saved as output.
```
```
['train', 'test', 'train.csv', 'sample_submission.csv']
```
In [2]:
```py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
import scipy
import cv2
import keras
```
```
Using TensorFlow backend.
```
In [3]:
```py
import random
```
**Exploration**
In [4]:
```py
train_data = pd.read_csv('../input/train.csv')
```
In [5]:
```py
train_data.shape
```
Out[5]:
```
(17500, 2)
```
In [6]:
```py
train_data.head()
```
Out[6]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |
| --- | --- | --- |
| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |
| --- | --- | --- |
| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |
| --- | --- | --- |
| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |
| --- | --- | --- |
| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |
| --- | --- | --- |
In [7]:
```py
train_data.has_cactus.unique()
```
Out[7]:
```
array([1, 0])
```
In [8]:
```py
train_data.has_cactus.hist()
```
Out[8]:
```
```
In [9]:
```py
train_data.has_cactus.value_counts()
```
Out[9]:
```
1 13136
0 4364
Name: has_cactus, dtype: int64
```
In [10]:
```py
train_data.has_cactus.plot()
```
Out[10]:
```
```
**Model**
In [11]:
```py
def image_generator2(batch_size = 16, all_data=True, shuffle=True, train=True, indexes=None):
while True:
if indexes is None:
if train:
if all_data:
indexes = np.arange(train_data.shape[0])
else:
indexes = np.arange(train_data[:15000].shape[0])
if shuffle:
np.random.shuffle(indexes)
else:
indexes = np.arange(train_data[15000:].shape[0])
N = int(len(indexes) / batch_size)
# Read in each input, perform preprocessing and get labels
for i in range(N):
current_indexes = indexes[i*batch_size: (i+1)*batch_size]
batch_input = []
batch_output = []
for index in current_indexes:
img = mpimg.imread('../input/train/train/' + train_data.id[index])
batch_input += [img]
batch_input += [img[::-1, :, :]]
batch_input += [img[:, ::-1, :]]
batch_input += [np.rot90(img)]
temp_img = np.zeros_like(img)
temp_img[:28, :, :] = img[4:, :, :]
batch_input += [temp_img]
temp_img = np.zeros_like(img)
temp_img[:, :28, :] = img[:, 4:, :]
batch_input += [temp_img]
temp_img = np.zeros_like(img)
temp_img[4:, :, :] = img[:28, :, :]
batch_input += [temp_img]
temp_img = np.zeros_like(img)
temp_img[:, 4:, :] = img[:, :28, :]
batch_input += [temp_img]
batch_input += [cv2.resize(img[2:30, 2:30, :], (32, 32))]
batch_input += [scipy.ndimage.interpolation.rotate(img, 10, reshape=False)]
batch_input += [scipy.ndimage.interpolation.rotate(img, 5, reshape=False)]
for _ in range(11):
batch_output += [train_data.has_cactus[index]]
batch_input = np.array( batch_input )
batch_output = np.array( batch_output )
yield( batch_input, batch_output.reshape(-1, 1) )
```
In [12]:
```py
positive_examples = train_data[train_data.has_cactus==1]
negative_examples = train_data[train_data.has_cactus==0]
```
In [13]:
```py
def augment_img(img):
batch_input = []
batch_input += [img]
batch_input += [img[::-1, :, :]]
batch_input += [img[:, ::-1, :]]
batch_input += [np.rot90(img)]
temp_img = np.zeros_like(img)
temp_img[:28, :, :] = img[4:, :, :]
batch_input += [temp_img]
temp_img = np.zeros_like(img)
temp_img[:, :28, :] = img[:, 4:, :]
batch_input += [temp_img]
temp_img = np.zeros_like(img)
temp_img[4:, :, :] = img[:28, :, :]
batch_input += [temp_img]
temp_img = np.zeros_like(img)
temp_img[:, 4:, :] = img[:, :28, :]
batch_input += [temp_img]
batch_input += [cv2.resize(img[2:30, 2:30, :], (32, 32))]
batch_input += [scipy.ndimage.interpolation.rotate(img, 10, reshape=False)]
batch_input += [scipy.ndimage.interpolation.rotate(img, 5, reshape=False)]
return batch_input
```
In [14]:
```py
def image_generator(batch_size = 8, all_data=True, shuffle=True, train=True, indexes=None):
while True:
if indexes is None:
if train:
indexes = positive_examples.index.tolist()
neg_indexes = negative_examples.index.tolist()
if shuffle:
np.random.shuffle(indexes)
np.random.shuffle(neg_indexes)
N = int(len(indexes) / (batch_size/2))
neg_N = int(len(neg_indexes) / (batch_size/2))
j = 0
# Read in each input, perform preprocessing and get labels
for i in range(N):
current_indexes = indexes[i*(batch_size//2): (i+1)*(batch_size//2)]
current_neg_indexes = neg_indexes[j*(batch_size//2): (j+1)*(batch_size//2)]
j = (j + 1) % neg_N
batch_input = []
batch_output = []
for ind in range(len(current_indexes)):
index = current_indexes[ind]
neg_index = current_neg_indexes[ind]
img = mpimg.imread('../input/train/train/' + train_data.id[index])
batch_input.extend(augment_img(img))
for _ in range(11):
batch_output += [train_data.has_cactus[index]]
neg_img = mpimg.imread('../input/train/train/' + train_data.id[neg_index])
batch_input.extend(augment_img(neg_img))
for _ in range(11):
batch_output += [train_data.has_cactus[neg_index]]
# factor = 0.05
# new_img = factor*neg_img + (1-factor)*img
# batch_input.append(new_img)
# batch_output += [factor*train_data.has_cactus[neg_index]+(1-factor)*train_data.has_cactus[index]]
# factor = 0.95
# new_img = factor*neg_img + (1-factor)*img
# batch_input.append(new_img)
# batch_output += [factor*train_data.has_cactus[neg_index]+(1-factor)*train_data.has_cactus[index]]
batch_input = np.array( batch_input )
batch_output = np.array( batch_output )
yield( batch_input, batch_output.reshape(-1, 1) )
```
In [15]:
```py
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(64, (5, 5), input_shape=(32, 32, 3)))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.LeakyReLU(alpha=0.3))
model.add(keras.layers.Conv2D(64, (5, 5)))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.LeakyReLU(alpha=0.3))
model.add(keras.layers.Conv2D(128, (5, 5)))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.LeakyReLU(alpha=0.3))
model.add(keras.layers.Conv2D(128, (5, 5)))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.LeakyReLU(alpha=0.3))
model.add(keras.layers.Conv2D(256, (3, 3)))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.LeakyReLU(alpha=0.3))
model.add(keras.layers.Conv2D(256, (3, 3)))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.LeakyReLU(alpha=0.3))
model.add(keras.layers.Conv2D(512, (3, 3)))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.LeakyReLU(alpha=0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(100))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.LeakyReLU(alpha=0.3))
model.add(keras.layers.Dense(1, activation='sigmoid'))
```
```
WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
```
In [16]:
```py
model.summary()
```
```
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 28, 28, 64) 4864
_________________________________________________________________
batch_normalization_1 (Batch (None, 28, 28, 64) 256
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 28, 28, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 24, 24, 64) 102464
_________________________________________________________________
batch_normalization_2 (Batch (None, 24, 24, 64) 256
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU) (None, 24, 24, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 20, 20, 128) 204928
_________________________________________________________________
batch_normalization_3 (Batch (None, 20, 20, 128) 512
_________________________________________________________________
leaky_re_lu_3 (LeakyReLU) (None, 20, 20, 128) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 16, 16, 128) 409728
_________________________________________________________________
batch_normalization_4 (Batch (None, 16, 16, 128) 512
_________________________________________________________________
leaky_re_lu_4 (LeakyReLU) (None, 16, 16, 128) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 14, 14, 256) 295168
_________________________________________________________________
batch_normalization_5 (Batch (None, 14, 14, 256) 1024
_________________________________________________________________
leaky_re_lu_5 (LeakyReLU) (None, 14, 14, 256) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 12, 12, 256) 590080
_________________________________________________________________
batch_normalization_6 (Batch (None, 12, 12, 256) 1024
_________________________________________________________________
leaky_re_lu_6 (LeakyReLU) (None, 12, 12, 256) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 10, 10, 512) 1180160
_________________________________________________________________
batch_normalization_7 (Batch (None, 10, 10, 512) 2048
_________________________________________________________________
leaky_re_lu_7 (LeakyReLU) (None, 10, 10, 512) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 51200) 0
_________________________________________________________________
dense_1 (Dense) (None, 100) 5120100
_________________________________________________________________
batch_normalization_8 (Batch (None, 100) 400
_________________________________________________________________
leaky_re_lu_8 (LeakyReLU) (None, 100) 0
_________________________________________________________________
dense_2 (Dense) (None, 1) 101
=================================================================
Total params: 7,913,625
Trainable params: 7,910,609
Non-trainable params: 3,016
_________________________________________________________________
```
In [17]:
```py
opt = keras.optimizers.SGD(lr=0.0001, momentum=0.9, nesterov=True)
model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
```
In [18]:
```py
def step_decay_schedule(initial_lr=1e-3, decay_factor=0.75, step_size=10):
'''
Wrapper function to create a LearningRateScheduler with step decay schedule.
'''
def schedule(epoch):
return initial_lr * (decay_factor ** np.floor(epoch/step_size))
return keras.callbacks.LearningRateScheduler(schedule)
lr_sched = step_decay_schedule(initial_lr=1e-3, decay_factor=0.75, step_size=2)
early_stop = keras.callbacks.EarlyStopping(monitor='loss', patience=3)
model.fit_generator(image_generator(), steps_per_epoch= train_data.shape[0] / 8, epochs=30, callbacks=[lr_sched, early_stop])
```
```
WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Epoch 1/30
2188/2187 [==============================] - 86s 39ms/step - loss: 0.0982 - acc: 0.9642
Epoch 2/30
2188/2187 [==============================] - 81s 37ms/step - loss: 0.0414 - acc: 0.9855
Epoch 3/30
2188/2187 [==============================] - 80s 37ms/step - loss: 0.0200 - acc: 0.9931
Epoch 4/30
2188/2187 [==============================] - 80s 37ms/step - loss: 0.0122 - acc: 0.9961
Epoch 5/30
2188/2187 [==============================] - 80s 37ms/step - loss: 0.0066 - acc: 0.9982
Epoch 6/30
1897/2187 [=========================>....] - ETA: 10s - loss: 0.0050 - acc: 0.9988
```
In [19]:
```py
# def step_decay_schedule(initial_lr=1e-3, decay_factor=0.75, step_size=10):
# '''
# Wrapper function to create a LearningRateScheduler with step decay schedule.
# '''
# def schedule(epoch):
# return initial_lr * (decay_factor ** np.floor(epoch/step_size))
# return keras.callbacks.LearningRateScheduler(schedule)
# lr_sched = step_decay_schedule(initial_lr=1e-3, decay_factor=0.75, step_size=2)
# model.fit_generator(image_generator(), steps_per_epoch= train_data.shape[0] / 8, epochs=20, callbacks=[lr_sched])
```
In [20]:
```py
model.evaluate_generator(image_generator2(), steps=train_data.shape[0]//16)
```
Out[20]:
```
[0.009791018411583233, 0.9962779681069635]
```
In [21]:
```py
# model.evaluate_generator(image_generator(), steps=train_data.shape[0]//8)
```
In [22]:
```py
# keras.backend.eval(model.optimizer.lr.assign(0.00001))
```
In [23]:
```py
# model.fit_generator(image_generator(), steps_per_epoch= train_data.shape[0] / 16, epochs=15)
```
In [24]:
```py
indexes = np.arange(train_data.shape[0])
N = int(len(indexes) / 64)
batch_size = 64
wrong_ind = []
for i in range(N):
current_indexes = indexes[i*64: (i+1)*64]
batch_input = []
batch_output = []
for index in current_indexes:
img = mpimg.imread('../input/train/train/' + train_data.id[index])
batch_input += [img]
batch_output.append(train_data.has_cactus[index])
batch_input = np.array( batch_input )
# batch_output = np.array( batch_output )
model_pred = model.predict_classes(batch_input)
for j in range(len(batch_output)):
if model_pred[j] != batch_output[j]:
wrong_ind.append(i*batch_size+j)
```
In [25]:
```py
len(wrong_ind)
```
Out[25]:
```
28
```
In [26]:
```py
indexes = np.arange(train_data.shape[0])
N = int(len(indexes) / 64)
batch_size = 64
wrong_ind = []
for i in range(N):
current_indexes = indexes[i*64: (i+1)*64]
batch_input = []
batch_output = []
for index in current_indexes:
img = mpimg.imread('../input/train/train/' + train_data.id[index])
batch_input += [img[::-1, :, :]]
batch_output.append(train_data.has_cactus[index])
batch_input = np.array( batch_input )
model_pred = model.predict_classes(batch_input)
for j in range(len(batch_output)):
if model_pred[j] != batch_output[j]:
wrong_ind.append(i*batch_size+j)
```
In [27]:
```py
len(wrong_ind)
```
Out[27]:
```
60
```
In [28]:
```py
indexes = np.arange(train_data.shape[0])
N = int(len(indexes) / 64)
batch_size = 64
wrong_ind = []
for i in range(N):
current_indexes = indexes[i*64: (i+1)*64]
batch_input = []
batch_output = []
for index in current_indexes:
img = mpimg.imread('../input/train/train/' + train_data.id[index])
batch_input += [img[:, ::-1, :]]
batch_output.append(train_data.has_cactus[index])
batch_input = np.array( batch_input )
model_pred = model.predict_classes(batch_input)
for j in range(len(batch_output)):
if model_pred[j] != batch_output[j]:
wrong_ind.append(i*batch_size+j)
```
In [29]:
```py
len(wrong_ind)
```
Out[29]:
```
71
```
In [30]:
```py
!ls ../input/test/test/* | wc -l
```
```
4000
```
In [31]:
```py
test_files = os.listdir('../input/test/test/')
```
In [32]:
```py
len(test_files)
```
Out[32]:
```
4000
```
In [33]:
```py
batch = 40
all_out = []
for i in range(int(4000/batch)):
images = []
for j in range(batch):
img = mpimg.imread('../input/test/test/'+test_files[i*batch + j])
images += [img]
out = model.predict(np.array(images))
all_out += [out]
```
In [34]:
```py
all_out = np.array(all_out).reshape((-1, 1))
```
In [35]:
```py
all_out.shape
```
Out[35]:
```
(4000, 1)
```
In [36]:
```py
sub_file = pd.DataFrame(data = {'id': test_files, 'has_cactus': all_out.reshape(-1).tolist()})
```
In [37]:
```py
sub_file.to_csv('sample_submission.csv', index=False)
```
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/simple-fastai-exercise.md
================================================
# Simple_FastAI_exercise
> Author: https://www.kaggle.com/kenseitrg
> From: https://www.kaggle.com/kenseitrg/simple-fastai-exercise
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
> Score: 1.0000
In [1]:
```py
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
```
In [2]:
```py
from pathlib import Path
from fastai import *
from fastai.vision import *
import torch
```
In [3]:
```py
data_folder = Path("../input")
#data_folder.ls()
```
In [4]:
```py
train_df = pd.read_csv("../input/train.csv")
test_df = pd.read_csv("../input/sample_submission.csv")
```
In [5]:
```py
test_img = ImageList.from_df(test_df, path=data_folder/'test', folder='test')
trfm = get_transforms(do_flip=True, flip_vert=True, max_rotate=10.0, max_zoom=1.1, max_lighting=0.2, max_warp=0.2, p_affine=0.75, p_lighting=0.75)
train_img = (ImageList.from_df(train_df, path=data_folder/'train', folder='train')
.split_by_rand_pct(0.01)
.label_from_df()
.add_test(test_img)
.transform(trfm, size=128)
.databunch(path='.', bs=64, device= torch.device('cuda:0'))
.normalize(imagenet_stats)
)
```
In [6]:
```py
#train_img.show_batch(rows=3, figsize=(7,6))
```
In [7]:
```py
learn = cnn_learner(train_img, models.densenet161, metrics=[error_rate, accuracy])
```
```
Downloading: "https://download.pytorch.org/models/densenet161-8d451a50.pth" to /tmp/.torch/models/densenet161-8d451a50.pth
115730790it [00:06, 17660476.43it/s]
```
In [8]:
```py
#learn.lr_find()
#learn.recorder.plot()
```
In [9]:
```py
lr = 3e-02
learn.fit_one_cycle(5, slice(lr))
```
Total time: 06:22
| epoch | train_loss | valid_loss | error_rate | accuracy | time |
| --- | --- | --- | --- | --- | --- |
| 0 | 0.064186 | 0.090407 | 0.017143 | 0.982857 | 01:25 |
| 1 | 0.039902 | 0.001073 | 0.000000 | 1.000000 | 01:15 |
| 2 | 0.027812 | 0.003891 | 0.000000 | 1.000000 | 01:13 |
| 3 | 0.012305 | 0.000548 | 0.000000 | 1.000000 | 01:14 |
| 4 | 0.002986 | 0.001019 | 0.000000 | 1.000000 | 01:14 |
In [10]:
```py
#learn.unfreeze()
#learn.lr_find()
#learn.recorder.plot()
```
In [11]:
```py
#learn.fit_one_cycle(1, slice(1e-06))
```
In [12]:
```py
#interp = ClassificationInterpretation.from_learner(learn)
#interp.plot_top_losses(9, figsize=(7,6))
```
In [13]:
```py
preds,_ = learn.get_preds(ds_type=DatasetType.Test)
```
In [14]:
```py
test_df.has_cactus = preds.numpy()[:, 0]
```
In [15]:
```py
test_df.to_csv('submission.csv', index=False)
```
================================================
FILE: docs/Kaggle/competitions/playground/aerial-cactus-identification/xdstudent.md
================================================
# XDStudent
> Author: https://www.kaggle.com/xiuchengwang
> From: https://www.kaggle.com/xiuchengwang/xdstudent
> License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
> Score: 0.9999
In [1]:
```py
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
# Any results you write to the current directory are saved as output.
```
```
['test', 'sample_submission.csv', 'train.csv', 'train']
```
In [2]:
```py
from keras.layers import *
from keras.models import Model, Sequential, load_model
from keras import applications
from keras.utils.np_utils import to_categorical
from keras.optimizers import RMSprop, Adam, SGD
from keras.losses import sparse_categorical_crossentropy, binary_crossentropy
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras import backend as K
```
```
Using TensorFlow backend.
```
In [3]:
```py
train = pd.read_csv('../input/train.csv')
train.head()
```
Out[3]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 0004be2cfeaba1c0361d39e2b000257b.jpg | 1 |
| --- | --- | --- |
| 1 | 000c8a36845c0208e833c79c1bffedd1.jpg | 1 |
| --- | --- | --- |
| 2 | 000d1e9a533f62e55c289303b072733d.jpg | 1 |
| --- | --- | --- |
| 3 | 0011485b40695e9138e92d0b3fb55128.jpg | 1 |
| --- | --- | --- |
| 4 | 0014d7a11e90b62848904c1418fc8cf2.jpg | 1 |
| --- | --- | --- |
In [4]:
```py
train['has_cactus'].value_counts()
```
Out[4]:
```
1 13136
0 4364
Name: has_cactus, dtype: int64
```
In [5]:
```py
import matplotlib.pyplot as plt
import tqdm
img = plt.imread('../input/train/train/'+ train['id'][0])
img.shape
```
Out[5]:
```
(32, 32, 3)
```
In [6]:
```py
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, roc_auc_score
x_train, x_test, y_train, y_test = train_test_split(train['id'], train['has_cactus'], test_size = 0.1, random_state = 32)
```
In [7]:
```py
x_train_arr = []
for images in tqdm.tqdm(x_train):
img = plt.imread('../input/train/train/' + images)
x_train_arr.append(img)
x_train_arr = np.array(x_train_arr)
print(x_train_arr.shape)
```
```
100%|██████████| 15750/15750 [00:18<00:00, 861.12it/s]
```
```
(15750, 32, 32, 3)
```
In [8]:
```py
x_test_arr = []
for images in tqdm.tqdm(x_test):
img = plt.imread('../input/train/train/' + images)
x_test_arr.append(img)
x_test_arr = np.array(x_test_arr)
print(x_test_arr.shape)
```
```
100%|██████████| 1750/1750 [00:02<00:00, 759.34it/s]
```
```
(1750, 32, 32, 3)
```
In [9]:
```py
x_train_arr = x_train_arr.astype('float32')
x_test_arr = x_test_arr.astype('float32')
x_train_arr = x_train_arr/255
x_test_arr = x_test_arr/255
```
In [10]:
```py
from keras.applications.densenet import DenseNet201
from keras.layers import *
inputs = Input((32, 32, 3))
base_model = DenseNet201(include_top=False, input_shape=(32, 32, 3))#, weights=None
x = base_model(inputs)
out1 = GlobalMaxPooling2D()(x)
out2 = GlobalAveragePooling2D()(x)
out3 = Flatten()(x)
out = Concatenate(axis=-1)([out1, out2, out3])
out = Dropout(0.5)(out)
out = Dense(256, name="3_")(out)
out = BatchNormalization()(out)
out = Activation("relu")(out)
out = Dense(1, activation="sigmoid", name="3_2")(out)
model = Model(inputs, out)
model.summary()
```
```
Downloading data from https://github.com/keras-team/keras-applications/releases/download/densenet/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5
74842112/74836368 [==============================] - 2s 0us/step
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 32, 32, 3) 0
__________________________________________________________________________________________________
densenet201 (Model) (None, 1, 1, 1920) 18321984 input_1[0][0]
__________________________________________________________________________________________________
global_max_pooling2d_1 (GlobalM (None, 1920) 0 densenet201[1][0]
__________________________________________________________________________________________________
global_average_pooling2d_1 (Glo (None, 1920) 0 densenet201[1][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1920) 0 densenet201[1][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 5760) 0 global_max_pooling2d_1[0][0]
global_average_pooling2d_1[0][0]
flatten_1[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 5760) 0 concatenate_1[0][0]
__________________________________________________________________________________________________
3_ (Dense) (None, 256) 1474816 dropout_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 256) 1024 3_[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 256) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
3_2 (Dense) (None, 1) 257 activation_1[0][0]
==================================================================================================
Total params: 19,798,081
Trainable params: 19,568,513
Non-trainable params: 229,568
__________________________________________________________________________________________________
```
In [11]:
```py
base_model.Trainable=True
set_trainable=False
for layer in base_model.layers:
layer.trainable = True
```
In [12]:
```py
model.compile('rmsprop', loss = "binary_crossentropy", metrics=["accuracy"])
```
In [13]:
```py
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint
batch_size = 128
epochs = 36
filepath="weights_resnet.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
learning_rate_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.6, patience=4, verbose=1, mode='max', min_delta=0.0, cooldown=0, min_lr=0)
early_stop = EarlyStopping(monitor='val_acc', min_delta=0.0001, patience=25, verbose=1, mode='max', baseline=None, restore_best_weights=True)
train_datagen = ImageDataGenerator(
rotation_range=40,
zoom_range=0.1,
vertical_flip=True,
horizontal_flip=True)
train_datagen.fit(x_train_arr)
history = model.fit_generator(
train_datagen.flow(x_train_arr, y_train, batch_size=batch_size),
steps_per_epoch=x_train.shape[0] // batch_size,
epochs=epochs,
validation_data=(x_test_arr, y_test),
callbacks=[learning_rate_reduce, checkpoint]
)
```
```
Epoch 1/36
123/123 [==============================] - 75s 611ms/step - loss: 0.0981 - acc: 0.9632 - val_loss: 0.5698 - val_acc: 0.8554
Epoch 00001: val_acc improved from -inf to 0.85543, saving model to weights_resnet.hdf5
Epoch 2/36
123/123 [==============================] - 26s 211ms/step - loss: 0.0569 - acc: 0.9842 - val_loss: 2.3376 - val_acc: 0.7554
Epoch 00002: val_acc did not improve from 0.85543
Epoch 3/36
123/123 [==============================] - 25s 204ms/step - loss: 0.0379 - acc: 0.9883 - val_loss: 1.3842 - val_acc: 0.7783
Epoch 00003: val_acc did not improve from 0.85543
Epoch 4/36
123/123 [==============================] - 25s 203ms/step - loss: 0.0445 - acc: 0.9864 - val_loss: 0.1151 - val_acc: 0.9686
Epoch 00004: val_acc improved from 0.85543 to 0.96857, saving model to weights_resnet.hdf5
Epoch 5/36
123/123 [==============================] - 26s 215ms/step - loss: 0.0437 - acc: 0.9873 - val_loss: 0.2282 - val_acc: 0.9726
Epoch 00005: val_acc improved from 0.96857 to 0.97257, saving model to weights_resnet.hdf5
Epoch 6/36
123/123 [==============================] - 26s 208ms/step - loss: 0.0288 - acc: 0.9900 - val_loss: 0.3356 - val_acc: 0.9149
Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.0006000000284984708.
Epoch 00006: val_acc did not improve from 0.97257
Epoch 7/36
123/123 [==============================] - 26s 209ms/step - loss: 0.0160 - acc: 0.9945 - val_loss: 0.0146 - val_acc: 0.9954
Epoch 00007: val_acc improved from 0.97257 to 0.99543, saving model to weights_resnet.hdf5
Epoch 8/36
123/123 [==============================] - 25s 207ms/step - loss: 0.0143 - acc: 0.9954 - val_loss: 0.0158 - val_acc: 0.9937
Epoch 00008: val_acc did not improve from 0.99543
Epoch 9/36
123/123 [==============================] - 25s 200ms/step - loss: 0.0254 - acc: 0.9949 - val_loss: 0.0097 - val_acc: 0.9977
Epoch 00009: val_acc improved from 0.99543 to 0.99771, saving model to weights_resnet.hdf5
Epoch 10/36
123/123 [==============================] - 25s 207ms/step - loss: 0.0120 - acc: 0.9959 - val_loss: 0.1715 - val_acc: 0.9514
Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0003600000170990825.
Epoch 00010: val_acc did not improve from 0.99771
Epoch 11/36
123/123 [==============================] - 26s 215ms/step - loss: 0.0082 - acc: 0.9975 - val_loss: 0.0119 - val_acc: 0.9966
Epoch 00011: val_acc did not improve from 0.99771
Epoch 12/36
123/123 [==============================] - 25s 206ms/step - loss: 0.0092 - acc: 0.9968 - val_loss: 0.4189 - val_acc: 0.9029
Epoch 00012: val_acc did not improve from 0.99771
Epoch 13/36
123/123 [==============================] - 25s 205ms/step - loss: 0.0072 - acc: 0.9978 - val_loss: 0.0366 - val_acc: 0.9874
Epoch 00013: val_acc did not improve from 0.99771
Epoch 14/36
123/123 [==============================] - 27s 219ms/step - loss: 0.0077 - acc: 0.9973 - val_loss: 0.0312 - val_acc: 0.9920
Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.00021600000327453016.
Epoch 00014: val_acc did not improve from 0.99771
Epoch 15/36
123/123 [==============================] - 26s 212ms/step - loss: 0.0048 - acc: 0.9986 - val_loss: 0.0163 - val_acc: 0.9960
Epoch 00015: val_acc did not improve from 0.99771
Epoch 16/36
123/123 [==============================] - 25s 203ms/step - loss: 0.0047 - acc: 0.9986 - val_loss: 0.0555 - val_acc: 0.9806
Epoch 00016: val_acc did not improve from 0.99771
Epoch 17/36
123/123 [==============================] - 26s 211ms/step - loss: 0.0060 - acc: 0.9982 - val_loss: 0.0492 - val_acc: 0.9943
Epoch 00017: val_acc did not improve from 0.99771
Epoch 18/36
123/123 [==============================] - 24s 198ms/step - loss: 0.0045 - acc: 0.9986 - val_loss: 0.0067 - val_acc: 0.9977
Epoch 00018: ReduceLROnPlateau reducing learning rate to 0.00012960000021848827.
Epoch 00018: val_acc did not improve from 0.99771
Epoch 19/36
123/123 [==============================] - 25s 206ms/step - loss: 0.0060 - acc: 0.9973 - val_loss: 0.0070 - val_acc: 0.9971
Epoch 00019: val_acc did not improve from 0.99771
Epoch 20/36
123/123 [==============================] - 27s 217ms/step - loss: 0.0048 - acc: 0.9989 - val_loss: 0.0065 - val_acc: 0.9983
Epoch 00020: val_acc improved from 0.99771 to 0.99829, saving model to weights_resnet.hdf5
Epoch 21/36
123/123 [==============================] - 25s 203ms/step - loss: 0.0235 - acc: 0.9974 - val_loss: 0.0203 - val_acc: 0.9949
Epoch 00021: val_acc did not improve from 0.99829
Epoch 22/36
123/123 [==============================] - 24s 199ms/step - loss: 0.0040 - acc: 0.9990 - val_loss: 0.0149 - val_acc: 0.9966
Epoch 00022: ReduceLROnPlateau reducing learning rate to 7.775999838486313e-05.
Epoch 00022: val_acc did not improve from 0.99829
Epoch 23/36
123/123 [==============================] - 26s 211ms/step - loss: 0.0039 - acc: 0.9988 - val_loss: 0.0084 - val_acc: 0.9977
Epoch 00023: val_acc did not improve from 0.99829
Epoch 24/36
123/123 [==============================] - 25s 200ms/step - loss: 0.0382 - acc: 0.9951 - val_loss: 0.0088 - val_acc: 0.9983
Epoch 00024: val_acc did not improve from 0.99829
Epoch 25/36
123/123 [==============================] - 25s 201ms/step - loss: 0.0035 - acc: 0.9989 - val_loss: 0.0054 - val_acc: 0.9983
Epoch 00025: val_acc did not improve from 0.99829
Epoch 26/36
123/123 [==============================] - 26s 209ms/step - loss: 0.0246 - acc: 0.9970 - val_loss: 0.0059 - val_acc: 0.9977
Epoch 00026: ReduceLROnPlateau reducing learning rate to 4.6655999904032795e-05.
Epoch 00026: val_acc did not improve from 0.99829
Epoch 27/36
123/123 [==============================] - 26s 210ms/step - loss: 0.0032 - acc: 0.9989 - val_loss: 0.0100 - val_acc: 0.9983
Epoch 00027: val_acc did not improve from 0.99829
Epoch 28/36
123/123 [==============================] - 25s 205ms/step - loss: 0.0025 - acc: 0.9993 - val_loss: 0.0063 - val_acc: 0.9971
Epoch 00028: val_acc did not improve from 0.99829
Epoch 29/36
123/123 [==============================] - 26s 211ms/step - loss: 0.0222 - acc: 0.9981 - val_loss: 0.0077 - val_acc: 0.9977
Epoch 00029: val_acc did not improve from 0.99829
Epoch 30/36
123/123 [==============================] - 26s 212ms/step - loss: 0.0300 - acc: 0.9969 - val_loss: 0.0085 - val_acc: 0.9977
Epoch 00030: ReduceLROnPlateau reducing learning rate to 2.799360081553459e-05.
Epoch 00030: val_acc did not improve from 0.99829
Epoch 31/36
123/123 [==============================] - 25s 203ms/step - loss: 0.0233 - acc: 0.9966 - val_loss: 0.0155 - val_acc: 0.9960
Epoch 00031: val_acc did not improve from 0.99829
Epoch 32/36
123/123 [==============================] - 26s 212ms/step - loss: 0.0036 - acc: 0.9981 - val_loss: 0.0150 - val_acc: 0.9966
Epoch 00032: val_acc did not improve from 0.99829
Epoch 33/36
123/123 [==============================] - 26s 209ms/step - loss: 0.0239 - acc: 0.9969 - val_loss: 0.0077 - val_acc: 0.9983
Epoch 00033: val_acc did not improve from 0.99829
Epoch 34/36
123/123 [==============================] - 25s 201ms/step - loss: 0.0158 - acc: 0.9967 - val_loss: 0.0112 - val_acc: 0.9966
Epoch 00034: ReduceLROnPlateau reducing learning rate to 1.6796160707599483e-05.
Epoch 00034: val_acc did not improve from 0.99829
Epoch 35/36
123/123 [==============================] - 25s 201ms/step - loss: 9.8774e-04 - acc: 0.9997 - val_loss: 0.0110 - val_acc: 0.9977
Epoch 00035: val_acc did not improve from 0.99829
Epoch 36/36
123/123 [==============================] - 26s 211ms/step - loss: 0.0012 - acc: 0.9996 - val_loss: 0.0098 - val_acc: 0.9971
Epoch 00036: val_acc did not improve from 0.99829
```
In [14]:
```py
train_pred = model.predict(x_train_arr, verbose= 1)
valid_pred = model.predict(x_test_arr, verbose= 1)
train_acc = roc_auc_score(np.round(train_pred), y_train)
valid_acc = roc_auc_score(np.round(valid_pred), y_test)
```
```
15750/15750 [==============================] - 24s 2ms/step
1750/1750 [==============================] - 2s 1ms/step
```
In [15]:
```py
confusion_matrix(np.round(valid_pred), y_test)
```
Out[15]:
```
array([[ 442, 0],
[ 5, 1303]])
```
In [16]:
```py
sample = pd.read_csv('../input/sample_submission.csv')
```
In [17]:
```py
test = []
for images in tqdm.tqdm(sample['id']):
img = plt.imread('../input/test/test/' + images)
test.append(img)
test = np.array(test)
```
```
100%|██████████| 4000/4000 [00:04<00:00, 909.30it/s]
```
In [18]:
```py
test = test/255
test_pred = model.predict(test, verbose= 1)
```
```
4000/4000 [==============================] - 5s 1ms/step
```
In [19]:
```py
sample['has_cactus'] = test_pred
sample.head()
```
Out[19]:
| | id | has_cactus |
| --- | --- | --- |
| 0 | 000940378805c44108d287872b2f04ce.jpg | 9.999971e-01 |
| --- | --- | --- |
| 1 | 0017242f54ececa4512b4d7937d1e21e.jpg | 9.999970e-01 |
| --- | --- | --- |
| 2 | 001ee6d8564003107853118ab87df407.jpg | 9.930815e-08 |
| --- | --- | --- |
| 3 | 002e175c3c1e060769475f52182583d0.jpg | 1.431321e-05 |
| --- | --- | --- |
| 4 | 0036e44a7e8f7218e9bc7bf8137e4943.jpg | 9.999958e-01 |
| --- | --- | --- |
In [20]:
```py
sample.to_csv('sub.csv', index= False)
```
================================================
FILE: docs/Kaggle/competitions/playground/dogs-vs-cats/README.md
================================================
# **猫和狗**
> 注意：[项目规范](/docs/kaggle-quickstart.md)
## 比赛说明
* 在本次比赛中，您将编写一个算法来分类图像是否包含狗或猫。这对人类，狗和猫来说很容易。你的电脑会觉得有点困难。
深蓝在1997年在国际象棋比赛中击败卡斯帕罗夫。
沃森在2011 年击败了Jeopardy最聪明的琐事。
你能否在2013年从米登斯那里告诉菲多？
> Asirra数据集
Web服务通常受到人们解决这个难题的挑战，但这对计算机来说很困难。这样的挑战通常被称为 [CAPTCHA](http://www.captcha.net/) （完全自动公开的图灵测试来告诉计算机和人类）或HIP（人类交互证明）。HIP用于多种用途，例如减少电子邮件和博客垃圾邮件，防止对网站密码进行暴力攻击。
[Asirra](http://research.microsoft.com/en-us/um/redmond/projects/asirra/)（限制访问的动物物种图像识别）是一项HIP，通过询问用户识别猫和狗的照片而工作。这项任务对于计算机来说很难，但研究表明人们可以快速准确地完成任务。许多人甚至认为这很有趣！以下是Asirra界面的一个例子：
Asirra是独一无二的，因为它与全球最大的网站 [Petfinder.com](http://www.petfinder.com/) 合作， 致力于为无家可归的宠物寻找住所。他们向微软研究院提供了超过三百万张猫和狗的图像，由美国各地数千个动物收容所的人员手动分类。Kaggle很幸运能够提供这些数据的一个子集，用于娱乐和研究。
> 图像识别攻击
虽然随机猜测是最简单的攻击形式，但各种形式的图像识别可以让攻击者做出比随机更好的猜测。照片数据库（各种各样的背景，角度，姿势，照明等）具有巨大的多样性，难以进行准确的自动分类。在多年前进行的一项非正式调查中，计算机视觉专家认为，如果没有现有技术的重大进展，精度高于60％的分类器将很困难。作为参考，60％分类器将12幅图像HIP的猜测概率从1/4096提高到1/459。
> 最先进的
目前的文献表明机器分类器可以在这项任务上得到80％以上的准确度[1]。因此，Asirra不再被认为是安全的。我们创建了这个比赛，以针对这个问题对最新的计算机视觉和深度学习方法进行基准测试 你能破解CAPTCHA吗？你能改善艺术状态吗？你能在猫狗之间创造持久的和平吗？
好的，我们会解决前者。
> 致谢
我们感谢微软研究院为此次比赛提供数据。
* Jeremy Elson，John R. Douceur，Jon Howell，Jared Saul，Asirra：在计算机和通信安全（CCS）第14届ACM会议论文集计算机械协会会刊上发表的利用调整手动图像分类的CAPTCHA， 2007年10月
## 参赛成员
* 开源组织: [ApacheCN ~ apachecn.org](http://www.apachecn.org/)
* 参与人员: [片刻](https://github.com/jiangzhonglian)
## 比赛分析
* 回归问题：价格的问题
* 常用算法： `回归`、`树回归`、`GBDT`、`xgboost`、`lightGBM`
```
步骤:
一. 数据分析
1. 下载并加载数据
2. 总体预览:了解每列数据的含义,数据的格式等
3. 数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备
二. 特征工程
1.根据业务,常识,以及第二步的数据分析构造特征工程.
2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)
三. 模型选择
1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.
2.比较各个模型的分数,然后取效果较好的模型作为基础模型.
四. 模型融合
五. 修改特征和模型参数
1.可以通过添加或者修改特征,提高模型的上限.
2.通过修改模型的参数,是模型逼近上限
六. 提交格式
'''(label: 1=dog, 0=cat)
id,label
1,0
2,0
3,0
etc...
'''
```
## 一. 数据分析
### 数据下载和加载
> 数据获取
* 数据集下载地址：
> 特征说明
* Dogs vs. Cats是一个传统的二分类问题。
* 训练集包含25000张图片，命名格式为..jpg, 如cat.10000.jpg、dog.100.jpg
* 测试集包含12500张图片，命名为.jpg，如1000.jpg。
* 参赛者需根据训练集的图片训练模型，并在测试集上进行预测，输出它是狗的概率。
* 最后提交的csv文件如下，第一列是图片的，第二列是图片为狗的概率。
================================================
FILE: docs/Kaggle/competitions/playground/dogs-vs-cats/kernel.md
================================================
# Dogs vs. Cats (kaggle 猫狗大战)
Create an algorithm to distinguish dogs from cats.
正如上面这句话所说，我们的目的就是创建一个算法，来对混合猫狗图片的数据集中，将猫和狗分别识别出来。
## 一、简介
猫狗大战这个项目其实与我们之前做的数字识别类似，只不过是图片复杂了一些。当然，我们这次使用深度学习，来完成我们想要做的事情。
## 二、安装包/第三方库要求
- Python 3.x
- Pytorch
## 三、数据预处理
### 1、本次的数据来自 kaggle 的比赛项目 [dogs-vs-cats](https://www.kaggle.com/c/dogs-vs-cats)
### 2、查看数据格式
- 训练数据
- 训练数据集 - 说明：训练数据集中的数据，是经过人工标记的数据，类别和数字之间使用的 "." （点）做的分隔。
- 测试数据
- 测试数据集 - 说明：测试数据集中的数据，是没有经过人工标记的数据，没有对应的类别，只有一些相应的数字号码。
### 3、对数据的预处理
#### 3.1、提取训练 & 测试数据集的编号
训练数据集 & 测试数据集 给出的序号和 label 都是在文件名称中。
```python
imgs = [os.path.join(root, img) for img in os.listdir(root)]
# test1：即测试数据集， D:/dataset/dogs-vs-cats/test1
# train: 即训练数据集，D:/dataset/dogs-vs-cats/train
if self.test:
# 提取 测试数据集的序号，
# 如 x = 'd:/path/123.jpg'，
# x.split('.') 得到 ['d:/path/123', 'jpg']
# x.split('.')[-2] 得到 d:/path/123
# x.split('.')[-2].split('/') 得到 ['d:', 'path', '123']
# x.split('.')[-2].split('/')[-1] 得到 123
imgs = sorted(imgs, key=lambda x: int(x.split('.')[-2].split('/')[-1]))
else:
# 如果不是测试集的话，就是训练集，我们只切分一下，仍然得到序号，123
imgs = sorted(imgs, key=lambda x: int(x.split('.')[-2]))
```
#### 3.2、划分训练集 & 验证集
首先我们知道我们手里的数据现在只有训练集和测试集，并没有验证集。那么为了我们训练得到的模型更好地拟合我们的测试数据，我们人为地将训练数据划分为 训练数据 + 验证数据（比例设置为 7:3）
```python
# 获取图片的数量
imgs_num = len(imgs)
# 划分训练、验证集，验证集:训练集 = 3:7
# 判断是否为测试集
if self.test:
# 如果是 测试集，那么 就直接赋值
self.imgs = imgs
# 判断是否为 训练集
elif train:
# 如果是训练集，那么就把数据集的开始位置的数据 到 70% 部分的数据作为训练集
self.imgs = imgs[:int(0.7 * imgs_num)]
else:
# 这种情况就是划分验证集啦,从 70% 部分的数据 到达数据的末尾，全部作为验证集
self.imgs = imgs[int(0.7 * imgs_num):]
```
#### 3.3、测试集，验证集和训练集的数据转换
```python
# 数据的转换操作，测试集，验证集，和训练集的数据转换有所区别
if transforms is None:
# 如果转换操作没有设置，那我们设置一个转换
normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
# 测试集 和 验证集 的转换
# 判断如果是测试集或者不是训练集（也就是说是验证集），就应用我们下边的转换
if self.test or not train:
self.trainsforms = T.Compose([T.Resize(224), T.CenterCrop(224), T.ToTensor(), normalize])
else:
# 如果是训练集的话，使用另外的转换
self.transforms = T.Compose([T.Resize(256), T.RandomResizedCrop(224), T.RandomHorizontalFlip(), T.ToTensor(), normalize])
```
#### 3.4、重写子类 / 函数
这里我们使用了 torch.utils.data 中的一些函数，比如 Dataset
class torch.utils.data.Dataset 表示 Dataset 的抽象类，所有其他的数据集都应继承该类。所有子类都应该重写 __len__ ，提供数据集大小的方法，和 __getitem__ ，支持从 0 到 len(self) 整数索引的方法。
```python
def __len__(self):
return len(self.imgs)
def __getitem__(self, index):
img_path = self.imgs[index]
# 判断，如果是测试集的数据的话，那就返回对应的序号，比如 d:path/123.jpg 返回 123
if self.test:
label = int(self.imgs[index].split('.')[-2].split('/')[-1])
else:
# 如果不是测试集的数据，那么会有相应的类别（label），也就是对应的dog 和 cat，dog 为 1，cat 为0
label = 1 if 'dog' in img_path.split('/')[-1] else 0
# 这里使用 Pillow 模块，使用 Image 打开一个图片
data = Image.open(img_path)
# 使用我们定义的 transforms ，将图片转换，详情参考：https://pytorch.org/docs/stable/torchvision/transforms.html#transforms-on-pil-image
# 默认的 transforms 设置的是 none
data = self.transforms(data)
# 将转换完成的 data 以及对应的 label（如果有的话），返回
return data,label
```
#### 3.5、数据加载
```python
# 训练数据集的路径
train_path = 'D:/dataset/dogs-vs-cats/train'
# 从训练数据集的存储路径中提取训练数据集
train_dataset = GetData(train_path, train=True)
# 将训练数据转换成 mini-batch 形式
load_train = data.DataLoader(train_dataset, batch_size=20, shuffle=True, num_workers=1)
# 测试数据的获取
# 首先设置测试数据的路径
test_path = 'D:/dataset/dogs-vs-cats/test1'
# 从测试数据集的存储路径中提取测试数据集
test_path = GetData(test_path, test=True)
# 将测试数据转换成 mini-batch 形式
loader_test = data.DataLoader(test_dataset, batch_size=3, shuffle=True, num_workers=1)
```
## 四、构建 CNN 模型
```python
# 调用已经写好的 AlexNet() 模型
cnn = AlexNet()
# 将模型打印出来观察一下
print(cnn)
```
## 五、设置相应的优化器和损失函数
我们已经构造完成了 CNN 模型，并将我们所需要的数据进行了相应的预处理。那我们接下来的一步就是定义相应的损失函数和优化函数。
torch.optim 是一个实现各种优化算法的软件包。
```python
# 设置优化器和损失函数
# 这里我们使用 Adam 优化器，使用的损失函数是 交叉熵损失
optimizer = torch.optim.Adam(cnn.parameters(), lr=0.005, betas=(0.9, 0.99)) # 优化所有的 cnn 参数
loss_func = nn.CrossEntropyLoss() # 目标 label 不是 one-hotted 类型的
```
## 六、训练模型
数据以及相对应的损失函数和优化器，我们都已经设置好了，那接下来就是紧张刺激的训练模型环节了。
```python
# 训练模型
# 设置训练模型的次数，这里我们设置的是 10 次，也就是用我们的训练数据集对我们的模型训练 10 次，为了节省时间，我们可以只训练 1 次
EPOCH = 10
# 训练和测试
for epoch in range(EPOCH):
num = 0
# 给出 batch 数据，在迭代 train_loader 的时候对 x 进行 normalize
for step, (x, y) in enumerate(loader_train):
b_x = Variable(x) # batch x
b_y = Variable(y) # batch y
output = cnn(b_x) # cnn 的输出
loss = loss_func(output, b_y) # 交叉熵损失
optimizer.zero_grad() # 在这一步的训练步骤上，进行梯度清零
loss.backward() # 反向传播，并进行计算梯度
optimizer.step() # 应用梯度
# 可以打印一下
# print('-'*30, step)
if step % 20 == 0:
num += 1
for _, (x_t, y_test) in enumerate(loader_test):
x_test = Variable(x_t) # batch x
test_output = cnn(x_test)
pred_y = torch.max(test_output, 1)[1].data.squeeze()
accuracy = sum(pred_y == y_test) / float(y_test.size(0))
print('Epoch: ', epoch, '| Num: ', num, '| Step: ', step, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.4f' % accuracy)
```
================================================
FILE: docs/Kaggle/competitions/playground/leaf-classification/leaf-classification-competition-1st-place-winners-interview-ivan-sosnovik.md
================================================
# 树叶分类竞赛：Ivan Sosnovik 的冠军采访
> 原文：[Leaf Classification Competition: 1st Place Winner's Interview, Ivan Sosnovik](http://blog.kaggle.com/2017/03/24/leaf-classification-competition-1st-place-winners-interview-ivan-sosnovik/)
>
> 译者：[飞龙](https://github.com/wizardforcel)
>
> 自豪地采用[谷歌翻译](https://translate.google.cn)
你能看到随机森林的树叶吗？[树叶分类入门竞赛](https://www.kaggle.com/c/leaf-classification)于 2016 年 8 月至 2017 年 2 月在 Kaggle 上进行。Kagglers 面临着根据图像和预先提取的特征正确识别 99 种树叶的挑战。在这位获胜者的采访中，Kaggler [Ivan Sosnovik](https://www.kaggle.com/isosnovik) 分享了他的冠军方法。他解释了在这个特征工程竞赛中，他使用逻辑回归和随机森林算法比 XGBoost 或卷积神经网络更好运。
## 简介
我是莫斯科 Skoltech 的数据分析硕士生。 大约一年前，当我参加大学的第一门 ML 课程时，我加入了 Kaggle。 第一场比赛是 [What's Cooking](https://www.kaggle.com/c/whats-cooking)。 从那以后，我参加了几场 Kaggle 比赛，但没有那么多关注它。 理解 ML 方法的工作方式更像是一种练习。
树叶分类的想法非常简单和具有挑战性。 看起来像我不需要堆叠这么多模型，解决方案可能是优雅的。 此外，数据总量仅为 100 多 MB，即使使用笔记本电脑也可以进行学习。 这是非常有希望的，因为大多数计算应该在我的 MacBook Air 上使用 1.3 GHz Intel Core i5 和 4GB RAM 完成。
之前我曾经处理过黑白图像。 我家附近有一片森林。 但是，在这次比赛中，它没有给我这么多的好处。
## 让我们看看技术
当我参加比赛时，发布了几个排名前20％的内核。 解决方案使用最初提取的特征和 Logistic 回归。 它的 logloss 约为 0.03818。通过调整参数，无法实现显着的改进。为了提高质量，必须进行特征工程。似乎没有人这样做，因为顶级解决方案的得分略高于我的。
## 特征工程
我先做了第一件事，并绘制了每个类的图像。
原始图像具有不同的分辨率，旋转，纵横比，宽度和高度。 但是，类中每个参数的变化小于类之间的变化。 因此，可以即时构建一些信息特征。 他们是：
+ 宽和高
+ 纵横比：`width / height`
+ 面积：`width * height`
+ 是否横向：`int(width > height)`
另一个看似有用的非常有用的特征是图像像素的平均值。
我将这些特征添加到已经提取的特征中。 Logistic 回归改善了结果。 但是，大部分工作尚未完成。
所有上述特征都不代表图像的内容。
## PCA
尽管神经网络作为特征提取器取得了成功，但我仍然喜欢 PCA。它很简单，允许人们在`IR^N`中获得图像的有用表示。 首先，将图像重新调整为`50x50`。然后应用 PCA。 将成分添加到先前提取的特征集中。
成分数量各不相同。 最后，我使用了`N = 35`个主成分。 这种方法表明 logloss 约为 0.01511。
## Moments 和 hull
为了生成更多特征，我使用了 OpenCV。这里是如何获取图像的 Moments 和 hull 的精彩[教程](http://docs.opencv.org/3.1.0/dd/d49/tutorial_py_contour_features.html)。 我还添加了几个特征的一些成对乘法。
最后的特征如下：
+ 初始特征
+ 宽高，比例，以及其他
+ PCA
+ Moments
Logistic 回归表明 loglos 约为 0.00686。
## 核心思想
所有上述都证明了良好的结果。 这样的结果适合于现实生活中的应用。 但是，它可以得到加强。
### 不确定性
大多数对象都有特定的决策：只有一个类别的 p 约为 1.0的类，其余的 p 小于 0.01。 但是，我在预测中发现了几个具有不确定性的对象，如下所示：
混淆类别的集合很小（15 个类别分成几个小组），所以我决定查看树叶的图片，并检查我是否可以对它们进行分类。 结果如下：
我必须承认，对于不同的亚种，Quercus'（橡树）的树叶看起来几乎相同。 我想，我可以区分桉树（Eucalyptus ）和山茱萸（Cornus），但亚种的分类对我来说似乎很复杂。
## 你能看到随机森林的树叶吗
我的解决方案的关键思想是创建另一个分类器，它将仅针对混淆类进行预测。 我试过的第一个是来自`sklearn`的`RandomForestClassifier`，它在调整超参数后得到了很好的结果。 随机森林使用逻辑回归的相同数据进行训练，但仅使用来自混淆类的对象。
如果逻辑回归给出了对象的不确定预测，则使用随机森林分类器的预测。 随机森林给出了 15 个类的概率，其余假设为绝对0。
最后的流水线如下：
### 阈值
排行榜得分是在整个数据集上计算的。 这就是为什么一些有风险的方法可以用于这场比赛。
提交由多分类 logloss 评估。
其中 N，M 是对象和类的数量，`p_{ij}`是预测，`y_ {ij}`是指标：如果对象`i`在类`j`中，则`y_ {ij} = 1`，否则它等于 0。 如果模型正确地选择了类，以下方法将减少整体 logloss，否则它将显着增加。
在阈值处理后，我得到了 logloss = 0.0 的分数。就是这样。所有标签都是正确的。
## 接下来
我已经尝试了几种方法，它们显示适当结果但未在最终流水线中使用。 此外，我有了一些想法，对于如何使解决方案更优雅。 在本节中，我将尝试讨论它们。
### XGBoost
dmlc 的 [XGBoost](https://github.com/dmlc/xgboost) 是一个很棒的工具。 我之前在几个比赛中使用过它，并决定在最初提取的特征上训练它。 它表现出与逻辑回归相同的分数甚至更差，但时间消耗更大。
### 提交的 blending
在我想出随机森林用作第二个分类器的想法之前，我尝试了不同的单模型方法。 因此我收集了很多提交。 一个微小的想法是将提交混合：使用预测的平均值或加权平均值。 结果也不是很好。
### 神经网络
神经网络是我试图实现的第一个想法之一。 卷积神经网络是很好的特征提取器，因此，它们可以用作第一级模型，甚至可以用作主分类器。 原始图像具有不同的分辨率。 我将它们重新调整为`50x50`。在我的笔记本电脑上进行 CNN 训练太费时间，而无法在合理的时间内选择合适的架构，所以经过几个小时的训练后我拒绝了这个想法。 我相信，CNN 可以为这个数据集提供准确的预测。
================================================
FILE: docs/Kaggle/kernel.md
================================================
# Kaggle Kernel 备份
+ [第一部分](https://github.com/apachecn/kaggle-kernel-pt1)
+ [第二部分](https://github.com/apachecn/kaggle-kernel-pt2)
+ [第三部分](https://github.com/apachecn/kaggle-kernel-pt3)
+ [第四部分](https://github.com/apachecn/kaggle-kernel-pt4)
================================================
FILE: docs/Kaggle/learn/embeddings/README.md
================================================
# Kaggle 官方教程：嵌入
> 原文：[Embeddings](https://www.kaggle.com/learn/embeddings)
>
> 译者：[飞龙](https://github.com/wizardforcel)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
P.S...
本课程仍处于测试阶段，因此我很乐意收到你的反馈意见。 如果你有时间填写[本课程的超短期调查](https://form.jotform.com/82826168584267)，我将非常感激。 你也可以在下面的评论中或在[学习论坛](https://www.kaggle.com/learn-forum)上留下公开反馈。
## 一、嵌入层
欢迎阅读嵌入主题的第一课。 在本课程中，我将展示如何使用`tf.keras` API 实现带嵌入层的模型。 嵌入是一种技术，使深度神经网络能够处理稀疏的类别变量。
### 稀疏类别变量
我的意思是一个具有许多可能值（高基数）的类别变量，其中少数（通常只有 1）存在于任何给定的观察中。 一个很好的例子是词汇。 英语中的词汇是成千上万的，但一条推文可能只有十几个词。 词嵌入是将深度学习应用于自然语言的关键技术。 但其他例子还有很多。
例如，[洛杉矶餐馆检查](https://www.kaggle.com/meganrisdal/la-county-restaurant-inspections-and-violations)的这个数据集有几个稀疏的分类变量，包括：
+ `employee_id`：卫生部门的哪些员工进行了这次检查？ （约 250 个不同的值）
+ `facility_zip`：餐厅的邮政编码是什么？ （约 3,000 个不同的值）
+ `owner_name`：谁拥有餐厅？ （约 35,000 个不同的值）
对于将任何这些变量用作网络的输入，嵌入层是个好主意。
在本课程中，我将使用 [MovieLens 数据集](https://www.kaggle.com/grouplens/movielens-20m-dataset)作为示例。
### MovieLens
MovieLens 数据集由用户给电影的评分组成。这是一个示例：
| | userId | movieId | rating | y | title | year |
| --- | --- | --- | --- | --- | --- | --- |
| 12904240 | 85731 | 1883 | 4.5 | 0.974498 | Labyrinth | 1986 |
| 6089380 | 45008 | 1221 | 4.5 | 0.974498 | Femme Nikita, La (Nikita) | 1990 |
| 17901393 | 125144 | 3948 | 4.0 | 0.474498 | The Alamo | 1960 |
| 9024816 | 122230 | 3027 | 3.5 | -0.025502 | Toy Story 2 | 1999 |
| 11655659 | 21156 | 5202 | 3.0 | -0.525502 | My Big Fat Greek Wedding |
评分范围是 0.5 到 5。我们的目标是预测由给定用户`ui`给出的，特定电影`mj`的评分。 （列`y`只是评分列的副本，减去了平均值 - 这在以后会有用。）
`userId`和`movieId`都是稀疏分类变量。 它们有许多可能的值：
```
138,493 个独立用户评分了 26,744 个不同的电影（总评分是 20,000,263 个）
```
### 在 Keras 中创建评分预测模型
我们想要构建一个模型，它接受用户`ui`和电影`mj`，并输出 0.5-5 的数字，表示我们认为该用户将为该电影评分多少星。
> 注：你可能已经注意到`MovieLens`数据集包含每部电影的信息，例如标题，发行年份，一组流派和用户指定的标签。 但就目前而言，我们不会试图利用任何额外的信息。
我说我们需要一个嵌入层来处理这些输入。 为什么？ 让我们回顾一些替代方案，看看为什么它们不起作用。
### 坏主意 #1：使用用户和电影 ID 作为数值输入
为什么不将用户 ID 和电影 ID 作为数值来输入，然后添加一些密集层？即：
```py
model = keras.Sequential([
# 2 个输入值：用户 ID 和电影 ID
keras.layers.Dense(256, input_dim=2, activation='relu'),
keras.layers.Dense(32, activation='relu'),
# 单个输出节点，包含预测的评分
keras.layers.Dense(1)
])
```
用最简单的术语来说，神经网络的原理是对输入进行数学运算。 但分配给用户和电影的 ID 的实际数值是没有意义的。《辛德勒的名单》的 id 为 527，而《非常嫌疑犯》的 id 为 50，但这并不意味着《辛德勒的名单》比《非常嫌疑犯》大十倍。
### 坏主意 #2：独热编码的用户和电影输入
如果你不熟悉单热编码，可能需要查看我们的课程[“使用独热编码处理类别数据”](https://www.kaggle.com/dansbecker/using-categorical-data-with-one-hot-encoding)。
在该课程中，我们说独热编码是“类别数据的标准方法”。 那么为什么这是一个坏主意呢？ 让我们看看模型是什么样子，它接受独热编码的用户和电影。
```py
input_size = n_movies + n_users
print("Input size = {:,} ({:,} movies + {:,} users)".format(
input_size, n_movies, n_users,
))
model = keras.Sequential([
# 一个带有 128 个单元的隐藏层
keras.layers.Dense(128, input_dim=input_size, activation='relu'),
# 单个输出节点，包含预测的评分
keras.layers.Dense(1)
])
model.summary()
'''
Input size = 165,237 (26,744 movies + 138,493 users)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_3 (Dense) (None, 128) 21150464
_________________________________________________________________
dense_4 (Dense) (None, 1) 129
=================================================================
Total params: 21,150,593
Trainable params: 21,150,593
Non-trainable params: 0
_________________________________________________________________
'''
```
这里的一个基本问题是扩展和效率。 我们模型的单个输入是包含 165,237 个数字的向量（其中我们知道 165,235 是零）。 我们整个 2000 万个评分实例数据集的特征数据，将需要一个大小为 20,000,000 x 165,237 或大约 3 万亿个数字的二维数组。 但愿你能把这一切都放进内存中！
此外，在我们的模型上进行训练和推断将是低效的。 为了计算我们的第一个隐藏层的激活，我们需要将我们的 165k 输入乘以大约 2100 万个权重 - 但是这些乘积的绝大多数都将为零。
对于具有少量可能值的分类变量，例如`{Red, Yellow, Green}`或`{Monday, Tuesday, Wednesday, Friday, Saturday, Sunday}`，独热编码是合适的。 但在像我们的电影推荐问题的情况下，它并不是那么好，其中变量有数十或数十万个可能的值。
### 好主意：嵌入层
简而言之，嵌入层将一组离散对象（如单词，用户或电影）中的每个元素映射到实数的密集（嵌入）向量。
> 注：一个关键的实现细节是嵌入层接受被嵌入实体的索引作为输入（即我们可以将`userId`和`movieId`作为输入）。 你可以将其视为一种“查找表”。 这比采用独热向量并进行巨大的矩阵乘法要有效得多！
例如，如果我们为电影学习大小为 8 的嵌入，则《律政俏佳人》（`index = 4352`）的嵌入可能如下所示：
```
[1.624,−0.612,−0.528,−1.073,0.865,−2.302,1.745,−0.761]
```
它们来自哪里？ 我们使用随机噪声为每个用户和电影初始化嵌入，然后我们将它们训练，作为整体评分预测模型训练过程的一部分。
他们的意思是什么？ 如果对象的嵌入有任何好处，它应该捕获该对象的一些有用的潜在属性。 但这里的关键词是潜在，也就是隐藏的。 由模型来发现实体的任何属性，并在嵌入空间中对它们编码，对预测任务有用。 听起来很神秘？ 在后面的课程中，我将展示一些解释习得的嵌入的技术，例如使用 t-SNE 算法将它们可视化。
### 实现它
我希望我的模型是这样：
需要注意的一个关键点是，这个网络不仅仅是从输入到输出的一堆层级。 我们将用户和电影视为单独的输入，只有在每个输入经过自己的嵌入层之后才会聚集在一起。
这意味着[`keras.Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential)类（你可能从我们的[图像数据深度学习课程](https://www.kaggle.com/learn/deep-learning)中熟悉它）将无法工作。 我们需要使用[`keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)类转向更强大的“函数式 API”。 函数式 API 的更多详细信息，请查看 [Keras 的指南](https://keras.io/getting-started/functional-api-guide/)。
这是代码：
```py
hidden_units = (32,4)
movie_embedding_size = 8
user_embedding_size = 8
# 每个实例将包含两个输入：单个用户 ID 和单个电影 ID
user_id_input = keras.Input(shape=(1,), name='user_id')
movie_id_input = keras.Input(shape=(1,), name='movie_id')
user_embedded = keras.layers.Embedding(df.userId.max()+1, user_embedding_size,
input_length=1, name='user_embedding')(user_id_input)
movie_embedded = keras.layers.Embedding(df.movieId.max()+1, movie_embedding_size,
input_length=1, name='movie_embedding')(movie_id_input)
# 连接嵌入（并删除无用的额外维度）
concatenated = keras.layers.Concatenate()([user_embedded, movie_embedded])
out = keras.layers.Flatten()(concatenated)
# 添加一个或多个隐层
for n_hidden in hidden_units:
out = keras.layers.Dense(n_hidden, activation='relu')(out)
# 单一输出：我们的预测评分
out = keras.layers.Dense(1, activation='linear', name='prediction')(out)
model = keras.Model(
inputs = [user_id_input, movie_id_input],
outputs = out,
)
model.summary(line_length=88)
'''
________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
========================================================================================
user_id (InputLayer) (None, 1) 0
________________________________________________________________________________________
movie_id (InputLayer) (None, 1) 0
________________________________________________________________________________________
user_embedding (Embedding) (None, 1, 8) 1107952 user_id[0][0]
________________________________________________________________________________________
movie_embedding (Embedding) (None, 1, 8) 213952 movie_id[0][0]
________________________________________________________________________________________
concatenate (Concatenate) (None, 1, 16) 0 user_embedding[0][0]
movie_embedding[0][0]
________________________________________________________________________________________
flatten (Flatten) (None, 16) 0 concatenate[0][0]
________________________________________________________________________________________
dense_5 (Dense) (None, 32) 544 flatten[0][0]
________________________________________________________________________________________
dense_6 (Dense) (None, 4) 132 dense_5[0][0]
________________________________________________________________________________________
prediction (Dense) (None, 1) 5 dense_6[0][0]
========================================================================================
Total params: 1,322,585
Trainable params: 1,322,585
Non-trainable params: 0
________________________________________________________________________________________
'''
```
### 训练
我们将编译我们的模型，来最小化平方误差（'MSE'）。 我们还将绝对值误差（'MAE'）作为在训练期间报告的度量标准，因为它更容易解释。
> 需要考虑的事情：我们知道评分只能取值`{0.5,1,1.5,2,2.5,3,3.5,4,4.5,5}` - 所以为什么不将其视为 10 类的多类分类问题 ，每个可能的星级评分一个？
```py
model.compile(
# 技术说明：使用嵌入层时，我强烈建议使用
# tf.train 中发现的优化器之一：
# https://www.tensorflow.org/api_guides/python/train#Optimizers
# 传入像 'adam' 或 'SGD' 这样的字符串，会加载一个 keras 优化器
# （在 tf.keras.optimizers 下寻找）。 对于像这样的问题，它们似乎要慢得多，
# 因为它们无法有效处理稀疏梯度更新。
tf.train.AdamOptimizer(0.005),
loss='MSE',
metrics=['MAE'],
)
```
让我们训练模型：
> 注：我传入`df.y`而不是`df.rating`，作为我的目标变量。`y`列只是评分的“中心”版本 - 即评分列减去其在训练集上的平均值。 例如，如果训练集中的总体平均评分是 3 星，那么我们将 3 星评分翻译为 0, 5星评分为 2.0 等等，来获得`y`。 这是深度学习中的常见做法，并且往往有助于在更少的时期内获得更好的结果。 对于更多详细信息，请随意使用我在 MovieLens 数据集上执行的所有预处理来检查[这个内核](https://www.kaggle.com/colinmorris/movielens-preprocessing)。
```py
history = model.fit(
[df.userId, df.movieId],
df.y,
batch_size=5000,
epochs=20,
verbose=0,
validation_split=.05,
);
```
为了判断我们的模型是否良好，有一个基线是有帮助的。 在下面的单元格中，我们计算了几个虚拟基线的误差：始终预测全局平均评分，以及预测每部电影的平均评分：
+ 训练集的平均评分：3.53 星
+ 总是预测全局平均评分，结果为 MAE=0.84，MSE=1.10
+ 预测每部电影的平均评分，结果为 MAE=0.73，MSE=0.88
这是我们的嵌入模型的绝对误差随时间的绘图。 为了进行比较，我们的最佳基线（预测每部电影的平均评分）用虚线标出：
与基线相比，我们能够将平均误差降低超过 0.1 星（或约 15%）。不错！
### 示例预测
让我们尝试一些示例预测作为健全性检查。 我们首先从数据集中随机挑选一个特定用户。
```
用户 #26556 评分了 21 个电影（平均评分为 3.7）
```
| | userId | movieId | rating | title | year |
| --- | --- | --- | --- | --- | --- |
| 4421455 | 26556 | 2705 | 5.0 | Airplane! | 1980 |
| 14722970 | 26556 | 2706 | 5.0 | Airplane II: The Sequel | 1982 |
| 7435440 | 26556 | 2286 | 4.5 | Fletch | 1985 |
| 16621016 | 26556 | 2216 | 4.5 | History of the World: Part I | 1981 |
| 11648630 | 26556 | 534 | 4.5 | Six Degrees of Separation | 1993 |
| 14805184 | 26556 | 937 | 4.5 | Mr. Smith Goes to Washington | 1939 |
| 14313285 | 26556 | 2102 | 4.5 | Strangers on a Train | 1951 |
| 13671173 | 26556 | 2863 | 4.5 | Dr. No | 1962 |
| 13661434 | 26556 | 913 | 4.0 | Notorious | 1946 |
| 11938282 | 26556 | 916 | 4.0 | To Catch a Thief | 1955 |
| 2354167 | 26556 | 3890 | 4.0 | Diamonds Are Forever | 1971 |
| 16095891 | 26556 | 730 | 4.0 | Spy Hard | 1996 |
| 16265128 | 26556 | 3414 | 3.5 | Network | 1976 |
| 13050537 | 26556 | 1414 | 3.5 | Waiting for Guffman | 1996 |
| 9891416 | 26556 | 2907 | 3.5 | Thunderball | 1965 |
| 3496223 | 26556 | 4917 | 3.5 | Gosford Park | 2001 |
| 1996728 | 26556 | 1861 | 3.0 | On the Waterfront | 1954 |
| 15893218 | 26556 | 1082 | 2.5 | A Streetcar Named Desire | 1951 |
| 13875921 | 26556 | 3445 | 2.5 | Keeping the Faith | 2000 |
| 13163853 | 26556 | 1225 | 2.0 | The Day the Earth Stood Still | 1951 |
| 7262983 | 26556 | 2348 | 0.5 | A Civil Action | 1998 |
用户 26556 给电影《空前绝后满天飞》和《空前绝后满天飞 II》打了两个完美的评分。很棒的选择！ 也许他们也会喜欢《白头神探》系列 - 另一系列由 Leslie Nielsen 主演的恶搞电影。
我们没有那么多关于这个用户讨厌什么的证据。 我们不根据他们的少数低评分做推断，用户不喜欢什么的更好推断是，他们甚至没有评价的电影类型。 让我们再举几个电影的例子，根据用户的评分历史记录，他们似乎不太可能看过。
```py
candidate_movies = movies[
movies.title.str.contains('Naked Gun')
| (movies.title == 'The Sisterhood of the Traveling Pants')
| (movies.title == 'Lilo & Stitch')
].copy()
preds = model.predict([
[uid] * len(candidate_movies), # 用户 ID
candidate_movies.index, # 电影 ID
])
# 注意：记住我们在 'y' 上训练，这是评分列的中心为 0 的版本。
# 要将我们模型的输出值转换为 [0.5, 5] 原始的星级评分范围，
# 我们需要通过添加均值来对值“去中心化”
row = df.iloc[0] # rating 和 y 之间的差对于所有行都是相同的，所以我们可以使用第一行
y_delta = row.rating - row.y
candidate_movies['predicted_rating'] = preds + y_delta
# 添加一列，带有我们的预测评分（对于此用户）
# 和电影对于数据集中所有用户的总体平均评分之间的差
candidate_movies['delta'] = candidate_movies['predicted_rating'] - candidate_movies['mean_rating']
candidate_movies.sort_values(by='delta', ascending=False)
```
| | title | year | mean_rating | n_ratings | predicted_rating | delta |
| --- | --- | --- | --- | --- | --- | --- |
| movieId | | | | | | |
| 366 | Naked Gun 33 1/3: The Final Insult | 1994 | 2.954226 | 13534.0 | 3.816926 | 0.862699 |
| 3776 | The Naked Gun 2 1/2: The Smell of Fear | 1991 | 3.132616 | 4415.0 | 3.946124 | 0.813508 |
| 3775 | The Naked Gun: From the Files of Police Squad! | 1988 | 3.580381 | 6973.0 | 4.236419 | 0.656037 |
| 5347 | Lilo & Stitch | 2002 | 3.489323 | 4402.0 | 3.971318 | 0.481995 |
| 10138 | The Sisterhood of the Traveling Pants | 2005 | 3.369987 | 773.0 | 2.041227 | -1.328760 |
看起来很合理！ 对于《白头神探》系列中的每部电影，我们对此用户的预测评分，大约比数据集中平均评分高一星，而我们的“out of left field”使它们的预测评分低于平均值。
### 你的回合
前往[练习笔记本](https://www.kaggle.com/kernels/fork/1598432)，进行嵌入层的实践练习。
## 二、用于推荐问题的矩阵分解
在上一课中，我们训练了一个模型来预测在 MovieLens 数据集中，用户给电影的评分。 提醒一下，模型看起来像这样：
我们为电影和用户查找嵌入向量，将它们连接在一起。 然后我们添加一些隐层。 最后，这些在一个输出节点汇集在一起来预测评分。
在本节课中，我将展示一个更简单的架构，来解决同样的问题：矩阵分解。 更简单可以是一件非常好的事情！ 有时，简单的模型会快速收敛到适当的解决方案，一个更复杂的模型可能会过拟合或无法收敛。
这是我们的矩阵分解模型的样子：
### 点积
让我们回顾一下数学。 如果你是线性代数专家，请跳过本节。
两个长度为`n`的向量`a`和`b`的点积定义为：
结果是单个标量（不是向量）。
点积仅为相同长度的矢量而定义。 这意味着我们需要为电影嵌入和用户嵌入使用相同的大小。
例如，假设我们已经训练了大小为 4 的嵌入，并且电影 Twister 由向量表示：
```
m_Twister=[ 1.0 −0.5 0.3 −0.1 ]
```
用户 Stanley 表示为：
```
u_Stanley=[ −0.2 1.5 −0.1 0.9 ]
```
我们认为 Stanley 会给 Twister 什么评分？ 我们可以将模型的输出计算为：
```
m_Twister · u_Stanley
= (1.0·−0.2)+(−0.5·1.5)+(0.3·−0.1)+(−0.1·0.9)
= −1.07
```
因为我们正在在评分列的中心版本上训练，所以我们的模型输出的比例为 0 等于训练集中的总体平均评分（约 3.5）。 因此我们预测 Stanley 将给 Twister `3.5+(−1.07)=2.43`星。
### 为什么
有一个直观的解释，支持了以这种方式组合我们的嵌入向量的决定。 假设我们的电影嵌入空间的维度对应于以下变化的轴：
维度 1：多么令人激动？
维度 2：多么浪漫？
维度 3：目标受众有多成熟？
维度 4：多么好笑？
因此，Twister 是一部令人激动的灾难电影，`m1`的正值为 1.0。
简单来说，`u1`告诉我们“这个用户对动作片的看法如何？”。 他们喜欢它吗？讨厌它？还是不喜欢也不讨厌？
Stanley 的向量告诉，我们他是浪漫和喜剧的忠实粉丝，并且略微不喜欢动作和成熟的内容。 如果我们给他一部类似于最后一部的电影，除了它有很多浪漫元素，会怎么样？
```
m_Titanic=[ 1.0 1.1 0.3 −0.1 ]
```
不难预测这会如何影响我们的评分输出。 我们给 Stanley 更多他喜欢的东西，所以他的预测评分会增加。
```
predicted_rating(Stanley,Titanic)
= m_Titanic·u_Stanley+3.5
=(1.0·−0.2)+(1.1·1.5)+(0.3·−0.1)+(−0.1·0.9)+3.5
=4.83 stars
```
> 注：在实践中，我们的电影嵌入的维度的含义不会那么明确，但我们的电影嵌入空间和用户嵌入空间的含义从根本上联系在一起，这仍然是正确的：`ui`总是代表“这个用户多么喜欢某个电影，其质量由`mi`代表？“ （希望这也提供了一些直觉，为什么电影嵌入空间和用户嵌入空间在这个技巧中必须大小相同。）
### 实现它
创建此模型的代码，类似于我们在上一课中编写的代码，除了我使用点积层来组合用户和电影嵌入层的输出（而不是连接它们，并输入到密集层）。
```py
movie_embedding_size = user_embedding_size = 8
# 每个实例由两个输入组成：单个用户 ID 和单个电影 ID
user_id_input = keras.Input(shape=(1,), name='user_id')
movie_id_input = keras.Input(shape=(1,), name='movie_id')
user_embedded = keras.layers.Embedding(df.userId.max()+1, user_embedding_size,
input_length=1, name='user_embedding')(user_id_input)
movie_embedded = keras.layers.Embedding(df.movieId.max()+1, movie_embedding_size,
input_length=1, name='movie_embedding')(movie_id_input)
dotted = keras.layers.Dot(2)([user_embedded, movie_embedded])
out = keras.layers.Flatten()(dotted)
model = keras.Model(
inputs = [user_id_input, movie_id_input],
outputs = out,
)
model.compile(
tf.train.AdamOptimizer(0.001),
loss='MSE',
metrics=['MAE'],
)
model.summary(line_length=88)
'''
________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
========================================================================================
user_id (InputLayer) (None, 1) 0
________________________________________________________________________________________
movie_id (InputLayer) (None, 1) 0
________________________________________________________________________________________
user_embedding (Embedding) (None, 1, 8) 1107952 user_id[0][0]
________________________________________________________________________________________
movie_embedding (Embedding) (None, 1, 8) 213952 movie_id[0][0]
________________________________________________________________________________________
dot (Dot) (None, 1, 1) 0 user_embedding[0][0]
movie_embedding[0][0]
________________________________________________________________________________________
flatten (Flatten) (None, 1) 0 dot[0][0]
========================================================================================
Total params: 1,321,904
Trainable params: 1,321,904
Non-trainable params: 0
________________________________________________________________________________________
'''
```
让我们训练它。
```py
history = model.fit(
[df.userId, df.movieId],
df.y,
batch_size=5000,
epochs=20,
verbose=0,
validation_split=.05,
);
```
让我们将这个模型随时间的误差，与我们在上一课中训练的深度神经网络进行比较：
我们新的，更简单的模型（蓝色）看起来非常好。
然而，即使我们的嵌入相当小，两种模型都会产生一些明显的过拟合。 也就是说，训练集上的误差 - 实线 - 明显好于看不见的数据。 我们将在练习中尽快解决这个问题。
### 你的回合
前往[练习笔记本](https://www.kaggle.com/kernels/fork/1598589)，进行矩阵分解的实践练习。
## 三、使用 Gensim 探索嵌入
早些时候，我们训练了一个模型，使用一个网络，它带有为每个电影和用户学习的嵌入，来预测用户为电影提供的评分。 嵌入是强大的！但他们实际如何工作？
以前，我说嵌入捕获了它们所代表的对象的“含义”，并发现了有用的潜在结构。 让我们来测试吧！
### 查询嵌入
让我们加载我们之前训练过的模型，这样我们就可以研究它学到的嵌入权重。
```py
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow import keras
input_dir = '../input/movielens-preprocessing'
model_dir = '../input/movielens-spiffy-model'
model_path = os.path.join(model_dir, 'movie_svd_model_32.h5')
model = keras.models.load_model(model_path)
```
嵌入权重是模型内部的一部分，因此我们必须进行一些挖掘才能访问它们。 我们将获取负责嵌入电影的层，并使用`get_weights()`方法获取其学习的权重。
```py
emb_layer = model.get_layer('movie_embedding')
(w,) = emb_layer.get_weights()
w.shape
# (26744, 32)
```
对于那么多电影，我们的权重矩阵有 26,744 行。 每行是 32 个数字 - 我们的电影嵌入的大小。
我们来看一个示例电影向量：
```py
w[0]
'''
array([-0.08716497, -0.25286013, -0.52679837, -0.2602235 , -0.4349191 ,
-0.48805636, -0.30346015, -0.1416321 , 0.08305884, -0.17578898,
-0.36220485, 0.14578693, 0.37118354, -0.02961254, -0.063666 ,
-0.5223456 , 0.0526049 , 0.47991064, -0.19034313, -0.3271599 ,
0.32792446, -0.3794548 , -0.55778086, -0.42602876, 0.14532137,
0.21002969, -0.32203963, -0.46950188, -0.22500233, -0.08298543,
-0.00373308, -0.3885791 ], dtype=float32)
'''
```
这是什么电影的嵌入？ 让我们加载我们的电影元数据的数据帧。
```py
movies_path = os.path.join(input_dir, 'movie.csv')
movies_df = pd.read_csv(movies_path, index_col=0)
movies_df.head()
```
| | movieId | title | genres | key | year | n_ratings | mean_rating |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | 0 | Toy Story | Adventure|Animation|Children|Comedy|Fantasy | Toy Story | 1995 | 49695 | 3.921240 |
| 1 | 1 | Jumanji | Adventure|Children|Fantasy | Jumanji | 1995 | 22243 | 3.211977 |
| 2 | 2 | Grumpier Old Men | Comedy|Romance | Grumpier Old Men | 1995 | 12735 | 3.151040 |
| 3 | 3 | Waiting to Exhale | Comedy|Drama|Romance | Waiting to Exhale | 1995 | 2756 | 2.861393 |
| 4 | 4 | Father of the Bride Part II | Comedy | Father of the Bride Part II | 1995 | 12161 | 3.064592 |
当然，这是《玩具总动员》！ 我应该在任何地方认出这个向量。
好吧，我很滑稽。此时很难利用这些向量。 我们从未告诉模型如何使用任何特定嵌入维度。 我们只让它学习它认为有用的任何表示。
那么我们如何检查这些表示是否合理且连贯？
### 向量相似度
测试它的一种简单方法是，查看嵌入空间中电影对有多么接近或远离。 嵌入可以被认为是智能的距离度量。 如果我们的嵌入矩阵是良好的，它应该将类似的电影（如《玩具总动员》和《怪物史莱克》）映射到类似的向量。
```py
i_toy_story = 0
i_shrek = movies_df.loc[
movies_df.title == 'Shrek',
'movieId'
].iloc[0]
toy_story_vec = w[i_toy_story]
shrek_vec = w[i_shrek]
print(
toy_story_vec,
shrek_vec,
sep='\n',
)
'''
[-0.08716497 -0.25286013 -0.52679837 -0.2602235 -0.4349191 -0.48805636
-0.30346015 -0.1416321 0.08305884 -0.17578898 -0.36220485 0.14578693
0.37118354 -0.02961254 -0.063666 -0.5223456 0.0526049 0.47991064
-0.19034313 -0.3271599 0.32792446 -0.3794548 -0.55778086 -0.42602876
0.14532137 0.21002969 -0.32203963 -0.46950188 -0.22500233 -0.08298543
-0.00373308 -0.3885791 ]
[ 0.0570179 0.5991162 -0.71662885 0.22245468 -0.40536046 -0.33602375
-0.24281627 0.08997302 0.03362623 -0.12569055 -0.2764452 -0.12710975
0.48197436 0.2724923 0.01551001 -0.20889504 -0.04863157 0.39106563
-0.24811408 -0.05642252 0.24475795 -0.53363544 -0.2281187 -0.17529544
0.21050802 -0.37807122 0.03861505 -0.27024794 -0.24332719 -0.17732081
0.07961234 -0.39079434]
'''
```
逐个维度地比较，这些看起来大致相似。 如果我们想为它们的相似度分配一个数字，我们可以计算这两个向量之间的欧氏距离。 （这是我们传统的“乌鸦飞过的”两点之间的距离的概念。容易在 1,2 或 3 维上进行研究。在数学上，我们也可以将它扩展到 32 维，虽然需要好运来可视化它。）
```py
from scipy.spatial import distance
distance.euclidean(toy_story_vec, shrek_vec)
# 1.4916094541549683
```
这与我们认为非常不同的一对电影相比如何？
```py
i_exorcist = movies_df.loc[
movies_df.title == 'The Exorcist',
'movieId'
].iloc[0]
exorcist_vec = w[i_exorcist]
distance.euclidean(toy_story_vec, exorcist_vec)
# 2.356588363647461
```
更远了，和我们期待的一样。
### 余弦距离
如果你看看[`scipy.spatial`模块的文档](https://docs.scipy.org/doc/scipy-0.14.0/reference/spatial.distance.html)，你会发现人们用于不同任务的距离，实际上有很多不同的衡量标准。
在判断嵌入的相似性时，使用[余弦相似性](https://en.wikipedia.org/wiki/Cosine_similarity)更为常见。
简而言之，两个向量的余弦相似度范围从 -1 到 1，并且是向量之间的角度的函数。 如果两个向量指向同一方向，则它们的余弦相似度为 1。如果它们指向相反的方向，它为 -1。 如果它们是正交的（即成直角），则它们的余弦相似度为 0。
余弦距离定义为 1 减去余弦相似度（因此范围从 0 到 2）。
让我们计算电影向量之间的几个余弦距离：
```py
print(
distance.cosine(toy_story_vec, shrek_vec),
distance.cosine(toy_story_vec, exorcist_vec),
sep='\n'
)
'''
0.3593705892562866
0.811933159828186
'''
```
> 注：为什么在使用嵌入时常用余弦距离？ 与许多深度学习技巧一样，简短的答案是“凭经验，它能用”。 在即将进行的练习中，你将进行一些实践调查，更深入地探讨这个问题。
哪部电影与《玩具总动员》最相似？ 在嵌入空间中哪些电影落在 Psycho 和 Scream 之间？ 我们可以编写一堆代码来解决这样的问题，但这样做非常繁琐。 幸运的是，已经有一个库可以完成这类工作：Gensim。
## 使用 Gensim 探索嵌入
我将使用我们的模型的电影嵌入和相应电影的标题，来实例化`WordEmbeddingsKeyedVectors`。
> 注：你可能会注意到，Gensim 的文档及其许多类和方法名称都指的是词嵌入。 虽然库最常用于文本领域，但我们可以使用它来探索任何类型的嵌入。
```py
from gensim.models.keyedvectors import WordEmbeddingsKeyedVectors
# 将数据集中的电影限制为至少具有这么多评分
threshold = 100
mainstream_movies = movies_df[movies_df.n_ratings >= threshold].reset_index(drop=True)
movie_embedding_size = w.shape[1]
kv = WordEmbeddingsKeyedVectors(movie_embedding_size)
kv.add(
mainstream_movies['key'].values,
w[mainstream_movies.movieId]
)
```
好的，哪个电影与《玩具总动员》最相似？
```py
kv.most_similar('Toy Story')
'''
/opt/conda/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.
if np.issubdtype(vec.dtype, np.int):
[('Toy Story 2', 0.9583659172058105),
('Toy Story 3', 0.9159570932388306),
('Finding Nemo', 0.882755696773529),
('Monsters, Inc.', 0.8684015870094299),
("A Bug's Life", 0.8322919607162476),
('The Incredibles', 0.8271597623825073),
('Ratatouille', 0.8149864673614502),
('Up', 0.802034318447113),
('WALL·E', 0.7794805765151978),
('The Iron Giant', 0.7664535641670227)]
'''
```
哇，这些都很棒！ 《玩具总动员 2》是与玩具总动员最相似的电影，这是完全合理的。 其余大多数都是具有类似计算机动画风格的动画儿童电影。
所以它学到了关于三维动画儿童电影的一些东西，但也许这只是一个侥幸。 让我们来看看几个不同类型的电影的最近邻居：
```
/opt/conda/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.
if np.issubdtype(vec.dtype, np.int):
```
小众的性爱剧，风骚的半吊子喜剧，老派音乐剧，超级英雄电影......我们的嵌入能够支持各种各样的电影类型！
### 语义向量数学
`most_similar`方法接受可选的第二个参数`negative`。 如果我们调用`kv.most_similar(a, b)`，那么它将找到最接近`a-b`的向量，而不是找到最接近`a`的向量。
你为什么想这么做？ 事实证明，对嵌入向量进行加法和减法通常会产生令人惊讶的有意义的结果。 例如，你将如何填写以下等式？
```
Scream = Psycho + ________
```
Scream 和 Psycho 的相似之处在于它们是恐怖片和惊悚片之间的某个地方的暴力恐怖电影。 最大的区别是 Scream 有喜剧元素。 因此，如果你将 Psycho 与喜剧结合起来，我会说 Scream 就是你所得到的。
但我们实际上可以通过向量数学（在重新排列之后）让 Gensim 为我们填补空白：
```
________ = Scream - Psycho
```
```py
kv.most_similar(
positive = ['Scream'],
negative = ['Psycho (1960)']
)
'''
/opt/conda/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.
if np.issubdtype(vec.dtype, np.int):
[('Scream 3', 0.6535503268241882),
('Scream 2', 0.6417772769927979),
('Piranha (Piranha 3D)', 0.6411199569702148),
('Freddy vs. Jason', 0.6275623440742493),
('Final Destination 5', 0.6264907121658325),
('Booty Call', 0.6207411289215088),
("Charlie's Angels", 0.6146069765090942),
('Mortal Kombat', 0.6145076155662537),
('Deuce Bigalow: Male Gigolo', 0.6140967607498169),
('Final Destination 2', 0.612423300743103)]
'''
```
如果你熟悉这些电影，你会发现，从 Psycho 到 Scream 的缺失成分是喜剧（也是 90 年代后期的青少年电影）。
### 类比解决
用于进入美国大学和学院的 SAT 考试提出了类似的问题：
```
shower : deluge :: _____ : stare
```
（意思是“shower”（淋浴）对于“deluge”（洪水），相当于“_____”对于“stare”（凝视））
为了解决这个问题，我们找到了“deluge”和“shower”之间的关系，并将其应用于“stare”。 “shower”是一种温和的“deluge”形式。 什么是温和的“stare”的形式？ 这里一个好的答案是“glance”（一瞥）或“look”（看）。
令人惊讶的是，这种方法很有效，但人们发现这些通常可以通过单词嵌入的简单向量数学来有效地解决。 我们可以通过嵌入来解决电影类比问题吗？ 我们试试吧。这样如何：
```
Brave : Cars 2 :: Pocahontas : _____
```
答案不明确。 一种解释是，《勇敢传说》（Brave）就像《赛车总动员 2》（Cars 2），除了后者主要针对男孩，而前者可能对女孩更具吸引力，因为它是女性主角。 所以也许答案应该像《风中奇缘》（Pocahontas）一样，90 年代中期的传统动画儿童电影，但更像是一部“男孩电影”。《大力士》？《狮子王》？
让我们问一下他们的想法。
在向量数学方面，我们可以将其构建为......
```
Cars 2 = Brave + X
_____ = Pocahontas + X
```
重新排列之后，我们得到：
```
____ = Pocahontas + (Cars 2 - Brave)
```
我们可以通过将两部电影（《风中奇缘》和《赛车总动员 2》）传递给`most_similar`的`positive`，将《勇敢传说》作为`negative`参数，来解决这个问题：
```py
kv.most_similar(
['Pocahontas', 'Cars 2'],
negative = ['Brave']
)
'''
/opt/conda/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.
if np.issubdtype(vec.dtype, np.int):
[("A Kid in King Arthur's Court", 0.8660464882850647),
('Land Before Time III: The Time of the Great Giving', 0.8655920624732971),
('Free Willy 2: The Adventure Home', 0.8606677651405334),
('3 Ninjas Knuckle Up', 0.8496973514556885),
('3 Ninjas Kick Back', 0.8479241132736206),
('The Return of Jafar', 0.8474882245063782),
("Beethoven's 2nd", 0.8443870544433594),
('Air Bud: Golden Receiver', 0.84358811378479),
('Meet the Deedles', 0.8370730876922607),
('All Dogs Go to Heaven 2', 0.8368842601776123)]
'''
```
这与我们的预测无关：4 部最接近的电影确实是 90 年代的儿童动画电影。 在那之后，结果有点令人困惑。
我们的模型是错的，还是我们是错的？ 在《赛车总动员 2》和《勇敢传说》之间，我们未能解释的另一个区别是前者是续集，而后者则不是。 我们的结果中有 7/10 也是续集。 这告诉我们关于我们学习的嵌入的一些有趣内容（最终，关于预测电影偏好的问题）。 “Sequelness”是我们模型的一个重要特性 - 这表明我们数据中的一些变化，是因为有些人倾向于比其他人更喜欢续集。
### 你的回合
前往[练习笔记本](https://www.kaggle.com/kernels/fork/1598893)，进行一些动手实践，使用 gensim 探索嵌入。
## 四、将 t-SNE 用于可视化
在上一课中，我们查看了我们学习的电影嵌入的一些示例，测量了电影对之间的距离，查找了与某些电影最相似的电影，并且通过向量数学组合了电影语义。 这些是调试嵌入模型或理解嵌入模型的好方法。 但它也非常耗时。
在本课程中，你将学习如何使用 t-SNE 算法可视化嵌入。 这是一种很好的廉价技术，用于理解嵌入的本质。
### t-SNE
可视化 1 维或 2 维的数据很容易 - 但目前尚不清楚如何可视化 8 维或 32 维的嵌入。 t-SNE 是一种降维算法，通常用于可视化。 它学习从一组高维向量到较小维数（通常为 2）的空间映射，这有望很好地表示高维空间。
是什么让映射成为“良好的表示”？ 简而言之，t-SNE 试图确保如果高维向量`u`和`v`靠近在一起，则`map(u)`和`map(v)`在 2d 映射空间中靠近在一起。
### 代码
首先，我们将加载我们的预训练嵌入，就像之前一样。
```py
%matplotlib inline
import random
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow import keras
input_dir = '../input/movielens-preprocessing'
model_dir = '../input/movielens-spiffy-model'
model_path = os.path.join(model_dir, 'movie_svd_model_32.h5')
model = keras.models.load_model(model_path)
emb_layer = model.get_layer('movie_embedding')
(w,) = emb_layer.get_weights()
movies_path = os.path.join(input_dir, 'movie.csv')
movies_df = pd.read_csv(movies_path, index_col=0)
```
正如我们在前面的课程中看到的那样，我们的数据集中有很多不起眼的电影，评分很少（有时只有一个）。 我们对这些电影知之甚少，因为它们的嵌入效果和随机一样。 我们可以通过仅仅选择满足一定流行度阈值的电影，来弄清楚我们的可视化。
```py
threshold = 100
mainstream_movies = movies_df[movies_df.n_ratings >= threshold].reset_index(drop=True)
print("Went from {} to {} movies after applying threshold".format(
len(movies_df), len(mainstream_movies),
))
w_full = w
w = w[mainstream_movies.movieId]
df = mainstream_movies
# 在应用阈值后，电影从 26744 变为 8546 部
```
我们将使用 scikit-learn 的 t-SNE 实现。
我提到 t-SNE 在特征空间中试图保持实体之间的“接近度”。 我们在之前的课程中看到，有许多竞争性的距离概念。 默认情况下，t-SNE 使用欧氏距离。 但是因为已知余弦距离适用于嵌入，我们将在创建模型时传递`metric ="cosine"`。
```py
from sklearn.manifold import TSNE
# 1,000 次迭代的默认值可以得到很好的结果，
# 但是我的训练时间更长，只是为了一些微小的改进。
# 注意：这需要近一个小时！
tsne = TSNE(random_state=1, n_iter=15000, metric="cosine")
embs = tsne.fit_transform(w)
# 为方便起见，添加到数据帧
df['x'] = embs[:, 0]
df['y'] = embs[:, 1]
```
这是我们将电影映射到的二维向量样本：
```py
embs[:5]
'''
array([[ -93.78184 , 74.296936 ],
[ -78.09159 , -107.294334 ],
[ 27.506392 , -73.33844 ],
[ -7.8512335, -82.217896 ],
[ -10.345706 , -71.288704 ]], dtype=float32)
'''
```
这种降维的全部意义在于可视化，所以让我们使用 matplotlib 绘制我们电影的散点图，使用我们新的二维映射。
```py
FS = (10, 8)
fig, ax = plt.subplots(figsize=FS)
# 使点变得半透明，以便我们可以直观地识别具有高密度重叠点的区域
ax.scatter(df.x, df.y, alpha=.1);
```
### 它有效嘛
单凭形状很难判断。 良好的理智检查是识别，我们强烈认为应该靠近的一些电影分组，看看它们是否在二维空间中接近。
例如，所有的哈利波特电影都应该互相接近，对吧？
```py
# 这个和其他几个辅助函数在上面的代码单元中定义。
# 如果你对它们的实现方式感到好奇，请点击上面的“代码”按钮。
plot_by_title_pattern('Harry Potter', figsize=(15, 9), bg_alpha=.05, text=False);
```
上面的绘图中，8 个哈利波特电影中的每一个都有一个绿点 - 但它们是如此接近，它们无法在这个刻度上区分。 是个好的标志！
让我们放大一下，仔细看看。
```py
plot_region_around('Harry Potter and the Order of the Phoenix', 4);
```
哈利波特的电影不仅紧密聚集在一起，而且大致按发布顺序排列！
### 局部和全局结构
t-SNE 的一个关键特性使它非常适合可视化，它擅长在多个尺度上捕获簇。 我们已经看到，我们的映射成功捕获了小而紧凑的局部结构。 那些包含更多松散的相关电影的大型结构呢？
我们在上面已经看到了这方面的一个小例子：与哈利波特电影最接近的邻居是饥饿游戏系列的电影 - 另一组基于一系列青年幻想小说的电影。这说得通！
小众流派如何？ 纪录片落在哪里？
```py
docs = df[ (df.genres == 'Documentary') ]
plot_with_annotations(docs.index, text=False, alpha=.4, figsize=(15, 8));
```
太好了！ 它不是一个紧密的簇，但这里肯定有较强的规律。
并且重申一下：我们从未真正将类型展示给模型作为特征。 它无法读取标题，并看到《哈利波特和魔法石》和《哈利波特和密室》属于同一系列。 它设法获取这些潜在的模式并将它们合并到嵌入空间中，只需看到数据点，例如“用户 5299 给电影 806 评分为 4.5”。 非常好！
这是另一个稍微复杂的类型实验：可视化所有电影，其类型是`{喜剧，戏剧，浪漫}`的一部分（即喜剧，戏剧，浪漫，戏剧，浪漫剧，romcoms 和......我猜是“dromcoms”？）
这是最大规模的结构的一个很棒的例子。 戏剧主要在上半部分，而喜剧主要在另一半（浪漫片的分布更加分散）。
### 你的回合
前往[练习笔记本](https://www.kaggle.com/kernels/fork/1599029)进行一些实践练习，使用 t-SNE 可视化嵌入。
## 扩展阅读
我们使用 t-SNE 模型的开箱即用的默认参数取得了良好的效果，但根据你的数据特征，你可能不会那么幸运。
t-SNE 不是简单的闭式数学运算。 你正在训练模型，使用随机梯度下降来最小化一些非凸损失函数。 可能需要一段时间，需要一点折腾。 你甚至可以在使用相同参数训练的两个 t-SNE 模型之间看到非常不同的结果（如果你想要可重复性，则设置固定的`random_state`）。
如果你在尝试训练 t-SNE 模型时得到的结果不令人满意，或者你只是想了解更多数学基础和实现，那么下面的链接会提供一些你可能会觉得有用的信息。
+ 如果你对 t-SNE 的更深入的数学细节感兴趣，我强烈建议你查看 [Laurens van der Maaten 和 Geoff Hinton 向世界介绍 t-SNE 的原始论文](http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf)。
+ [如何使用 t-SNE](https://distill.pub/2016/misread-tsne/) 有效地展示了一些令人难以置信的实时交互式示例，允许你将 t-SNE 应用于各种合成数据集并实时观察训练，并看到改变参数的效果，例如 perplexity。
+ [sklearn TSNE 文档](http://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)提供了每个参数含义的详细信息，以及一些设置它们的提示。
+ 另请参阅：[scikit-learn 的 t-SNE 用户指南](http://scikit-learn.org/stable/modules/manifold.html#t-sne)
+ [t-SNE FAQ](https://lvdmaaten.github.io/tsne/#faq) 由 Laurens van der Maaten 撰写
================================================
FILE: docs/Kaggle/learn/intermediate-machine-learning/1.md
================================================
# Kaggle 官方教程：机器学习中级1 课程介绍
> 原文：[Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) > [Introduction](https://www.kaggle.com/alexisbcook/introduction)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
## 1、课程介绍
欢迎来到Kaggle Learning《机器学习中级》微课程！
如果你有一些机器学习的基础，并且你想学习如何快速提高模型的质量，那么你就来对地方了！在这个微型课程中，你将学习如何：
- 处理现实数据集中常见的数据类型(`缺失的值、分类变量`)，
- 设计`pipelines`来提高机器学习代码的质量，
- 使用先进的技术进行模型验证(`交叉验证`)，
- 建立最先进的模型，广泛用于赢得Kaggle比赛(`XGBoost`)，和
- 避免常见和重要的数据科学错误(`泄漏`)。
在此过程中，你将通过使用各个新主题的真实数据完成实际操作来巩固你的知识。实际操作数据来自于赛题 [Housing Prices Competition for Kaggle Learn Users](https://www.kaggle.com/c/home-data-for-ml-course), 你将使用79个不同的统计变量(如屋顶类型、卧室数量和浴室数量)来预测房价。通过提交预测结果，观察你在排行榜上的名次上升！
## 2、先决条件
如果你以前构建过机器学习模型，并且熟悉模型验证、欠拟合和过拟合以及随机森林等主题，那么你已经为这门微型课程做好了准备。
如果你对机器学习完全陌生，请学习我们的微课程[《机器学习入门》](https://leytton.blog.csdn.net/article/details/101154693)，它涵盖了机器学习的基础知识。
## 3、去吧，皮卡丘
继续[第一个练习](https://www.kaggle.com/kernels/fork/3370272)，学习如何向Kaggle竞赛提交预测结果，并确定在开始之前可能需要检查的内容。
================================================
FILE: docs/Kaggle/learn/intermediate-machine-learning/2.md
================================================
# Kaggle 官方教程：机器学习中级2 缺失值处理
> 原文：[Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) > [Missing Values](https://www.kaggle.com/alexisbcook/missing-values)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
在本课程中，你将学习三种处理缺失值的方法。然后使用实际数据集比较这些方法的效果。
## 1、介绍
造成数据丢失的原因有很多。例如，
- 两间卧室的房子不包括第三间卧室的价值。
- 调查对象可能选择不分享其收入。
大多数机器学习库(包括`scikit-learn`)在试图使用缺失值的数据构建模型时都会出现错误。因此，你需要选择下面的策略之一。
## 2、三种方法
**1) 一个简单的选择：删除缺少值的列**
最简单的选择是删除缺少值的列。
除非删除列中的大多数值都丢失了，否则使用这种方法将丢失许多潜在价值的信息。举个极端的例子，假如有一个10,000行的数据集，其中一个重要的列缺少一条数据。这种方法将完全删除该列！
**2) 一个更好的选择：填充**
用一些数字填充缺失的值。例如，我们可以用平均值填充。
在大多数情况下，填充的值不一定正确，但它通常会比完全删除该列数据要好。
**3) 填充扩展**
`填充`是标准的方法，通常效果很好。然而，填充的值可能高于或低于实际值(数据集中没有收集这些值)。或者缺少值的行在某些方面可能是唯一的。在这种情况下，你的模型将考虑哪些值是最初丢失的，从而做出更好的预测。
在这种方法中，我们像以前一样输入缺失的值。此外，新增一列用于记录哪条数据是缺失填充的。
在某些情况下，这将非常有效。但某些情况，这没有一点用。
## 3、案例
在本例中，我们将使用 [Melbourne Housing](https://www.kaggle.com/dansbecker/melbourne-housing-snapshot/home) 数据集。我们的模型将使用房间数量和土地面积等信息来预测房价。
我们不会关注数据加载步骤。相反，你可以想象你已经拥有了`X_train`、`X_valid`、`y_train`和`y_valid`中的训练和验证数据。
**定义函数来评估每种方法的效果**
我们定义了一个函数`score_dataset()`来比较处理缺失值的不同方法。该函数计算`随机森林模型`的`平均绝对误差(MAE)`。
**方法1的得分(删除缺少值的列)**
由于我们同时处理训练集和验证集，所以要注意在两个数据框中删除相同的列。
```python
# 获取缺少值的列名称
cols_with_missing = [col for col in X_train.columns
if X_train[col].isnull().any()]
# 删除训练和验证数据中的列
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_valid = X_valid.drop(cols_with_missing, axis=1)
print("MAE from Approach 1 (Drop columns with missing values):")
print(score_dataset(reduced_X_train, reduced_X_valid, y_train, y_valid))
```
输出结果：
```
MAE from Approach 1 (Drop columns with missing values):
183550.22137772635
```
**方法2得分(填充)**
接下来，我们使用`SimpleImputer`用每一列的平均值填充缺失的值。
虽然它很简单，但是填充平均值的方法通常很好用(不同数据集有所差异)。尽管统计学家已经尝试了更复杂的方法来确定填充值(例如回归填充)，但一旦将结果插入复杂的机器学习模型，这些复杂的策略通常不会带来额外的好处。
```python
from sklearn.impute import SimpleImputer
# 填充
my_imputer = SimpleImputer()
imputed_X_train = pd.DataFrame(my_imputer.fit_transform(X_train))
imputed_X_valid = pd.DataFrame(my_imputer.transform(X_valid))
# 填充移除了列名;补回来
imputed_X_train.columns = X_train.columns
imputed_X_valid.columns = X_valid.columns
print("MAE from Approach 2 (Imputation):")
print(score_dataset(imputed_X_train, imputed_X_valid, y_train, y_valid))
```
输出结果：
```
MAE from Approach 2 (Imputation):
178166.46269899711
```
我们看到方法2的`MAE`比方法1低，所以方法2在这个数据集中表现得更好。
**方法3得分(填充扩展)**
接下来，我们填充缺失的值，同时记录哪些值是填充的。
```python
# Make copy to avoid changing original data (when imputing)
X_train_plus = X_train.copy()
X_valid_plus = X_valid.copy()
# Make new columns indicating what will be imputed
for col in cols_with_missing:
X_train_plus[col + '_was_missing'] = X_train_plus[col].isnull()
X_valid_plus[col + '_was_missing'] = X_valid_plus[col].isnull()
# Imputation
my_imputer = SimpleImputer()
imputed_X_train_plus = pd.DataFrame(my_imputer.fit_transform(X_train_plus))
imputed_X_valid_plus = pd.DataFrame(my_imputer.transform(X_valid_plus))
# Imputation removed column names; put them back
imputed_X_train_plus.columns = X_train_plus.columns
imputed_X_valid_plus.columns = X_valid_plus.columns
print("MAE from Approach 3 (An Extension to Imputation):")
print(score_dataset(imputed_X_train_plus, imputed_X_valid_plus, y_train, y_valid))
```
输出结果：
```
MAE from Approach 3 (An Extension to Imputation):
178927.503183954
```
正如我们所看到的，方法3的效果略差于方法2。
**那么，为什么填充比删除列更好呢?**
训练数据有10864行和12列，其中3列包含丢失的数据。对于每一列，缺少的条目不到一半。因此，删除列会删除很多有用的信息，因此填充方法效果会更好。
```python
# Shape of training data (num_rows, num_columns)
print(X_train.shape)
# Number of missing values in each column of training data
missing_val_count_by_column = (X_train.isnull().sum())
print(missing_val_count_by_column[missing_val_count_by_column > 0])
```
输出结果：
```
(10864, 12)
Car 49
BuildingArea 5156
YearBuilt 4307
dtype: int64
```
## 4、结论
一般来说，比起简单地删除具有缺失值的列(在方法1中)，填充缺失值(在方法2和方法3中)会得到更好的效果。
## 5、去吧，皮卡丘
在[练习中](https://www.kaggle.com/kernels/fork/3370280)比较处理缺失值的方法。
================================================
FILE: docs/Kaggle/learn/intermediate-machine-learning/3.md
================================================
# Kaggle 官方教程：机器学习中级3 分类变量
> 原文：[Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) > [Categorical Variables](https://www.kaggle.com/alexisbcook/categorical-variables)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
在本教程中，你将了解什么是分类变量，以及处理这类数据的三种方法。
## 1、介绍
分类变量类似于枚举，拥有特定数量的值类型。
- 比如一项调查，询问你多久吃一次早餐，并提供四个选项:“从不”、“很少”、“大多数日子”或“每天”。在本例中，数据是分类的，因为答案属于一组固定的类别。
- 如果对人们所拥有的汽车品牌进行调查，回答可以分为“本田”、“丰田”和“福特”。在本例中，数据也是分类的。
如果您没有预处理这些分类变量，就将这些变量应用于机器学习模型中，大多数情况您将得到一个错误结果。在本教程中，我们将比较三种预处理分类数据的方法。
## 2、三种方法
**1) 删除分类变量**
处理分类变量最简单的方法是从数据集中删除它们。这种方法适用于该列中不包含有用信息的情况。
**2) 标签编码**
标签编码将每个变量类型标记为不同的整数。
这种方法假设类别的顺序为:“Never”(0)<“rare”(1)<“Most days”(2)<“Every day”(3)。
在本例中，这个假设是有意义的，因为对类别有个唯一的排名。 并不是所有的分类变量在值中都有一个明确的顺序，但是我们将那些有顺序的变量称为`有序变量`。对于基于树的模型(如决策树和随机森林)，有序变量的标签编码可能效果不错。
**3) One-Hot 编码**
“One-hot”编码创建新列，表明原始数据中每个可能值的存在(或不存在)。为了说明这点，我们举个例子：
)
在原始数据集中，“Color”是一个分类变量，包含“Red”、“Yellow”和“Green”三个类别。对应的`one-hot编码`是每个可能的值各自作为一列，原始数据集中的每一行作为一行。当原始值为“Red”时，我们在“Red”列中放入1;如果原始值是“Yellow”，则在“Yellow”列中放入1，以此类推。
与`标签编码`不同，`one-hot编码`不假定类别的顺序。因此，如果在分类数据中没有明确的顺序(例如，“红色”既不比“黄色”多也不比“黄色”少)，这种方法可能会特别有效。我们把没有内在排序的分类变量称为`名义变量`。
如果分类变量具有大量不同的值（超过15个），效果将不会很好。
## 3、案例
像前一篇教程一样，我们将使用 [Melbourne Housing数据集](https://www.kaggle.com/dansbecker/melbourne-housing-snapshot/home)。
我们不会关注数据加载步骤。假设您已经拥有了`X_train`、`X_valid`、`y_train`和`y_valid`中的训练和验证数据。
```python
import pandas as pd
from sklearn.model_selection import train_test_split
# Read the data
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
# Separate target from predictors
y = data.Price
X = data.drop(['Price'], axis=1)
# Divide data into training and validation subsets
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2,
random_state=0)
# Drop columns with missing values (simplest approach)
cols_with_missing = [col for col in X_train_full.columns if X_train_full[col].isnull().any()]
X_train_full.drop(cols_with_missing, axis=1, inplace=True)
X_valid_full.drop(cols_with_missing, axis=1, inplace=True)
# "Cardinality" means the number of unique values in a column
# Select categorical columns with relatively low cardinality (convenient but arbitrary)
low_cardinality_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and
X_train_full[cname].dtype == "object"]
# Select numerical columns
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
# Keep selected columns only
my_cols = low_cardinality_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
```
我们使用下面的`head()`方法查看训练数据。
```python
X_train.head()
```
输出结果：
```
Type Method Regionname Rooms Distance Postcode Bedroom2 Bathroom Landsize Lattitude Longtitude Propertycount
12167 u S Southern Metropolitan 1 5.0 3182.0 1.0 1.0 0.0 -37.85984 144.9867 13240.0
6524 h SA Western Metropolitan 2 8.0 3016.0 2.0 2.0 193.0 -37.85800 144.9005 6380.0
8413 h S Western Metropolitan 3 12.6 3020.0 3.0 1.0 555.0 -37.79880 144.8220 3755.0
2919 u SP Northern Metropolitan 3 13.0 3046.0 3.0 1.0 265.0 -37.70830 144.9158 8870.0
6043 h S Western Metropolitan 3 13.3 3020.0 3.0 1.0 673.0 -37.76230 144.8272 4217.0
```
接下来，我们获得训练数据中所有分类变量的列。
我们通过检查每个列的数据类型(或`dtype`)来做到这一点。`object` 类型表示改列存在文本(理论上它还可以是其他东西，但对于我们的目的来说并不重要)。对于这个数据集，带有文本的列表示分类变量。
```python
# Get list of categorical variables
s = (X_train.dtypes == 'object')
object_cols = list(s[s].index)
print("Categorical variables:")
print(object_cols)
```
输出结果：
```
Categorical variables:
['Type', 'Method', 'Regionname']
```
**定义函数来评估每种方法的效果**
我们定义了一个函数`score_dataset()`来比较处理分类变量的不同方法。该函数计算`随机森林模型`的`平均绝对误差(MAE)`。一般来说，我们希望`MAE`越低越好！
**方法1的得分(删除分类变量)**
我们使用`select_dtypes()`方法删除对象列。
```python
drop_X_train = X_train.select_dtypes(exclude=['object'])
drop_X_valid = X_valid.select_dtypes(exclude=['object'])
print("MAE from Approach 1 (Drop categorical variables):")
print(score_dataset(drop_X_train, drop_X_valid, y_train, y_valid))
```
```
MAE from Approach 1 (Drop categorical variables):
175703.48185157913
```
**方法2的得分(标签编码)**
`Scikit-learn`有一个`LabelEncoder`类，可以用来获取标签编码。我们循环遍历分类变量，并将标签编码器分别应用于每一列。
```python
from sklearn.preprocessing import LabelEncoder
# 复制一份数据防止改变源数据
label_X_train = X_train.copy()
label_X_valid = X_valid.copy()
# 将标签编码器分别应用于每一列
label_encoder = LabelEncoder()
for col in object_cols:
label_X_train[col] = label_encoder.fit_transform(X_train[col])
label_X_valid[col] = label_encoder.transform(X_valid[col])
print("MAE from Approach 2 (Label Encoding):")
print(score_dataset(label_X_train, label_X_valid, y_train, y_valid))
```
```
MAE from Approach 2 (Label Encoding):
165936.40548390493
```
在上面的代码中，对于每个列，我们随机分配一个唯一整数。这是一种比提供自定义标签更简单的常见方法；然而，如果我们为所有有序变量提供更好的信息标签，效果会更好。
**方法3的得分((One-Hot编码)**
我们使用`scikit-learn`的`OneHotEncoder`类来获得`one-hot编码`。有许多参数可定义。
- 设置`handle_unknown='ignore'`，以避免在验证数据包含训练数据中没有包括的值时发生错误
- 设置`sparse=False`可以确保将已编码的列作为`numpy数组`(而不是稀疏矩阵)返回。
为了使用这个编码器，我们提供了只有分类变量的数据列。举例来说，为了对训练数据进行编码，我们提供了`X_train[object_cols]`(代码中的`object_cols`表示分类变量名称，`X_train[object_cols]`包含了训练数据的所有分类变量)。
```python
from sklearn.preprocessing import OneHotEncoder
# 将one-hot编码器分别应用于每一列分类变量
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[object_cols]))
OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[object_cols]))
# One-hot编码时移除了index;补回来
OH_cols_train.index = X_train.index
OH_cols_valid.index = X_valid.index
# 删除分类列(将替换为One-hot编码),留下编码列
num_X_train = X_train.drop(object_cols, axis=1)
num_X_valid = X_valid.drop(object_cols, axis=1)
# 向数值特征添加One-hot编码列
OH_X_train = pd.concat([num_X_train, OH_cols_train], axis=1)
OH_X_valid = pd.concat([num_X_valid, OH_cols_valid], axis=1)
print("MAE from Approach 3 (One-Hot Encoding):")
print(score_dataset(OH_X_train, OH_X_valid, y_train, y_valid))
```
输出结果：
```
MAE from Approach 3 (One-Hot Encoding):
166089.4893009678
```
## 4、哪种方法最好?
在本案例中，`删除分类变量`(方法1)的性能最差，因为它有最高的`MAE`分数。至于另外两种方法，由于返回的`MAE`分数值非常接近，没有太大差异。
通常，`one-hot编码`(方法3)的效果最好，`删除分类变量`(方法1)的效果最差，但还得视情况而定。
## 5、结论
这个世界充满了分类数据。如果您知道如何使用这种常见的数据类型，您将成为一个更高效的数据科学家！
## 6、去吧，皮卡丘
把你的新技能运用到下面的[练习中](https://www.kaggle.com/kernels/fork/3370279)！
================================================
FILE: docs/Kaggle/learn/intermediate-machine-learning/4.md
================================================
# Kaggle 官方教程：机器学习中级4 Pipeline
> 原文：[Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) > [Pipelines](https://www.kaggle.com/alexisbcook/pipelines)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
在本教程中，你将学习如何使用`pipeline`来清理你的建模代码。
## 1、介绍
`Pipeline`是一种简单的方法，能让你的数据预处理和建模步骤一步到位。
很多数据科学家没有使用`pipeline`来建模，但`pipeline`有很多重要好处。包含：
1. 更精简的代码：考虑到数据处理时会造成混乱，使用`pipeline`不需要在每个步骤都特别注意训练和验证数据。
2. 更少的Bug：错误应用和忘记处理步骤的概率更小。
3. 更易产品化：把模型转化成规模化发布原型是比较难的，再此我们不做过多讨论，但`pipeline`对这个有帮助。
4. 模型验证更加多样化：你将会在下一个课程中看到`交叉验证`的案例。
## 2、案例
跟前面教程一样，我们将会使用 [Melbourne Housing 数据集](https://www.kaggle.com/dansbecker/melbourne-housing-snapshot/home)
我们不会关注数据加载步骤。假设你已经拥有了`X_train`、`X_valid`、`y_train`和`y_valid`中的训练和验证数据。
我们先使用`head()`方法瞄一眼训练数据。注意这些数据保护分类数据和缺失数据。使用pipelines将会很方便处理这两者。
```python
X_train.head()
```
输出结果：
```
Type Method Regionname Rooms Distance Postcode Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt Lattitude Longtitude Propertycount
12167 u S Southern Metropolitan 1 5.0 3182.0 1.0 1.0 1.0 0.0 NaN 1940.0 -37.85984 144.9867 13240.0
6524 h SA Western Metropolitan 2 8.0 3016.0 2.0 2.0 1.0 193.0 NaN NaN -37.85800 144.9005 6380.0
8413 h S Western Metropolitan 3 12.6 3020.0 3.0 1.0 1.0 555.0 NaN NaN -37.79880 144.8220 3755.0
2919 u SP Northern Metropolitan 3 13.0 3046.0 3.0 1.0 1.0 265.0 NaN 1995.0 -37.70830 144.9158 8870.0
6043 h S Western Metropolitan 3 13.3 3020.0 3.0 1.0 2.0 673.0 673.0 1970.0 -37.76230 144.8272 4217.0
```
我们通过三个步骤来使用pipelines：
**步骤1：定义处理步骤**
与`pipeline`将预处理与建模步骤打包一样，我们使用`ColumnTransformer`类来将不同的步骤打包在一起。下面的代码做了两件事情：
- 填充缺失数据为`数值`类型（用均值等方法填充数值类型数据）
- 用`one-hot`编码来填充`分类`缺失数据
```python
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
# 预处理数值类型数据
numerical_transformer = SimpleImputer(strategy='constant')
# 预处理分类数据
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='most_frequent')),
('onehot', OneHotEncoder(handle_unknown='ignore'))
])
# 将处理步骤打包
preprocessor = ColumnTransformer(
transformers=[
('num', numerical_transformer, numerical_cols),
('cat', categorical_transformer, categorical_cols)
])
```
**步骤2：定义模型**
接下来我们使用熟悉的`RandomForestRegressor`类定义一个`随机森林`模型。
```python
from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=100, random_state=0)
```
**步骤3：创建和评估Pipeline**
最后，我们使用`Pipeline`类来定义一个打包预处理和建模步骤的pipeline。这里有些重点需要注意：
- 使用pipeline，我们预处理训练数据以及拟合模型只有了一行代码。（相反，如果不使用pipeline，我们需要做填充、编码、和模型训练的步骤。如果我们需要同时处理数值和分类变量，这将非常繁琐！）
- 我们在调用`predict()`指令时使用的`X_valid`中包含未经处理的特征值，pipeline会在预测前自动进行预处理。（然而，如果不使用pipeline，在预测前，我们需要记住对验证数据进行预处理）。
```python
from sklearn.metrics import mean_absolute_error
# 在pipeline中打包预处理和建模代码
my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),
('model', model)
])
# 预处理训练数据，拟合模型
my_pipeline.fit(X_train, y_train)
# 预处理验证数据, 获取预测值
preds = my_pipeline.predict(X_valid)
# 评估模型
score = mean_absolute_error(y_valid, preds)
print('MAE:', score)
```
输出结果：
```
MAE: 160679.18917034855
```
## 3、结论
`Pipeline`在机器学习代码清理和规避错误中，尤其是复杂数据预处理的工作流，非常实用。
## 4、去吧，皮卡丘
在接下来的[练习](https://www.kaggle.com/kernels/fork/3370278)中，使用`pipeline`来体验高级数据预处理技术并改善你的预测！
================================================
FILE: docs/Kaggle/learn/intermediate-machine-learning/5.md
================================================
# Kaggle 官方教程：机器学习中级5 交叉验证
> 原文：[Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) > [Cross-Validation](https://www.kaggle.com/alexisbcook/cross-validation)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
在本节课程中，你将会学习如何使用交叉验证来评估模型性能。
## 1、介绍
机器学习是一个迭代的过程。
您将面临以下选择：使用什么预测变量、使用什么类型的模型、向这些模型提供什么参数等等。目前为止，你使用验证数据（或保留数据）评估模型质量来做出这些选择。
但这种方法也有一些缺点。假设你有一个5000行的数据集，通常你会保留大约20%（1000行）的数据作为验证数据。但这再模型评分中会带来随机变化，有时在一组1000行校验数据中表现良好，但在另一个1000行数据集中却不准确。
在极端情况下，你可以想象在验证数据集中只有一行数据。如果你比较不同的模型，那么哪一个模型预测效果最好主要取决于运气！
一般来说，验证数据集越大，评估模型质量中带来的随机性（即“噪音”）越小，越可靠。不幸的是，我们只能从训练数据中取出部分来获取更大的验证数据集，更小的训练数据集意味着更糟糕的模型！
## 2、什么是交叉验证？
在`交叉验证中`，我们使用不同的数据子集来执行建模过程，以获得模型质量的多个度量。
例如，我们可以将数据分成5个部分，每个部分占整个数据集的20%。.在这种情况下，我们将数据分成5个“**folds**”。
然后，我们对每个fold进行一次实验：
- 在**实验1**中，我们使用第一个fold作为验证（保留）数据集，其他fold为训练数据。这给我们一个基于20%保留数据集的模型质量度量。
- 在**实验2**中，我们保留第二个fold的数据（其他fold为训练数据）。这将得到第二个模型质量评估结果。
- 以此类推，使用每个fold作为验证数据集，我们最终得到的模型质量，是基于所有的行数据集(即使我们不同时使用所有行)。
## 3、什么时候使用交叉验证？
交叉验证为模型质量提供了更精确的度量，这在您进行大量建模决策时尤为重要。然而，它可能需要更长的时间来运行，因为它评估了多个模型（每个fold一个）。
所以，在此权衡之下，什么时候使用每种方法呢？
- 对于小数据集，额外的计算并不是什么大事，你应该进行交叉验证。
- 对于较大的数据集，一个验证数据集就够了。你的代码将会执行得更快，有足够的数据就没必要复用数据。
并没有一个明确的界限来区数据集是大还是小，但如果你的模型几分钟或更短时间就能跑完，那么久值得使用交叉验证。
或者，你可以运行交叉验证，看看每个实验的分数是否接近。如果每个实验都产生相同的结果，一个验证集可能就足够了。
## 4、案例
我们将会使用前面课程中相同的数据，加载输入数据`X`并输出数据到`y`。
然后我们定义一个`pipeline`，使用`填充器`来填充缺失数据，以及`随机森林模型`来做预测。
如果不使用 `pipeline`，来交叉验证是非常困难的！使用`pipeline`将会使代码非常简单。
```python
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
my_pipeline = Pipeline(steps=[('preprocessor', SimpleImputer()),
('model', RandomForestRegressor(n_estimators=50,
random_state=0))
])
```
我们使用`cross_val_score() `函数来获取交叉验证评分，通过`cv`参数来设置fold数量。
```python
from sklearn.model_selection import cross_val_score
# 乘以-1，因为sklearn计算得到的是负MAE值（neg_mean_absolute_error）
scores = -1 * cross_val_score(my_pipeline, X, y,
cv=5,
scoring='neg_mean_absolute_error')
print("MAE scores:\n", scores)
```
输出结果：
```
MAE scores:
[301628.7893587 303164.4782723 287298.331666 236061.84754543
260383.45111427]
```
`scoring`参数指定了模型质量的度量方法，我们选择`负的平均绝对误差值`，`scikit-learn`文档列出了[可选值](http://scikit-learn.org/stable/modules/model_evaluation.html)。
指定负MAE有点奇怪。Scikit-learn有一个约定，大的数字意味着更好的效果。在这里使用负号可以使它们与约定保持一致，尽管在其他地方几乎没有听说过。
我们通常需要模型质量的单一度量来比较不同的模型，所以取全部实验的平均值。
```python
print("Average MAE score (across experiments):")
print(scores.mean())
```
输出结果：
```
Average MAE score (across experiments):
277707.3795913405
```
## 5、结论
使用交叉验证能使模型质量更好，同时代码也更加简洁：注意我们不再需要跟踪每个的训练和验证集。尤其是对于小数据集，这是个很大的提升！
## 6、去吧，皮卡丘
把你的新技能运用到[下一个练习](https://www.kaggle.com/kernels/fork/3370281)中~
================================================
FILE: docs/Kaggle/learn/intro-to-machine-learning/1.md
================================================
# Kaggle 官方教程：机器学习入门1 模型是怎样工作的
> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [How Models Work](https://www.kaggle.com/dansbecker/how-models-work)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
## 1、简介
我们将首先概述机器学习模型如何工作以及如何使用它们。如果您以前做过统计建模或机器学习，这可能会让您觉得很基础。别担心，我们很快就会建立强大的模型。
这门微课程将用以下场景为例来构建模型：
你的表哥在房地产投机上赚了几百万美元。由于你对数据科学的兴趣，他愿意成为你的商业伙伴。他会提供资金，你负责提供模型来预测各种房子的价值。
你问表哥他过去是如何预测房产价值的，他说只是凭直觉。但更多的问题表明，他从过去看到的房子中总结出价格模式，并利用这些模式对他当前考虑的新房做出预测。
机器学习也是如此。 我们将从一个叫做决策树的模型开始说起。当然还有更神奇的模型可以提供更准确的预测，但是决策树很容易理解，它们是数据科学中一些最佳模型的基本构件。
为了简单起见，我们将从最简单的决策树开始。
如上图所示，它将房子只分为两类。房子的预测价格是同类房子的历史平均价格。
我们使用数据来决定如何把房子分成两组，然后再确定每组的预测价格。从数据中捕获模式的这一步骤称为`拟合`或`训练模型`。用于`拟合模型`的数据称为`训练数据`。
模型拟合的过程(例如，如何分割数据)比较复杂，我们以后再提。在模型被拟合之后，您可以将其应用于预测新房的价格。
## 2、改进决策树
以下两种决策树中，哪一种更有可能来自于房子训练数据的拟合?
左边的决策树可能更有意义，因为它考虑一个事实：卧室多的房子往往比卧室少的房子售价更高。但这个模型没有考虑到影响房价的其他因素，比如卫生间的数量，地段大小，位置等。
你可以使用更多`“分叉”`的决策树来考虑其他影响因素，即`“更深”`的树。一个决策树也考虑了每栋房子的总占地面积，看起来可能是这样的：
只要沿着满足条件的分支进行选择，您可以通过跟踪决策树来预测任何房子的价格。房价将在决策树的底部点得出，我们将这个点叫做`叶节点`。
叶子处的分支和值将由数据决定，接下来将检查您用到的数据。
## 3、后续
接下来我们将详细讲解怎么检查您的数据。
================================================
FILE: docs/Kaggle/learn/intro-to-machine-learning/2.md
================================================
# Kaggle 官方教程：机器学习入门2 数据探索
> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Basic Data Exploration](https://www.kaggle.com/dansbecker/basic-data-exploration)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
## 1、使用Pandas熟悉数据
任何机器学习项目的第一步都是熟悉数据。你可以使用`Pandas`来实现。`Pandas`是数据科学家用来探索和操作数据的主要工具。大多数人在代码中将`panda`简写为`pd`，使用以下代码将其引用：
```python
import pandas as pd
```
`Pandas`最重要的部分就是`DataFrame`了。`DataFrame`保存了类似表的数据类型，就像Excel中的工作表或SQL数据库中的表。
`Pandas`具有强大的函数来实现大部分你想要的数据操作。
举个例子，我们来看看澳大利亚墨尔本的房价数据。
数据文件路径在`../input/melbourne-housing-snapshot/melb_data.csv`。
我们使用以下命令来加载和查看数据：
```python
# 文件路径
melbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'
# 读取并保存数据到DataFrame类型变量melbourne_data
melbourne_data = pd.read_csv(melbourne_file_path)
# 打印数据概览
melbourne_data.describe()
```
## 2、数据描述详解
如上图所示，结果打印了8个数据。第一个`count`显示有多少个未缺失的数据。缺失值的产生有很多原因。例如，本身只有一间卧室的房子，就不会存在第二间卧室的数据。我们重回数据缺失的主题。
第二个值是`mean`，也就是平均值。`std`是标准偏差，它体现了数据分布情况。
`min`和 `max` 比较好理解，分别是指`最小值`和`最大值`；
`25%, 50%, 75% `是指，我们将数据从小到大排列，返回25%，50%，75%数据量时的数字。
## 3、去吧，皮卡丘
从[这里](https://www.kaggle.com/kernels/fork/1258954)开启你的编程实战吧~
================================================
FILE: docs/Kaggle/learn/intro-to-machine-learning/3.md
================================================
# Kaggle 官方教程：机器学习入门3 你的第一个机器学习模型
> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Your First Machine Learning Model](https://www.kaggle.com/dansbecker/your-first-machine-learning-model)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
## 1、选择建模数据
原始数据集有太多的干扰变量，难以理解，甚至无法很好地打印出来。如何将这些数据处理为比较精简易懂呢？
我们先凭直觉选择几个变量。后面的课程将向你展示使用统计技术来自动优选变量。
选择变量/列前，我们先来看看数据集中有哪些列，使用`DataFrame`的`columns`属性来实现，代码如下：
```python
import pandas as pd
melbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'
melbourne_data = pd.read_csv(melbourne_file_path)
melbourne_data.columns
```
输出结果：
```
Index(['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',
'Date', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car',
'Landsize', 'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude',
'Longtitude', 'Regionname', 'Propertycount'],
dtype='object')
```
- Melbourne 数据集有部分缺失(有些房子没有记录变量。)
- 我们将在后面的教程中学习如何处理缺失值。
- Iowa 数据没有缺失值。
- 现在我们将采用最简单的方法，从数据中删除掉数据缺失的房屋。
- 现在不要太担心这个，代码如下:
```python
# dropna 删除有缺失的数据 (na可以看作是"not available")
melbourne_data = melbourne_data.dropna(axis=0)
```
有许多选择数据子集的方法，[《Pandas微课程》](https://www.kaggle.com/learn/pandas)将更深入地介绍这些内容，但目前我们将重点介绍两种方法。
There are many ways to select a subset of your data. The Pandas Micro-Course covers these in more depth, but we will focus on two approaches for now.
1. `点符号`，用来选择`"预测目标"`；
2. `列数组`, 用来选择`“特性”`。
## 2、选择预测目标
你可以用`点符号`来获取一个变量。这个单列存储在一个系列中，与只有单列数据的`DataFrame`非常类似。
我们将使用点符号来选择要预测的列，这称为预测目标。按照惯例，预测目标我们记为`y`，将房价保存到`y`变量的代码为：
```python
y = melbourne_data.Price
```
## 3、选择特征值
输入到模型中的数据列(稍后用于进行预测)称为“特性”。在我们的例子中，这些数据列是用来确定房价。有时，你将使用除预测目标之外的所有数据列作为特性。但有时候，剔除无效的特征，预测效果会更好。
现在，我们将构建一个只包含几个特性的模型。稍后你将看到如何迭代和比较这些使用不同特性构建的模型。
我们通过在括号内提供列名列表来选择多个特性。列表中的每一项都应该是一个字符串(带引号)。
在数据集中提取多个特征列的代码如下（列名称应该是字符串类型）：
```python
melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']
```
按照惯例，这个数据称为X。
```python
X = melbourne_data[melbourne_features]
```
我们快速地用`describe`方法与`head `方法，来查看数据概览：
```python
X.describe()
```
输出结果：
```python
X.head()
```
输出结果：
使用这些命令可视化地检查数据是数据科学家工作的一个重要部分，你会经常在其中发现值得进一步研究的惊喜。
---
## 4、构建模型
你将使用`scikit-learn`库创建模型。编写代码时，这个库被编写为`sklearn`，你将在示例代码中看到。`Scikit-learn` 很容易将处理存储在`DataFrames`中的数据进行建模，是最流行的库。
建立和使用模型的步骤如下：
`定义`: 它将是什么类型的模型？一个决策树吗？还是其他类型的模型？也指定了模型类型的一些参数。
`拟合: `从提供的数据中捕获模式，这是建模的核心。
`预测: `表面意思，这个就不说了
`评估: `确定模型的预测有多准确。
下面是一个用`scikit-learn`定义一个`决策树模型`，并用`特性`和`目标`变量进行拟合的例子：
```python
from sklearn.tree import DecisionTreeRegressor
# 定义模型. 指定一个参数random_state确保每次运行结果一致
melbourne_model = DecisionTreeRegressor(random_state=1)
# 拟合模型
melbourne_model.fit(X, y)
```
输出结果：
```
DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,
max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1, splitter='best')
```
许多机器学习模型在模型训练中允许一定的随机性。为`random_state`指定一个数字可以确保每次运行得到相同的结果。这被认为是一种很好的做法。你使用任何数字，而模型的质量并不完全取决于你所选择的值。
我们现在有了一个拟合的模型，可以用来进行预测。
在实践中，你会想要预测即将上市的新房子，而不是我们已经有价格的房子。但我们将对训练数据的前几行进行预测，以了解`predict`函数是如何工作的。
```python
print("Making predictions for the following 5 houses:")
print(X.head())
print("The predictions are")
print(melbourne_model.predict(X.head()))
```
输出结果：
```
Making predictions for the following 5 houses:
Rooms Bathroom Landsize Lattitude Longtitude
1 2 1.0 156.0 -37.8079 144.9934
2 3 2.0 134.0 -37.8093 144.9944
4 4 1.0 120.0 -37.8072 144.9941
6 3 2.0 245.0 -37.8024 144.9993
7 2 1.0 256.0 -37.8060 144.9954
The predictions are
[1035000. 1465000. 1600000. 1876000. 1636000.]
```
## 5、去吧，皮卡丘
在[模型构建练习](https://www.kaggle.com/kernels/fork/400771)中自己尝试一下~
================================================
FILE: docs/Kaggle/learn/intro-to-machine-learning/4.md
================================================
# Kaggle 官方教程：机器学习入门4 模型验证
> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Model Validation](https://www.kaggle.com/dansbecker/model-validation)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
你已经建立了一个模型。但是它好不好呢?
在本节课中，你将学习使用模型验证来度量模型的质量。度量模型质量是迭代改进模型的关键。
## 1、什么是模型验证
你将需要评估几乎所有构建的模型。在大多数应用程序中，模型质量的相关度量是预测精度。换言之，模型预测结果是否接近实际发生情况。
`许多人在测量预测精度时犯了一个巨大的错误。他们用训练数据进行预测，并将预测结果与训练数据中的目标值进行比较。`你很快就会发现这个弊端并且学习如何解决它，先让我们想一下该怎么做吧。
你首先需要以一种可理解的方式总结模型质量。如果你比较10000套房子的预测和实际房价，你可能会发现好坏参半。浏览10000个预测值和实际值的数据是没有意义的。我们需要将其总结为一个单一的度量。
总结模型质量有许多度量标准，但我们将从一个称为`平均绝对误差(Mean Absolute Error，也称为MAE)`的度量标准开始。让我们从最后一个单词`error`开始分析这个度量。
每栋房子的预测误差为:
```
error = actual − predicted
```
所以，如果一栋房子实际售价15万美元，而你预测它的售价是10万美元，则误差是5万美元。
利用MAE度量，我们取每个误差的绝对值，这将把每个误差转换为一个正数。然后取绝对误差的平均值。这就是我们对模型质量的评估。也可以这么说：
>我们的预测平均偏离了X。
为了计算MAE，我们首先需要一个模型：
```python
import pandas as pd
# 加载数据
melbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'
melbourne_data = pd.read_csv(melbourne_file_path)
# 过滤缺失数据
filtered_melbourne_data = melbourne_data.dropna(axis=0)
# 选择预测目标和特征
y = filtered_melbourne_data.Price
melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea',
'YearBuilt', 'Lattitude', 'Longtitude']
X = filtered_melbourne_data[melbourne_features]
from sklearn.tree import DecisionTreeRegressor
# 定义模型
melbourne_model = DecisionTreeRegressor()
# 拟合模型
melbourne_model.fit(X, y)
```
我们有了一个模型，下面来计算平均绝对误差：
```python
from sklearn.metrics import mean_absolute_error
predicted_home_prices = melbourne_model.predict(X)
mean_absolute_error(y, predicted_home_prices)
```
输出数据：
```
434.71594577146544
```
## 2、“样本内”分数问题
刚刚计算的测量值可以称为“样本内”分数。我们使用了单个的房屋“样本”来构建模型并对其进行评估。这就是弊端产生原因。
想象一下，在大型房地产市场中，门的颜色与房价无关。
然而，在你用于构建模型的数据样本中，所有带有绿色门的住宅都非常昂贵。该模型的工作是找到预测房价的模式，所以它会看到这种模式，而且总是会将带有绿色门的房子预测为高价。
由于这种模式是从训练数据中推导出来的，所以模型在训练数据中会显得比较准确。
但是，如果模型使用新数据进行预测，这种模式是不成立的，在实际使用中，模型将非常不准确。
由于模型的实用价值体现在对新数据的预测，所以我们需要使用没有参与模型构建的数据，来度量模型性能。
最直接的方法是从模型构建过程中`排除一些数据`，然后使用这些数据来测试模型的准确性。这些数据称为`验证数据`。
## 3、编程实现
`scikit-learn`库有一个函数`train_test_split`，用于将数据分成两部分。我们将使用一部分数据作为训练数据来适应模型，并将使用另一部分数据作为验证数据来计算`mean_absolute_error`。
代码如下：
```python
from sklearn.model_selection import train_test_split
# 将数据分割为训练和验证数据，都有特征和预测目标值
# 分割基于随机数生成器。为random_state参数提供一个数值可以保证每次得到相同的分割
# 执行下面代码
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)
# 定义模型
melbourne_model = DecisionTreeRegressor()
# 拟合模型
melbourne_model.fit(train_X, train_y)
# 根据验证数据获得预测价格
val_predictions = melbourne_model.predict(val_X)
print(mean_absolute_error(val_y, val_predictions))
```
输出数据：
```
260585.51323434475
```
## 3、哇！
样本内数据的平均绝对误差是500美元。样本外价格超过25万美元。
这就是一个几乎完全正确的模型和一个不实用模型之间的区别。实际上，验证数据中的平均房屋价值为110万美元。因此，预测误差约为实际平均房价的四分之一。
有很多方法可以改进这个模型，比如尝试找到更好的特征或使用不同类型的模型。
## 4、去吧，皮卡丘
在我们考虑改进这个模型之前，请自己尝试[模型验证](https://www.kaggle.com/kernels/fork/1259097)。
原文：
https://www.kaggle.com/dansbecker/model-validation
================================================
FILE: docs/Kaggle/learn/intro-to-machine-learning/5.md
================================================
# Kaggle 官方教程：机器学习入门5 欠拟合与过拟合
> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Underfitting and Overfitting](https://www.kaggle.com/dansbecker/underfitting-and-overfitting)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
在这一步的最后，你将了解欠拟合和过拟合的概念，并将能够应用这些概念使你的模型更加准确。
## 1、尝试不同的模型
现在你已经有了一种可靠的方法来度量模型的准确性，你可以使用其他模型进行试验，看看哪个模型的预测效果最好。那么有哪些模型可选择呢?
你可以在`scikit-learn`的文档中看到，决策树模型有许多选项。最重要的选项决定了树的深度。回想一下这门微课程的第一节课，一棵树的深度是它在做出预测之前进行分裂次数的度量。这是一棵相对较浅的树：
在实践中，一棵树的顶层(所有的房子)和一片叶子之间有10个分叉是很常见的。随着树越来越深，数据集被分割成具有更少房子的叶子。
如果一棵树只有一个分叉，它将数据分成两组。如果每组再分裂一次，我们会得到4组房子。再把它们分成8组。如果我们在每一层增加更多的分叉，使群组数量翻倍，到第10层时，我们将有2^10组房子。即1024片叶子。
当我们把房子分成许多片叶子时，每片叶子上的房子也就更少了。叶子上的房子越少，则预测值更接近房子的实际价值。但它们对新数据的预测可能非常不可靠(因为每个预测都只基于少数房子)。
这种现象叫做`过拟合`，模型与训练数据几乎完全匹配，但在验证其他新数据方面效果很差。另一方面，如果我们设计的树很浅，它不会把房子分成很明显的组。
在极端情况下，如果一棵树只有2或4个分支，则各个叶子仍然有各种各样的房子。这就导致预测房价相差甚远，即使是在训练数据中(由于这个原因，验证结果也会很糟糕)。当一个模型不能捕捉到数据中的重要特征和模式时，它在训练数据时就表现得很差，这称为`欠拟合`。
由于我们关心新数据的准确性，根据验证数据估算，我们希望在`欠拟合`和`过拟合`之间找到一个最佳点。在视觉上，我们想要(红色)验证曲线的最低点。
## 2、案例
有几种方法可以控制树的深度，树的一些路径可以比其他路径有更大的深度。但是`max_leaf_nodes`参数提供了一种非常合适的方法来控制`过拟合`和`欠拟合`。我们允许模型生成的叶子越多，在上图中就越接近`过拟合区域`。
我们可以使用一个实用函数来比较不同`max_leaf_nodes`值模型的`MAE分数`：
```python
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(train_X, train_y)
preds_val = model.predict(val_X)
mae = mean_absolute_error(val_y, preds_val)
return(mae)
```
数据被加载进`train_X`, `val_X`, `train_y` 和 `val_y` 变量：
```python
import pandas as pd
# 加载数据
melbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'
melbourne_data = pd.read_csv(melbourne_file_path)
# 过滤缺失数据
filtered_melbourne_data = melbourne_data.dropna(axis=0)
# 选择特征和目标值
y = filtered_melbourne_data.Price
melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea',
'YearBuilt', 'Lattitude', 'Longtitude']
X = filtered_melbourne_data[melbourne_features]
from sklearn.model_selection import train_test_split
# 将数据分割为训练和验证数据，都有特征和预测目标值
train_X, val_X, train_y, val_y = train_test_split(X, y,random_state = 0)
```
我们可以使用`for循环`来比较使用不同`max_leaf_nodes`值构建模型的准确性。
```python
# 比较不同max_leaf_nodes值的MAE
for max_leaf_nodes in [5, 50, 500, 5000]:
my_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)
print("Max leaf nodes: %d \t\t Mean Absolute Error: %d" %(max_leaf_nodes, my_mae))
```
输出数据：
```
Max leaf nodes: 5 Mean Absolute Error: 347380
Max leaf nodes: 50 Mean Absolute Error: 258171
Max leaf nodes: 500 Mean Absolute Error: 243495
Max leaf nodes: 5000 Mean Absolute Error: 254983
```
在列出的选项中，500个是最佳的叶子数。
## 3、结论
结论是，构建模型可能遇到这两种情况：
- `过拟合`： 捕捉那些在未来不会重现的虚假模式，导致预测不那么准确。
- `欠拟合`： 未能捕捉到相关的模式，导致预测不那么准确。
我们使用不参与模型训练的验证数据来度量候选模型的准确性。这让我们可以尝试多种候选模型后，保留最佳模型。
## 4、去吧，皮卡丘
尝试[优化你之前构建的模型](https://www.kaggle.com/kernels/fork/1259126)
================================================
FILE: docs/Kaggle/learn/intro-to-machine-learning/6.md
================================================
# Kaggle 官方教程：机器学习入门6 随机森林
> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Random Forests](https://www.kaggle.com/dansbecker/random-forests)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
## 1、介绍
`决策树`给你留下一个难题。一颗较深、叶子多的树将会`过拟合`，因为每一个预测都来自叶子上仅有的几个历史训练数据。一颗较浅、叶子少的树将会`欠拟合`，因为它不能在原始数据中捕捉到那么多的差异。
即使是当今最精良的建模技术，也面临着拟合不足和拟合过度之间问题。但是，许多模型通过一些不错的点子来提升效果。我们将以`随机森林`为例。
`随机森林`使用了许多树，它通过对每棵成分树的预测进行平均来进行预测。它通常比单个决策树具有更好的预测精度，并且使用默认参数效果也不错。如果继续建模，你可以学习更多具有更好性能的模型，但是其中许多模型效果受调参影响特别大。
## 2、案例
你已经多次看到加载数据的代码。数据加载后，我们将得到以下变量：
- train_X
- val_X
- train_y
- val_y
```python
import pandas as pd
# 加载数据
melbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'
melbourne_data = pd.read_csv(melbourne_file_path)
# 过滤缺失数据
melbourne_data = melbourne_data.dropna(axis=0)
# Choose target and features
y = melbourne_data.Price
melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea',
'YearBuilt', 'Lattitude', 'Longtitude']
X = melbourne_data[melbourne_features]
from sklearn.model_selection import train_test_split
# 将数据分割为训练和验证数据，都有特征和预测目标值
# 分割基于随机数生成器。为random_state参数提供一个数值可以保证每次得到相同的分割
# 执行下面代码
train_X, val_X, train_y, val_y = train_test_split(X, y,random_state = 0)
```
我们构建了一个随机森林模型，类似于我们在scikit-learn中构建决策树的方法——这次使用的是`RandomForestRegressor`类，而不是`DecisionTreeRegressor`。
```python
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomForestRegressor(random_state=1)
forest_model.fit(train_X, train_y)
melb_preds = forest_model.predict(val_X)
print(mean_absolute_error(val_y, melb_preds))
```
输出结果：
```
202888.18157951365
/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.
"10 in version 0.20 to 100 in 0.22.", FutureWarning)
```
## 3、结论
可能还有进一步改进的空间，但是这比最佳决策树250,000的误差有很大的改进。你可以修改一些参数来提升随机森林的性能，就像我们改变单个决策树的最大深度一样。但是，随机森林模型的最佳特性之一是，即使没有这种调优，它们通常也可以正常工作。
你很快就会了解`XGBoost`模型，它在使用正确的参数进行调优时提供了更好的性能(但是需要一些技巧才能获得正确的模型参数)。
## 4、去吧，皮卡丘
尝试自己[使用一个随机森林模型](https://www.kaggle.com/kernels/fork/1259186)，看看它对你的模型有多大的改进。
================================================
FILE: docs/Kaggle/learn/intro-to-machine-learning/7.md
================================================
# Kaggle 官方教程：机器学习入门7 继续你的征程
> 原文：[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) > [Exercise: Machine Learning Competitions](https://www.kaggle.com/kernels/fork/1259198)
>
> 译者：[Leytton](https://github.com/Leytton)
>
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
PS：水平有限，欢迎交流指正（Leytton@126.com）
《Kaggle教程 机器学习入门》系列课程翻译完毕，撒花 ✿✿ヽ(°▽°)ノ✿
## 1、机器学习竞赛
进入机器学习竞赛的世界，不断提高，看看你的进步。
[https://www.kaggle.com/kernels/fork/1259198](https://www.kaggle.com/kernels/fork/1259198)
## 2、继续你的征程
有很多方法可以改进你的模型，此时，`尝试是一个很好的学习方法`。.
改进模型的最佳方法是添加特征。看看这些数据列表，想想什么可能影响房价。缺失值或非数字数据类型将导致错误。
[《中级机器学习》](https://www.kaggle.com/learn/intermediate-machine-learning)微课程将教你如何处理这些类型的特征。你还将学习使用`xgboost`，这是一种比`Random Forest`更精确的技术。
## 3、其他微课程
[《Pandas》](https://kaggle.com/Learn/Pandas) 微课程将为你讲解数据操作技巧，使你能够快速地实现从概念到数据科学项目的实现。
[《深度学习》](https://kaggle.com/Learn/Deep-Learning)微课程中，建立模型在计算机视觉任务中表现得比人类更好。
================================================
FILE: docs/kaggle-quickstart.md
================================================
# [Kaggle](https://www.kaggle.com) 入门操作指南
## [注册](https://www.kaggle.com/?login=true)
1. 首先注册账号
2. 关联 GitHub 账号
## [竞赛 - competitions](https://www.kaggle.com/competitions)
* [选择 - All 和 Getting Started](https://www.kaggle.com/competitions?sortBy=deadline&group=all&page=1&pageSize=20&segment=gettingStarted)
* [选择 - Digit Recognizer（数字识别器）](https://www.kaggle.com/c/digit-recognizer)
* [阅读资料 - Digit Recognizer（数字识别器）](https://www.kaggle.com/c/digit-recognizer)
**选择 版本框架 和 star 最高的 Kernels 编辑就行，然后模仿 [**数字识别**](/competitions/getting-started/digit-recognizer) 案例更新**
## 项目规范(以：DigitRecognizer 为例)
> 文档：结尾文件名为项目名.md
* 案例：`competitions/getting-started/digit-recognizer.md`
* 例如：数字识别，文档是属于 `competitions -> GettingStarted` 下面，所以创建 `competitions/getting-started` 存放文档就行
> 图片：结尾文件名可自由定义
* 案例：`static/images/comprtitions/getting-started/digit-recognizer/front_page.png`
* 例如：数字识别，图片是属于 `competitions -> GettingStarted -> DigitRecognizer` 下面，所以创建 `competitions/getting-started/digit-recognizer` 存放文档的图片就行
> 代码：结尾文件名可自由定义.py
* 案例：`src/py3.x/kaggle/getting-started/digit-recognizer/dr_knn_pandas.py`
* 例如：数字识别，代码只有 `竞赛` 有，所以直接创建 `getting-started/digit-recognizer` 存放代码就行
* 要求（方法：完全解耦）
1. 加载数据
2. 预处理数据(可没)
3. 训练模型
4. 评估模型(可没)
5. 导出数据
* 标注python和编码格式
```python
#!/usr/bin/python
# coding: utf-8
```
* 标注项目的描述
```python
'''
Created on 2017-10-26
Update on 2017-10-26
Author: 【如果是个人】片刻
Team: 【如果是团队】装逼从不退缩（张三、李四 等等）
Github: https://github.com/apachecn/kaggle
'''
```
> 数据：结尾文件名可自由定义
* 输入：`datasets/getting-started/digit-recognizer/input/train.csv`
* 输出：`datasets/getting-started/digit-recognizer/ouput/Result_sklearn_knn.csv`
* 例如：数字识别，数据只有 `竞赛` 有，所以直接创建 `getting-started/digit-recognizer` 存放数据就行
> 结果提交
将数据的输出结果提交到项目的页面中
## docs目录（可忽略）
`docs 目录存放的是 ApacheCN 整理的操作or说明文档，和 kaggle 网站内容没有关系`
**后面会持续更新**
================================================
FILE: docs/kaggle-start.md
================================================
# kaggle 组队开始啦
## 1、前言
我们学习 ML 的知识已经有一段时间了。不知道大家的 ML 技能还有实际问题的处理能力是不是一天一天在增长，也不知道大家是否找到了一个适合自己突破的学习方式。正所谓，学如逆水行舟，不进则退。既然我们希望增长得更快，况且我们身边有这么多的资源（不利用岂不是可惜），这样一想，这不是正好让我们展示一下自己的技术（是时候表演真正的技术了）？
装逼的江湖里可能不会出现李淳罡，但是谁能保证你不是下一个徐凤年呢？
于你而言，阿里天池、京东金融大赛、DF、CCF、kaggle 比赛，这不就是一个个江湖吗？
难道你就不想一声 “剑来！” 令那数千飞剑遮天蔽日？难道你就不想为那一袭红衣剑开天门？难道你就不想 “天上剑仙三百万，遇我也须尽低眉” ？还有比这更装逼的事情？
## 2、正文
考虑到大家学习完成 ML 的基础知识之后，可能会苦于一身本事无处施展，更准确来说，是学完之后没有练手的机会。所以，ApacheCN 准备开始组织大家一起刷 kaggle 啦啦啦~~~
### 2.1、运筹帷幄
[片刻](https://github.com/jiangzhonglian) 大佬已经在 github 中写了一个简单的小例子 ---> [数字识别](https://github.com/apachecn/kaggle/blob/master/competitions/getting-started/digit-recognizer.md)，供大家来参考。
近期因为涉及到 ApacheCN 的组织架构的调整，还有 sklearn 0.19.X 项目的进行等外在因素，可能片刻大佬没有时间和大家一起，我们对这次活动进行了一个简单的小规划，请看下面~~~
* 初期规划 1-3 个人一队，当然也可以调整，小队人数方面比较自由（为你们可以在群里尽情的搞基提供保障）
* 建议先从简单的小比赛入手，比如 [手写数字识别](https://www.kaggle.com/c/digit-recognizer) 、 [预测房价](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) 、 [泰坦尼克](https://www.kaggle.com/c/titanic)
* 初期规划每个小比赛 1-2 周每个小比赛之后有一次 ApacheCN 各参赛小队排名，根据 leaderboard 上面的最高成绩排名
* 排名前 3 的小队给大家做一下直播，讲解一下获得好成绩的心路历程
* 提交到 github 上，接受来自大家的膜拜~~~
### 2.2、初入江湖
在正式开始之前，你需要了解一下相关的背景知识
* github 地址： https://github.com/apachecn/kaggle
* 阅读一下 [README.md](https://github.com/apachecn/kaggle) 文档，了解一下套路
* 阅读一下 docs 目录下的文档（ [github 操作](https://github.com/apachecn/kaggle/blob/master/docs/GitHub/README.md) 和 [kaggle 操作](https://github.com/apachecn/kaggle/blob/master/docs/kaggle-quickstart.md)），熟悉一下具体的操作
### 2.3、小有名气
了解完一些相关的小套路之后，为自己定一下位。
* 如果你已经是大佬了，那就不需要从头开始了，直接找与你水平相近的人开始组队吧，直接上有难度的比赛；
* 如果你和我一样是个萌新，那就找一些和你一样的或水平比你高而且愿意带你的人组队，开始征战之旅吧！
少侠，无论做啥最幸运的就是碰壁或遇到挫折，跌宕起伏才是人生，如果一帆风顺，这样的人生我宁愿不要！
最可怕的是不敢，连自己这一关都过不去，你还想成为大佬？别做梦了！大不了重新来过，因为......你还年轻。年少人做年少事，有担当之事等到而立之年再去做。
如果不知道从哪儿开始，那就从 [手写数字识别](https://www.kaggle.com/c/digit-recognizer) 开始吧。
### 2.4、名动一方
组队，并不意味着你变得轻松了，相反，你可能会变得更累，事情更多。还有千万不要把自己想成一个可有可无的人，这样的想法真的很让人害怕。在一个小队里面，问题的解决就靠大家的相互交流，加倍信任，好成绩的获得，一定离不开超级棒的团队合作。
* 组队之后，各小组可以私下里建个讨论组一起讨论问题，但是更建议大家直接在群里提问，群里解答，毕竟群里卧虎藏龙，我能说，咱们群里有好几个大佬吗？他们玩比赛拿奖都拿到手软，但是我不能透露他的名字~~~
* 在群里，大家尽情头脑风暴，也正是因为这样的跳跃思维，才会出现更加好的问题解决方式，才会出现 leak 般的答案。
* 当然，组队完成之后，在群里你可能会发现某个人，或者某几个人在做比赛的时候思维炒鸡活跃，得到的结果也相当 Nice ，那还等什么，这是大腿啊！！！还不赶紧抱紧了。
### 2.5、天下闻名
等到每个小比赛结束之后，大家把自己相应的代码提交到 [github](https://github.com/apachecn/kaggle) 中，以小队的名称命名文件夹，接收来自世界各地的膜拜吧。唔哈哈哈哈哈哈哈哈。。。当然如果小队人员不愿意将自己的核心代码开源出来，我们也会尊重你们小队的意见~~~
* 排名前 3 的小队，选出负责人或者团队成员一起，对如何获得这样一个令（rang）人（ren）害（jiao）怕（ao）的成绩做一下总结，这才是我们想要的干货（就是这么傲娇）。当然最好的结果是让其他人能够从你们的经历中学到一些经验，比如：数据预处理需要注意的地方，建模完成后怎么进行验证等等。
* 我们尽量是以 **文档+代码+视频** 的形式或其中之一的形式开源出来，不因为别的，只因为我们是 ApacheCN 。
* 每个参加比赛的小队都有积分可以拿，但是积分多少需要看成绩来定啦，^_^，成绩越好，积分越高啊~~~
### 2.6、一代宗师
就这样驰骋在 kaggle 的沙场上，只要你没有被比赛压垮，你就能把比赛压垮。
咱们 ApacheCN 打算用 kaggle 上的中小型比赛让大家练手，但是手热了，我们该怎么办呢？咱们还有下面的几个选择：
* 继续挑战 kaggle 上的高难度比赛，一天不拿到 kaggle 的 Grandmaster 称号，一天就不告老还乡。
* 转战国内的比赛 ===> 天池/京东/CCF/challenger.ai ，沙场真的是应有尽有，就看你这千里马中意哪片草原了。
到时候，咱们 ApacheCN 内部会进行一次大讨论，定一下未来的走向。
### 2.7、超凡入圣
廉颇老矣，尚能饭否？
各大比赛你心里基本都有一点自己的认知了，自己也有了很大的成长了，自己的技术达到什么水平你心里也是门清儿，所以....你要停下来思考清楚自己接下来的路怎么走了！
举个栗子：
* 喜欢各种算法/建模 ===> 算法工程师/数据科学家/自动驾驶工程师
* 喜欢数据的处理：清洗/沉淀/分析等 ===> 数据开发工程师/大数据开发工程师
* 不喜欢数据方面的流程，还是喜欢功能模块开发 ===> web工程师
无论怎么样，到这个阶段，你已经知道自己接下来的路该怎么走了，不是吗？
### 2.8、天外飞仙
大佬，以后的路就只能靠你们自己走咯，哈哈哈哈~
还请萌新在咨询你们如何入门的时候，为他们指明一条可以走下去的路...
## 3、最后的寄语
我的心中是一直有一个江湖的……那里的山，云雾缭绕；那里的水，波光粼粼；那里的将士，持三尺剑立不世功；那里的侠客，十步杀一人千里不留行；那里的姑娘，美得万人空巷。那里有快马厮杀的豪气，也有一剑斩六合的孤胆英雄，只因那里的名字叫江湖。
江湖很美，一人一马就能踏遍满山桃花，有不世武功便可仗剑天涯，侠士们都是剑眉星目，姑娘们都烨若桃花。
江湖卧虎藏龙，你走进一家客栈，匾额上书两字：月来！
乍一看，不得了啊，这客栈里都不是一般人哇，你看，那坐正中间的大汉，身长九尺，髯长二尺，不怒自威；你再看那边另一位兄台，豹头环眼，燕颔虎须，势如奔马。你可能会感叹，长相便如此磅礴，服气！大哥，干了这碗酒，都在酒里了。别着急喝呀，这些可都是小喽啰，厉害的人，可在后头呢！你看那窗边，默默沏茶的黑衣男子，是不是像一棵不倒的青松？你看那炉边轻若芙蕖的酒女，是否袖中偶尔有寒光闪过？你看那低头哈腰的店小二，为何行走时听不到丝毫脚步声？
江湖时而精彩，却又偶现残缺。那位公子远赴边城，姑娘十八里相送，为什么眼睛红了呢？平日里威风凛凛的刀客，为何神色寂寥地坐在窗边？是否在想错手杀死的那个姑娘？宫斗剧里风姿绰约的姑娘们，都在为了皇帝的青睐和后位的宝座勾心斗角，左右使绊子。青春剧里的男生女生，不知又包了几座鱼塘，看了几夜流星。美剧里的时而颓废时而振作的男主女主们，不知又拯救了多少次世界。
但是江湖，还是那样，侠客们大口吃肉大碗喝酒，饮尽了一夜的明月，散尽了一宿的恩仇。那里的姑娘，身骑白马走三关，眼睛始终清澈得像泸沽湖的水，神采飞扬像三月的桃花……那里没有利欲熏心，绝不会背信弃义。话不投机，我就不屑言语，遇到知己，就倾杯相交，他们始终保持着一身风骨，不负权威，不畏权贵，不贪权力。你若是诧异，他们就飒然一笑，说，小爷我可是江湖人！江湖人自有江湖人的风骨，江湖自有江湖的不凡，江湖包罗万象又精彩纷呈，任你贪癫痴怨又六根不净，这江湖，都欢迎你。罢了，看你如此向往，少侠我便陪你喝一杯，跟你讲讲这人生到底该怎么过才爽利！
干了这碗酒，你我都是江湖人！
最后附上我超级喜欢的一句话：
* 李淳罡愿世间心诚剑士人人会两袖青蛇。
* 李淳罡愿天下惊艳后辈人人可剑开天门。
================================================
FILE: docs/writeup-list.md
================================================
# 解决方案列表
## 结构化数据/时间序列
1. 2018科大讯飞AI营销算法大赛
Rank1：https://zhuanlan.zhihu.com/p/47807544
2. 2018 IJCAI 阿里妈妈搜索广告转化预测
Rank1：https://github.com/plantsgo/ijcai-2018
Rank2：
+ https://github.com/YouChouNoBB/ijcai-18-top2-single-mole-solution
+ https://blog.csdn.net/Bryan__/article/details/80600189
Rank3: https://github.com/luoda888/2018-IJCAI-top3
Rank8: https://github.com/fanfanda/ijcai_2018
Rank8: https://github.com/Gene20/IJCAI-18
Rank9（第一赛季）https://github.com/yuxiaowww/IJCAI-18-TIANCHI
Rank29: https://github.com/bettenW/IJCAI18_Tianchi_Rank29
Rank41: https://github.com/cmlaughing/IJCAI-18
Rank48: https://github.com/YunaQiu/IJCAI-18alimama
Rank53: https://github.com/altmanWang/IJCAI-18-CVR
Rank60: https://github.com/Chenyaorui/ijcai_2018
Rank81: https://github.com/wzp123456/IJCAI_18
Rank94: https://github.com/Yangtze121/-IJCAI-18-
3. 2018腾讯广告算法大赛
Rank3: https://github.com/DiligentPanda/Tencent_Ads_Algo_2018
rank6: https://github.com/nzc/tencent-contest
Rank7: https://github.com/guoday/Tencent2018_Lookalike_Rank7th
Rank9: https://github.com/ouwenjie03/tencent-ad-game
Rank10: https://github.com/keyunluo/Tencent2018_Lookalike_Rank10th
rank10（初赛）: https://github.com/ShawnyXiao/2018-Tencent-Lookalike
Rank11:
+ https://github.com/liupengsay/2018-Tencent-social-advertising-algorithm-contest
+ https://my.oschina.net/xtzggbmkk/blog/1865680
Rank26: https://github.com/zsyandjyhouse/TencentAD_contest
Rank33: https://github.com/John-Yao/Tencent_Social_Ads2018
Rank69: https://github.com/BladeCoda/Tencent2018_Final_Phrase_Presto
4. 2018JDATA 用户购买时间预测
Rank9：https://zhuanlan.zhihu.com/p/45141799
5. 2018 DF风机叶片开裂预警
Rank2：https://github.com/SY575/DF-Early-warning-of-the-wind-power-system
6. 2018 DF光伏发电量预测
Rank1：https://zhuanlan.zhihu.com/p/44755488?utm_source=qq&utm_medium=social&utm_oi=623925402599559168
https://mp.weixin.qq.com/s/Yix0xVp2SiqaAcuS6Q049g
7. AI全球挑战者大赛-违约用户风险预测
Rank1：https://github.com/chenkkkk/User-loan-risk-prediction
8. 2016融360-用户贷款风险预测
Rank7：https://github.com/hczheng/Rong360
9. 2016 CCF-020优惠券使用预测
Rank1: https://github.com/wepe/O2O-Coupon-Usage-Forecast
10. 2016 ccf-农产品价格预测
Rank2: https://github.com/xing89qs/CCF_Product
Rank35: https://github.com/wqlin/ccf-price-prediction
11. 2016 ccf-客户用电异常
Rank4: https://github.com/AbnerYang/2016CCF-StateGrid
12. 2016 ccf-搜狗的用户画像比赛
Rank1: https://github.com/hengchao0248/ccf2016_sougou
Rank3: https://github.com/AbnerYang/2016CCF-SouGou
Rank5:
+ https://github.com/dhdsjy/2016_CCFsougou
+ https://github.com/dhdsjy/2016_CCFsougou2
+ https://github.com/prozhuchen/2016CCF-sougou
+ https://github.com/coderSkyChen/2016CCF_BDCI_Sougou
13. 2016 ccf-联通的用户轨迹
RankX: https://github.com/xuguanggen/2016CCF-unicom
14. 2016 ccf-Human or Robots
Rank6: https://github.com/pickou/ccf_human_or_robot
15. 菜鸟-需求预测与分仓规划
Rank6: https://github.com/wepe/CaiNiao-DemandForecast-StoragePlaning
Rank10: https://github.com/xing89qs/TianChi_CaiNiao_Season2
## NLP
1. 智能客服问题相似度算法设计——第三届魔镜杯大赛
rank6 https://github.com/qrfaction/paipaidai
rank12
+ https://www.jianshu.com/p/827dd447daf9
+ https://github.com/LittletreeZou/Question-Pairs-Matching
## CV
1. Kaggle-TGS
Rank1: http://t.cn/EzkDlOC
Rank9: http://t.cn/EznzvYv
Rank11:https://github.com/iasawseen/Kaggle-TGS-salt-solution
Rank14:https://github.com/lRomul/argus-tgs-salt
Rank15:https://github.com/adam9500370/Kaggle-TGS
Rank22: http://t.cn/EzYkR6i
Rank56 https://github.com/Gary-Deeplearning/TGS-Salt
2. Kaggle Google地标检索
Rank1: http://t.cn/R1i7Xiy
Rank14： http://t.cn/R1nQriY
3. Lyft感知挑战赛
赛题： http://t.cn/RBtrJcE
Rank4： http://t.cn/RBtrMdw
http://t.cn/RBJnlug
4. (Kaggle)CVPR 2018 WAD视频分割
Rank2: http://t.cn/Ehp4Ggm
5. Kaggle Google AI Open Images
Rank15: http://t.cn/RF1jnis
6. Quick, Draw! Kaggle Competition Starter Pack
http://t.cn/EZAoZDM
7. Kaggle植物幼苗图像分类挑战赛
Rank1: http://t.cn/RBssjf6
8. Kaggle Airbus Ship Detection Challenge (Kaggle卫星图像船舶检测比赛)
Rank8: https://github.com/SeuTao/Kaggle_Airbus2018_8th_code
Rank21:https://github.com/pascal1129/kaggle_airbus_ship_detection
9. kaggle RSNA Pneumonia Detection
Rank1:https://github.com/i-pan/kaggle-rsna18
Rank2:https://github.com/SeuTao/Kaggle_TGS2018_4th_solution
Rank3:https://github.com/pmcheng/rsna-pneumonia
Rank6:https://github.com/pfnet-research/pfneumonia
Rank10:https://github.com/alessonscap/rsna-challenge-2018
10. kaggle Carvana Image Masking Challenge
Rank1:https://github.com/asanakoy/kaggle_carvana_segmentation
Rank3:https://github.com/lyakaap/Kaggle-Carvana-3rd-place-solution
11. kaggle Statoil/C-CORE Iceberg Classifier Challenge
Rank4: https://github.com/asydorchuk/kaggle/blob/master/statoil/README.md
12. kaggle 2018 Data Science Bowl
Rank1: https://github.com/selimsef/dsb2018_topcoders
Rank2: https://github.com/jacobkie/2018DSB
Rank3: https://github.com/Lopezurrutia/DSB_2018
Rank4: https://github.com/pdima/kaggle_2018_data_science_bowl_solution
Rank5: https://github.com/mirzaevinom/data_science_bowl_2018
## 经验文章
1. 介绍featexp 一个帮助理解特征的工具包 http://www.sohu.com/a/273552971_129720
2. Ask Me Anything session with a Kaggle Grandmaster Vladimir I. Iglovikov
PDF：https://pan.baidu.com/s/1XkFwko_YrI5TfjjIai7ONQ
## 其它资源列表
1. 数据比赛资讯：https://github.com/iphysresearch/DataSciComp
2. Kaggle top方案整理：https://github.com/EliotAndres/kaggle-past-solutions
3. [Data competition Top Solution 数据竞赛Top解决方案开源整理](https://github.com/Smilexuhc/Data-Competition-TopSolution)
## 大佬的Git
1. 植物 ：https://github.com/plantsgo
2. wepon ：https://github.com/wepe
3. Snake：https://github.com/luoda888
4. Drop-out：https://github.com/drop-out
5. 金老师的知乎：https://zhuanlan.zhihu.com/jlbookworm
6. 渣大：https://github.com/nzc
7. 郭大：https://github.com/guoday
================================================
FILE: docs/矩阵求导.md
================================================
# 矩阵求导公式
================================================
FILE: docs/面试求职/学历.md
================================================
# 【求职系列】人工智能学历真的重要吗？
## 学历：奇怪现象？
* 如果你是跨专业的学习？进入AI行业，别人会觉得你很励志很牛逼！
* 但是如果你是低学历的人，企业一般认为你就是个垃圾（普遍而已）
表现形式：
1. 学历门槛（当然大家都有自己的理由，说降低面试成本 - 就是懒嘛！）
2. 风险转嫁（如果你是高学历，即使被裁也就是看走眼；而学历低，就是眼界有问题）
除非你做的最前沿的技术！
* 因为最前沿，学历高的也没几个会。。你还得去教别人
* 最后高学历的学会了，差不多也是该辞退你的时候，因为公司说你是没门面的事情。
当然你还会问，那极个别的牛人成功了为什么社会没变化呢？
* 因为人家当老板了，可能忘记了自己当初那么苦。
* 毕竟他有那么多事情要做，干嘛非要帮助当初的自己呢？？？
## 面试： 奇异现象？
* 毕业季拼命的 刷题（leetcode、剑指 Offer）
* 更有甚者，工作n年以后，面试还是刷题，刷题
我不理解，持续刷题的意义是什么？
```
请问你工作用让你写这些东西吗？【没有，性能问题：数据库、分布式、GPU帮你解决】
刷题为了获取精神上的愉悦？ 【考察你的逻辑思维？狗屁】
其实不然，这东西基本上不用都会忘记，这就是为什么每一次都要复习，背写！
我在5年的工作中，从大数据、算法策略、NLP，我都没有涉及到leetcode的题目
当然除非你重构一些框架，底层需要使用到一些复杂的数据结构【极少人】
很多人从大学毕业就是这样过来的，所以他以为面试就这样，所以这样面试别人【沃日】
```
现在算法面试不管你什么谁，都要上来做个题目，我真想草你们这些人，真尼玛闲的蛋疼！
* 你说一个应届生没啥社会经验，你考考，我理解
* 但是人家有项目，有比赛经验，你还考数据结构，为什么这么闲呢？？
* 我就想问你们，你们招聘人不就是使用开源框架做事情，难道还手写自己框架【沃日】
## 公司：奇异现象？
* 悲哀的是：
* 不管毕业多久，很多人炫耀的资本还是学校
* 而更悲哀的是：
* 这些人也都习惯性的默认了这一点并继续炫耀
…… 社会现实到有些过时呢
## 至少是个本科，是什么意思？
* 首先，学历真的很重要。。。（至少是个本科）
* 但是这个至少是个神马意思呢？？？
就是说：
* 如果一直没招到合适的研究生和博士，就勉为其难的试试本科，然后压低工资。。
什么意思呢？
* 就是说：你能力要强，工资还要低！（甚至可能还要给某些研究生or博士汇报工作）
## 那么如果是专科呢，能学吗？
专科当然也是有机会的，不过基本上即使技术过了也不会招聘你的！
为什么？
* 因为正常情况下大公司不需要学历低的人，无所谓你的能力。【即使叫你去面试】
* 但是大公司的创业部门和中小型公司创业公司是需要有能力的人，所以机会还是有的。。
那么我们该怎么办呢？
调整好自己的心态，别总是觉得要去大公司什么的。
很多从大公司出来的boss 创业都是直接带一个团队出来，要么就是从社招招聘。
基本上也都是普通的大学生，只是公司大了就开始矫情，所以摆正好心态，开心学习就行
记住一点：
* 工资高
* 圈子：例如 ApacheCN
* 自学能力强、有自己的想法（知道自己喜欢什么、为什么学他就行）
---
## 最近有趣的新闻
> 最近关于学历的残酷事实
[AI泡沫崩了？抖音员工爆料：校招内推985本硕简历过不了！](https://mp.weixin.qq.com/s?__biz=MzUzODMxMTI5MA==&mid=2247485175&idx=1&sn=d5f2359685b02cf33594eceae1cf52ba&chksm=fad8e9e2cdaf60f4382b1dff40ab570f68d7b64f63f86a7d92701aaf2f26a9d6f50ce9d22916)
本事件只是反映了一个问题，就是人已经过饱和。【饱和只是入门，高手依然很少】
> 在国内，为什么我们看到的大佬都是高学历的？
话说：低学历别人也不会给你机会进入这个圈子？
例如：
* 求职这一块，求职各种刁难【高压力、低薪资】
* 在社交圈这一块，基本上是无法进入他们的圈子【为什么大佬都要花钱进MBA什么圈】
* 在融资这一块，你没学历背景基本上也融不到资
* 学历这一块，是一个相互交流的点，【例如：你也是某某某学校的？我是你学弟！】
* 国内人口流量过大，导致对人要求过于严格，甚至高于全球水平，甚至排名最高几名
> 今日头条张一鸣炮轰公司HR: 按你们写的JD招聘，我自己都进不来公司
现实和理想的差距 VS 老板的要求和HR行动
> 互联网-大公司人员的传播路径
现在我们看到的现象是：看学历、看背景
其实才是：个人能力、个人品质（其中还有各种小套路）
头条CEO张一鸣虽然说了那些话，但其实改变是非常难的，因为公司已经被某些人腐蚀了！
要改变就要大动干戈，掉血掉肉；要么就新建一个创业公司，重新找人，定义好文化规范。
解决方案：
1. 其他的国家（相比而言：国外急缺这方面的人才）
2. 提升自己的能力，但是短期是不可能实现的（所以这个建议是个废话，但是我想告诉有些人这是个现实问题）
3. 选择换个方向，从算法转开发，说实话开发薪资并不低，并且你可以调用算法的接口，只是你叫开发工程师（别人叫算法工程师而已）【就好比找对象一样，1000个人（厉害不厉害都有）追一个妹子，所以作为一个男孩子而言，完全可以找一个其他的女孩，没必要在这个树上吊死。也不见得没有比她更好的，所以人得灵活一点】
算法只是一种思想，我们学它，可以从事它的研究，也可以从事它的应用。
一定别把自己搞死在这个地方就行。
所以？你准备好了入坑吗？
================================================
FILE: docs/面试求职/简历.md
================================================
# 简历如何写？
> 个人信息
姓名、性别、学历、工龄、籍贯、现居地、电话、电子邮箱
> 求职意向
* 工作情况: 北京-全职
* 期望职业: 数据/算法负责人
* 期望月薪: 面议
> 教育经历
* 20xx.09-20xx.06 xxxxx大学 xxx
> 技能描述
* 使用Python 6年、熟练使用 SQL
* 熟悉主流算法: 分类、回归
> 自我评价
1. 工作踏实认真，负责过Kaggle比赛项目、企业反作弊、推荐和大数据项目
2. 学习能力很强，热衷研究技术、经常分享交流技术、喜欢看Kaggle全球优秀kernels
3. 人脉资源广泛，在GitHub和知乎有点小名气，坚持做了2年的机器学习的开源社区
> 个人经历
```
xx 2018.09～现在 算法负责人 负责：负责AI算法实现和落地应用
xx 2016.06～2018-08 社区负责人 负责： 负责整个AI社区的内容梳理
xx 2014.08～2016.03 算法策略 负责：广告联盟反作弊算法策略
xx 2013.11～2014.07 数据仓库 负责：数据仓库优化和可视化展现
```
> 项目经验
* 工作时间：2018/09～现在
* 所处职位：数据算法负责人
* 日常工作
* 1/2/3/4 [聊工作相关的重点]
> 自学经验
* Pytorch + TensorFlow (2017/06 ~ 现在)
* 搜索：TensorFlow 2.0 - 教程: https://github.com/apachecn/AiLearning
* Pytorch 没有是因为翻译了教程: https://github.com/apachecn/pytorch-doc-zh
* 各种做过的开源项目
================================================
FILE: docs/面试求职/简历范文.md
================================================
# 简历范文
## 自我评价模块
## 技能模块
## 项目模块
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# Images for the whole repository!
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language: node_js # 构建所需的语言环境
node_js:
- "v10.16.0" # 对应的版本
branches:
only:
- master # 构建的分支
cache:
directories:
- node_modules # 依赖缓存的目录
install:
- npm install -g gitbook-cli # 安装编译工具
- gitbook fetch 3.2.3 # 安装 Gitbook 子版本
script:
- sh run_website.sh
after_script:
- cd _book
- git init
- git config user.name ${GH_UN}
- git config user.email ${GH_EMAIL}
- git add -A
- git commit -am "$(date "+%Y-%m-%d %H:%M:%S")"
- git push "https://${GH_TOKEN}@github.com/${GH_USER}/${GH_REPO}.git" master:${GH_BRANCH} -f
env:
global:
- GH_UN=jiangzhonglian
- GH_EMAIL=jiang-s@163.com
- GH_USER=apachecn
- GH_REPO=Interview
- GH_BRANCH=gh-pages
================================================
FILE: old/book.json
================================================
{
"title" : "Interview 求职指南",
"author" : "ApacheCN",
"description" : "Interview 求职指南",
"language" : "zh-hans",
"plugins": [
"github",
"github-buttons",
"-sharing",
"insert-logo",
"sharing-plus",
"back-to-top-button",
"code",
"copy-code-button",
"pageview-count",
"edit-link",
"emphasize",
"alerts",
"auto-scroll-table",
"popup",
"hide-element",
"page-toc-button",
"tbfed-pagefooter",
"sitemap",
"advanced-emoji",
"expandable-chapters",
"splitter",
"search-pro"
],
"pluginsConfig": {
"github": {
"url": "https://github.com/apachecn/Interview"
},
"github-buttons": {
"buttons": [
{
"user": "apachecn",
"repo": "Interview",
"type": "star",
"count": true,
"size": "small"
}
]
},
"insert-logo": {
"url": "http://data.apachecn.org/img/logo.jpg",
"style": "background: none; max-height: 150px; min-height: 150px"
},
"hide-element": {
"elements": [".gitbook-link"]
},
"edit-link": {
"base": "https://github.com/apachecn/Interview/blob/master",
"label": "编辑本页"
},
"sharing": {
"qzone": true,
"weibo": true,
"twitter": false,
"facebook": false,
"google": false,
"qq": false,
"line": false,
"whatsapp": false,
"douban": false,
"all": [
"qq", "douban", "facebook", "google", "linkedin", "twitter", "weibo", "whatsapp"
]
},
"page-toc-button": {
"maxTocDepth": 4,
"minTocSize": 4
},
"tbfed-pagefooter": {
"copyright":"Copyright © ibooker.org.cn 2019",
"modify_label": "该文件修订时间： ",
"modify_format": "YYYY-MM-DD HH:mm:ss"
},
"sitemap": {
"hostname": "http://pytorch.apachecn.org"
}
},
"my_links" : {
"sidebar" : {
"Home" : "https://www.baidu.com"
}
},
"my_plugins": [
"donate",
"todo",
"-lunr",
"-search",
"expandable-chapters-small",
"chapter-fold",
"expandable-chapters",
"expandable-chapters-small",
"back-to-top-button",
"ga",
"baidu",
"sitemap",
"tbfed-pagefooter",
"advanced-emoji",
"sectionx",
"page-treeview",
"simple-page-toc",
"ancre-navigation",
"theme-apachecn@git+https://github.com/apachecn/theme-apachecn#HEAD",
"pagefooter-apachecn@git+https://github.com/apachecn/gitbook-plugin-pagefooter-apachecn#HEAD"
],
"my_pluginsConfig": {
"github-buttons": {
"buttons": [
{
"user": "apachecn",
"repo": "Interview",
"type": "star",
"count": true,
"size": "small"
},
{
"user": "apachecn",
"width": "160",
"type": "follow",
"count": true,
"size": "small"
}
]
},
"ignores": ["node_modules"],
"simple-page-toc": {
"maxDepth": 3,
"skipFirstH1": true
},
"page-toc-button": {
"maxTocDepth": 2,
"minTocSize": 2
},
"page-treeview": {
"copyright": "Copyright © aleen42",
"minHeaderCount": "2",
"minHeaderDeep": "2"
},
"donate": {
"wechat": "微信收款的二维码URL",
"alipay": "支付宝收款的二维码URL",
"title": "",
"button": "赏",
"alipayText": "支付宝打赏",
"wechatText": "微信打赏"
},
"page-copyright": {
"description": "modified at",
"signature": "你的签名",
"wisdom": "Designer, Frontend Developer & overall web enthusiast",
"format": "YYYY-MM-dd hh:mm:ss",
"copyright": "Copyright © 你的名字",
"timeColor": "#666",
"copyrightColor": "#666",
"utcOffset": "8",
"style": "normal",
"noPowered": false
},
"ga": {
"token": "UA-127082511-1"
},
"baidu": {
"token": "84fca651656bc67b4b2d56605b6d0852"
},
"pagefooter-apachecn": {
"copyright":"Copyright © ibooker.org.cn 2019",
"modify_label": "该文件修订时间： ",
"modify_format": "YYYY-MM-DD HH:mm:ss"
}
}
}
================================================
FILE: old/config
================================================
[http]
postBuffer = 524288000
================================================
FILE: old/requirements.txt
================================================
torch
keras
numpy
pandas
sklearn
================================================
FILE: old/run_website.sh
================================================
#!/bin/bash
loginfo() { echo "[INFO] $@"; }
logerror() { echo "[ERROR] $@" 1>&2; }
python3 src/script.py "home" "book"
rm -rf node_modules/gitbook-plugin-tbfed-pagefooter
gitbook install
python3 src/script.py "home" "powered"
python3 src/script.py "home" "gitalk"
gitbook build ./ _book
# python3 src/script.py "home" "index"
versions="Algorithm/Leetcode/Python Algorithm/Leetcode/JavaScript"
for version in $versions;do
loginfo "==========================================================="
loginfo "开始", ${version}, "版本编译"
echo "cp book.json docs/${version}"
cp book.json docs/${version}
# 替换 book.json 的编辑地址
echo "python3 src/script.py ${version} book"
python3 src/script.py ${version} "book"
echo "cp -r node_modules docs/${version}"
rm -rf docs/${version}/node_modules
cp -r node_modules docs/${version}
echo "gitbook install docs/${version}"
gitbook install docs/${version}
echo "python3 src/script.py ${version} powered"
python3 src/script.py ${version} "powered"
echo "python3 src/script.py ${version} gitalk"
python3 src/script.py ${version} "gitalk"
echo "gitbook build docs/${version} _book/docs/${version}"
gitbook build docs/${version} _book/docs/${version}
# 注释多余的内容
# echo "python3 src/script.py ${version} index"
# python3 src/script.py ${version} "index"
done
# rm -rf /opt/apache-tomcat-9.0.17/webapps/test_book
# cp -r _book /opt/apache-tomcat-9.0.17/webapps/test_book
================================================
FILE: src/config_nowcoder.json
================================================
{
"name": "牛客前端岗面试求职真题解析 2021~2022",
"url": "https://www.nowcoder.com/tutorial/10091/fdde7d829d5c451d9b38a2ff042a6a28",
"link": ".sub-menu-tit>h3, .sub-menu-items dd a",
"title": ".study-tit>h2",
"content": "article",
"remove": "",
"credit": true,
"optiMode": "quant",
"headers": {
"Cookie": ""
}
}
================================================
FILE: src/do_dir_structure.py
================================================
#!/usr/bin/python3
# coding: utf8
import os
import sys
# 自定义包(添加：中间件)
sys.path.append(os.getcwd())
from Middleware.tool import get_dir_files
catalog = "docs/Algorithm/Leetcode/JavaScript"
files_path = get_dir_files(catalog, [], status=-1, str1=".DS_Store")
# print(files_path)
for line in files_path:
if "ipynb" not in line:
l_file = line.split("/")[-2:]
filename = "%s %s" % (l_file[-1].split(".")[0], l_file[-1].split(".")[1].replace("_", " ").strip())
filepath = " * [%s](%s)" % (filename, "/".join(l_file[-1:]))
# print(">>> ", filepath)
print(filepath)
================================================
FILE: src/py2.x/TreeRecursionIterator.py
================================================
# coding:utf8
from __future__ import print_function
class Node():
def __init__(self, value, left=None, right=None):
self.value = value
self.left = left
self.right = right
def midRecusion(node):
if node is None:
return
midRecusion(node.left)
print(node.value, end=' ')
midRecusion(node.right)
def midIterator(node):
stack = []
while stack or node:
if node is not None:
stack.append(node)
node = node.left
else:
node = stack.pop(-1)
print(node.value, end=' ')
node = node.right
if __name__ == "__main__":
node = Node("D", Node("B", Node("A"), Node("C")), Node("E", right=Node("G", left=Node("F"))))
print('\n中序遍历<递归>：')
midRecusion(node)
print('\n中序遍历<迭代>：')
midIterator(node)
================================================
FILE: src/py2.x/list2iteration.py
================================================
#!/usr/bin/python
# coding:utf8
'''
迭代使用的是循环结构。
递归使用的是选择结构。
'''
from __future__ import print_function
# 递归求解
def calculate(l):
if len(l) <= 1:
return l[0]
value = calculate(l[1:])
return 10**(len(l) - 1) * l[0] + value
# 迭代求解
def calculate2(l):
result = 0
while len(l) >= 1:
result += 10 ** (len(l)-1) * l[0]
l = l[1:]
return result
l1 = [1, 2, 3]
l2 = [4, 5]
sum = 0
result = calculate(l1) + calculate(l2)
# result = calculate2(l1) + calculate2(l2)
print(result)
================================================
FILE: src/py3.x/DataStructure/BinarySearch.py
================================================
"""
1. 二分查找是有条件的，首先是有序，其次因为二分查找操作的是下标，所以要求是顺序表
2. 最优时间复杂度：O(1)
3. 最坏时间复杂度：O(logn)
"""
# def binary_search(nums, data):
# """
# 非递归解决二分查找
# :param nums:
# :return:
# """
# n = len(nums)
# first = 0
# last = n - 1
# while first <= last:
# mid = (last + first) // 2
# if nums[mid] > data:
# last = mid - 1
# elif nums[mid] < data:
# first = mid + 1
# else:
# return True
# return False
def binary_search(nums, data):
"""
递归解决二分查找: nums 是一个有序数组
:param nums:
:return:
"""
n = len(nums)
if n < 1:
return False
mid = n // 2
if nums[mid] > data:
return binary_search(nums[:mid], data)
elif nums[mid] < data:
return binary_search(nums[mid+1:], data)
else:
return True
if __name__ == '__main__':
nums = [1, 4, 6, 8, 10, 20, 25, 30]
if binary_search(nums, 8):
print('ok')
================================================
FILE: src/py3.x/DataStructure/BubbleSort.py
================================================
# coding:utf-8
"""
# 冒泡排序
# 1. 外层循环负责循环的次数，依次递减到1就停止(1个数不存在下一个值)
# 2. 内层循环负责前后两两比较, 判断是否需要交换位置，然后移动判断
5个数
5 0，1，2，3，4
4 0，1，2，3
3 0，1，2
2 0，1
"""
def bubble_sort(nums):
# 判断外出循环的次数
index = len(nums) - 1
while index:
print(index)
# 第一个数字，和后面每一个数字进行对比，找出最大值，放到最后！！
for i in range(index):
if nums[i] > nums[i+1]:
nums[i], nums[i+1] = nums[i+1], nums[i]
index -= 1
if __name__ == "__main__":
nums = [3, 6, 8, 5, 2, 4, 9, 1, 7]
bubble_sort(nums)
print('result:', nums)
================================================
FILE: src/py3.x/DataStructure/InsertionSort.py
================================================
# coding:utf8
"""
插入排序和冒泡排序的区别在于：
插入排序的前提是：左边是有序的数列
而冒泡排序：相邻的值进行交换，一共进行n次交换
"""
def insertion_sort(nums):
for i in range(1, len(nums)):
while i:
if nums[i] < nums[i-1]:
nums[i], nums[i-1] = nums[i-1], nums[i]
i -= 1
return nums
if __name__ == "__main__":
nums = [3, 6, 8, 5, 2, 4, 9, 1, 7]
insertion_sort(nums)
print('result:', nums)
================================================
FILE: src/py3.x/DataStructure/MergeSort.py
================================================
# coding: utf-8
def MergeSort(nums):
if len(nums) <= 1:
return nums
num = int(len(nums)/2)
# 从中间，进行数据的拆分, 递归的返回数据进行迭代排序
left = MergeSort(nums[:num])
right = MergeSort(nums[num:])
print("left: ", left)
print("right: ", right)
print("-" * 20)
return Merge(left, right)
def Merge(left, right):
l, r = 0, 0
result = []
while l < len(left) and r < len(right):
if left[l] < right[r]:
result.append(left[l])
l += 1
else:
result.append(right[r])
r += 1
result += left[l:]
result += right[r:]
return result
if __name__ == "__main__":
nums = [2, 6, 8, 5, 1, 4, 9, 3, 7]
nums = MergeSort(nums)
print('result:', nums)
================================================
FILE: src/py3.x/DataStructure/QuickSort.py
================================================
#!/usr/bin/python
# coding:utf8
def quick_sort(nums, start, end):
i = start
j = end
# 结束排序
if i >= j:
return
# 保存首个数值
key = nums[i]
# 一次排序，i和j的值不断的靠拢，然后最终停止，结束一次排序
while i < j:
# 和最右边的比较，如果>=key,然后j-1，慢慢的和前一个值比较;如果值key，那么就交换位置
while i < j and key >= nums[i]:
print(key, nums[i], '*' * 30)
i += 1
nums[j] = nums[i]
nums[i] = key
# 左边排序
quick_sort(nums, start, i-1)
# 右边排序
quick_sort(nums, i+1, end)
if __name__ == "__main__":
nums = [3, 6, 8, 5, 2, 4, 9, 1, 7]
quick_sort(nums, 0, len(nums) - 1)
print('result:', nums)
================================================
FILE: src/py3.x/DataStructure/RadixSort.py
================================================
#************************基数排序****************************
#确定排序的次数
#排序的顺序跟序列中最大数的位数相关
def radix_sort_nums(L):
maxNum = L[0]
#寻找序列中的最大数
for x in L:
if maxNum < x:
maxNum = x
#确定序列中的最大元素的位数
times = 0
while (maxNum > 0):
maxNum = int((maxNum/10))
times += 1
return times
#找到num从低到高第pos位的数据
def get_num_pos(num, pos):
return (int((num/(10**(pos-1))))) % 10
#基数排序
def radix_sort(L):
count = 10 * [None] #存放各个桶的数据统计个数
bucket = len(L) * [None] #暂时存放排序结果
#从低位到高位依次执行循环
for pos in range(1, radix_sort_nums(L)+1):
#置空各个桶的数据统计
for x in range(0, 10):
count[x] = 0
#统计当前该位(个位，十位，百位....)的元素数目
for x in range(0, len(L)):
#统计各个桶将要装进去的元素个数
j = get_num_pos(int(L[x]), pos)
count[j] += 1
#count[i]表示第i个桶的右边界索引
for x in range(1,10):
count[x] += count[x-1]
#将数据依次装入桶中
for x in range(len(L)-1, -1, -1):
#求出元素第K位的数字
j = get_num_pos(L[x], pos)
#放入对应的桶中，count[j]-1是第j个桶的右边界索引
bucket[count[j]-1] = L[x]
#对应桶的装入数据索引-1
count[j] -= 1
# 将已分配好的桶中数据再倒出来，此时已是对应当前位数有序的表
for x in range(0, len(L)):
L[x] = bucket[x]
================================================
FILE: src/py3.x/DataStructure/SelectionSort.py
================================================
# coding:utf8
"""
选择排序和冒泡排序的区别在于：
选择排序的前提是：找到最小值的位置，最后才进行1次交换
而冒泡排序：相邻的值进行交换，一共进行n次交换
"""
def selection_sort(nums):
for i in range(len(nums)-1):
index = i
# 考虑到数组会遇到多个最小值，所以比较的时候直接用index表示当前比较最小
for j in range(i+1, len(nums)):
if nums[index] > nums[j]:
index = j
nums[i], nums[index] = nums[index], nums[i]
if __name__ == "__main__":
nums = [3, 6, 8, 5, 2, 4, 9, 1, 7]
selection_sort(nums)
print('result:', nums)
================================================
FILE: src/py3.x/DataStructure/ShellSort.py
================================================
# coding: utf8
from __future__ import print_function
def insert_sort(l, start, increment):
for i in range(start+increment, len(l), increment):
for j in range(start, len(l[:i]), increment):
if l[i] < l[j]:
l[i], l[j] = l[j], l[i]
print(increment, '--',l)
return l
def shell_sort(l, increment):
# 依次进行分层
while increment:
# 每一层，都进行n次插入排序
for i in range(0, increment):
insert_sort(l, i, increment)
increment -= 1
return l
if __name__ == "__main__":
l = [5, 2, 9, 8, 1, 10, 3, 4, 7]
increment = len(l)/3+1 if len(l)%3 else len(l)/3
print("开始", l)
l = shell_sort(l, increment)
print("结束", l)
================================================
FILE: src/py3.x/TreeRecursionIterator.py
================================================
# coding:utf8
from __future__ import print_function
class Node():
def __init__(self, value, left=None, right=None):
self.value = value
self.left = left
self.right = right
def midRecusion(node):
if node is None:
return
midRecusion(node.left)
print(node.value, end=' ')
midRecusion(node.right)
def midIterator(node):
stack = []
while stack or node:
if node is not None:
stack.append(node)
node = node.left
else:
node = stack.pop(-1)
print(node.value, end=' ')
node = node.right
if __name__ == "__main__":
node = Node("D", Node("B", Node("A"), Node("C")), Node("E", right=Node("G", left=Node("F"))))
print('\n中序遍历<递归>：')
midRecusion(node)
print('\n中序遍历<迭代>：')
midIterator(node)
================================================
FILE: src/py3.x/kaggle/featured/mercari-price-suggestion-challenge/script.py
================================================
import pyximport; pyximport.install()
import gc
import time
import numpy as np
import pandas as pd
from sklearn.decomposition import TruncatedSVD
#svd = TruncatedSVD(n_components=1000, random_state=42)
from joblib import Parallel, delayed
from scipy.sparse import csr_matrix, hstack
from sklearn.linear_model import Ridge
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import SGDRegressor
import lightgbm as lgb
NUM_BRANDS = 4500
NUM_CATEGORIES = 1200
NAME_MIN_DF = 10
MAX_FEATURES_ITEM_DESCRIPTION = 180000
def rmsle(y, y0):
assert len(y) == len(y0)
return np.sqrt(np.mean(np.power(np.log1p(y)-np.log1p(y0), 2)))
def split_cat(text):
try: return text.split("/")
except: return ("No Label", "No Label", "No Label")
def handle_missing_inplace(dataset):
dataset['general_cat'].fillna(value='missing', inplace=True)
dataset['subcat_1'].fillna(value='missing', inplace=True)
dataset['subcat_2'].fillna(value='missing', inplace=True)
dataset['brand_name'].fillna(value='missing', inplace=True)
dataset['item_description'].fillna(value='missing', inplace=True)
def cutting(dataset):
pop_brand = dataset['brand_name'].value_counts().loc[lambda x: x.index != 'missing'].index[:NUM_BRANDS]
dataset.loc[~dataset['brand_name'].isin(pop_brand), 'brand_name'] = 'missing'
pop_category1 = dataset['general_cat'].value_counts().loc[lambda x: x.index != 'missing'].index[:NUM_CATEGORIES]
pop_category2 = dataset['subcat_1'].value_counts().loc[lambda x: x.index != 'missing'].index[:NUM_CATEGORIES]
pop_category3 = dataset['subcat_2'].value_counts().loc[lambda x: x.index != 'missing'].index[:NUM_CATEGORIES]
dataset.loc[~dataset['general_cat'].isin(pop_category1), 'general_cat'] = 'missing'
dataset.loc[~dataset['subcat_1'].isin(pop_category2), 'subcat_1'] = 'missing'
dataset.loc[~dataset['subcat_2'].isin(pop_category3), 'subcat_2'] = 'missing'
def to_categorical(dataset):
dataset['general_cat'] = dataset['general_cat'].astype('category')
dataset['subcat_1'] = dataset['subcat_1'].astype('category')
dataset['subcat_2'] = dataset['subcat_2'].astype('category')
dataset['item_condition_id'] = dataset['item_condition_id'].astype('category')
def main():
start_time = time.time()
train = pd.read_table('../input/train.tsv', engine='c')
test = pd.read_table('../input/test.tsv', engine='c')
print('[{}] Finished to load data'.format(time.time() - start_time))
print('Train shape: ', train.shape)
print('Test shape: ', test.shape)
nrow_test = train.shape[0] #-dftt.shape[0]
dftt = train[(train.price < 1.0)]
train = train.drop(train[(train.price < 1.0)].index)
del dftt['price']
nrow_train = train.shape[0] #-dftt.shape[0]
#nrow_test = train.shape[0] + dftt.shape[0]
y = np.log1p(train["price"])
merge: pd.DataFrame = pd.concat([train, dftt, test])
submission: pd.DataFrame = test[['test_id']]
submission2: pd.DataFrame = test[['test_id']]
del train
del test
gc.collect()
merge['general_cat'], merge['subcat_1'], merge['subcat_2'] = \
zip(*merge['category_name'].apply(lambda x: split_cat(x)))
merge.drop('category_name', axis=1, inplace=True)
print('[{}] Split categories completed.'.format(time.time() - start_time))
handle_missing_inplace(merge)
print('[{}] Handle missing completed.'.format(time.time() - start_time))
cutting(merge)
print('[{}] Cut completed.'.format(time.time() - start_time))
to_categorical(merge)
print('[{}] Convert categorical completed'.format(time.time() - start_time))
cv = CountVectorizer(min_df=NAME_MIN_DF,ngram_range=(1, 2),
token_pattern=r'\b\w+\b|\w?-\w+', stop_words = 'english')
X_name = cv.fit_transform(merge['name'])
print('[{}] Count vectorize `name` completed.'.format(time.time() - start_time))
cv = CountVectorizer()
X_category1 = cv.fit_transform(merge['general_cat'])
X_category2 = cv.fit_transform(merge['subcat_1'])
X_category3 = cv.fit_transform(merge['subcat_2'])
print('[{}] Count vectorize `categories` completed.'.format(time.time() - start_time))
tv = TfidfVectorizer(max_features=MAX_FEATURES_ITEM_DESCRIPTION,
ngram_range=(1, 2),
token_pattern=r'\w+|\d\w+',)
X_description = tv.fit_transform(merge['item_description'])
print('[{}] TFIDF vectorize `item_description` completed.'.format(time.time() - start_time))
lb = LabelBinarizer(sparse_output=True)
X_brand = lb.fit_transform(merge['brand_name'])
print('[{}] Label binarize `brand_name` completed.'.format(time.time() - start_time))
X_dummies = csr_matrix(pd.get_dummies(merge[['item_condition_id', 'shipping']],
sparse=True).values)
print('[{}] Get dummies on `item_condition_id` and `shipping` completed.'.format(time.time() - start_time))
print (X_dummies.shape, X_description.shape, X_brand.shape, X_category1.shape, X_category2.shape, X_category3.shape, X_name.shape)
sparse_merge = hstack((X_dummies, X_description, X_brand, X_category1, X_category2, X_category3, X_name)).tocsr()
print('[{}] Create sparse merge completed'.format(time.time() - start_time))
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
model = Ridge(alpha=.6, copy_X=True, fit_intercept=True, max_iter=100,
normalize=False, random_state=101, solver='auto', tol=0.05)
model.fit(X, y)
print('[{}] Train ridge completed'.format(time.time() - start_time))
predsR = model.predict(X=X_test)
print('[{}] Predict ridge completed'.format(time.time() - start_time))
model = Ridge(solver='sag', fit_intercept=True, tol=0.05)
model.fit(X, y)
print('[{}] Train ridge v2 completed'.format(time.time() - start_time))
predsR2 = model.predict(X=X_test)
print('[{}] Predict ridge v2 completed'.format(time.time() - start_time))
model = Ridge(alpha=.6, copy_X=True, fit_intercept=True, max_iter=100,
normalize=False, random_state=101, solver='auto', tol=0.05)
model.fit(X, y)
print('[{}] Train ridge completed'.format(time.time() - start_time))
predsR3 = model.predict(X=X_test)
print('[{}] Predict ridge completed'.format(time.time() - start_time))
model = Ridge(solver='sag', fit_intercept=True, tol=0.05)
model.fit(X, y)
print('[{}] Train ridge v2 completed'.format(time.time() - start_time))
predsR4 = model.predict(X=X_test)
print('[{}] Predict ridge v2 completed'.format(time.time() - start_time))
train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size = 0.16, random_state = 144)
d_train = lgb.Dataset(train_X, label=train_y)
d_valid = lgb.Dataset(valid_X, label=valid_y)
watchlist = [d_train, d_valid]
params = {
'max_bin': 8192,
'learning_rate': 0.60,
'application': 'regression',
'max_depth': 3,
'num_leaves': 60,
'verbosity': -1,
'metric': 'RMSE',
'data_random_seed': 2,
'bagging_fraction': 0.5,
'nthread': 4,
'tree_learner' : 'data',
}
model = lgb.train(params, train_set=d_train, num_boost_round=8500, valid_sets=watchlist, \
early_stopping_rounds=1000, verbose_eval=1000)
predsL = model.predict(X_test)
print('[{}] Predict lgb 1 completed.'.format(time.time() - start_time))
preds = (predsR*0.15 + predsL*0.4 + predsR2*0.15 + predsR3*0.15 + predsR4*0.15)
submission['price'] = np.expm1(preds)
submission.to_csv("submission_ridge_2xlgbm.csv", index=False)
preds2 = (predsR*0.1 + predsL*0.6 + predsR2*0.1 + predsR3*0.1 + predsR4*0.1)
submission2['price'] = np.expm1(preds2)
submission.to_csv("submission_ridge_2xlgbm2.csv", index=False)
if __name__ == '__main__':
main()
================================================
FILE: src/py3.x/kaggle/getting-started/digit-recognizer/cnn_keras-python3.6.py
================================================
#!/usr/bin/python
# coding: utf-8
'''
Created on 2017-12-26
Update on 2017-12-26
Author: xiaomingnio
Github: https://github.com/apachecn/kaggle
'''
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from keras.callbacks import ReduceLROnPlateau
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPool2D
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.preprocessing.image import ImageDataGenerator
from keras.utils.np_utils import to_categorical # convert to one-hot-encoding
import os
np.random.seed(2)
# 数据路径
data_dir = '/media/wsw/B634091A3408DF6D/data/kaggle/datasets/getting-started/digit-recognizer/'
# Load the data
train = pd.read_csv(os.path.join(data_dir, 'input/train.csv'))
test = pd.read_csv(os.path.join(data_dir, 'input/test.csv'))
X_train = train.values[:, 1:]
Y_train = train.values[:, 0]
test = test.values
# Normalization
X_train = X_train / 255.0
test = test / 255.0
# Reshape image in 3 dimensions (height = 28px, width = 28px , canal = 1)
X_train = X_train.reshape(-1, 28, 28, 1)
test = test.reshape(-1, 28, 28, 1)
# Encode labels to one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])
Y_train = to_categorical(Y_train, num_classes=10)
# Set the random seed
random_seed = 2
# Split the train and the validation set for the fitting
X_train, X_val, Y_train, Y_val = train_test_split(
X_train, Y_train, test_size=0.1, random_state=random_seed)
# Set the CNN model
# my CNN architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out
model = Sequential()
model.add(
Conv2D(
filters=32,
kernel_size=(5, 5),
padding='Same',
activation='relu',
input_shape=(28, 28, 1)))
model.add(
Conv2D(
filters=32, kernel_size=(5, 5), padding='Same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(
Conv2D(
filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(
Conv2D(
filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation="softmax"))
# Define the optimizer
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
# Compile the model
model.compile(
optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])
epochs = 30
batch_size = 86
# Set a learning rate annealer
learning_rate_reduction = ReduceLROnPlateau(
monitor='val_acc', patience=3, verbose=1, factor=0.5, min_lr=0.00001)
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=10, # randomly rotate images in the range (degrees, 0 to 180)
zoom_range=0.1, # Randomly zoom image
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=False, # randomly flip images
vertical_flip=False) # randomly flip images
datagen.fit(X_train)
history = model.fit_generator(
datagen.flow(
X_train, Y_train, batch_size=batch_size),
epochs=epochs,
validation_data=(X_val, Y_val),
verbose=2,
steps_per_epoch=X_train.shape[0] // batch_size,
callbacks=[learning_rate_reduction])
# predict results
results = model.predict(test)
# select the indix with the maximum probability
results = np.argmax(results, axis=1)
results = pd.Series(results, name="Label")
submission = pd.concat(
[pd.Series(
range(1, 28001), name="ImageId"), results], axis=1)
submission.to_csv(os.path.join(data_dir, "output/Result_keras_CNN.csv",index=False))
print('finished')
================================================
FILE: src/py3.x/kaggle/getting-started/digit-recognizer/cnn_pytorch-python3.6.py
================================================
#!/usr/bin/python3
# coding: utf-8
'''
Created on 2017-12-18
Update on 2018-03-27
Author: 片刻
Github: https://github.com/apachecn/kaggle
Result:
BATCH_SIZE = 10 and EPOCH = 10; [10, 4000] loss: 0.069
BATCH_SIZE = 10 and EPOCH = 15; [10, 4000] loss: 0.069
'''
# import csv
import pandas as pd
# third-party library
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
import os.path
# 数据路径
data_dir = '/opt/data/kaggle/getting-started/digit-recognizer/'
class CustomedDataSet(Dataset):
def __init__(self, train=True):
self.train = train
if self.train:
trainX = pd.read_csv(
os.path.join(data_dir, 'input/train.csv')
# names=["ImageId", "Label"]
)
trainY = trainX.label.as_matrix().tolist()
trainX = trainX.drop(
'label', axis=1).as_matrix().reshape(trainX.shape[0], 1, 28, 28)
self.datalist = trainX
self.labellist = trainY
else:
testX = pd.read_csv(
os.path.join(data_dir, 'input/test.csv')
)
self.testID = testX.index
testX = testX.as_matrix().reshape(testX.shape[0], 1, 28, 28)
self.datalist = testX
def __getitem__(self, index):
if self.train:
return torch.Tensor(
self.datalist[index].astype(float)), self.labellist[index]
else:
return torch.Tensor(self.datalist[index].astype(float))
def __len__(self):
return self.datalist.shape[0]
train_data = CustomedDataSet()
test_data = CustomedDataSet(train=False)
BATCH_SIZE = 150
train_loader = DataLoader(
dataset=train_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)
test_loader = DataLoader(
dataset=test_data, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential( # input shape (1, 28, 28)
nn.Conv2d(
in_channels=1, # input height
out_channels=16, # n_filters
kernel_size=5, # filter size
stride=1, # filter movement/step
padding=2, # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1
), # output shape (16, 28, 28)
nn.ReLU(), # activation
nn.MaxPool2d(
kernel_size=2
), # choose max value in 2x2 area, output shape (16, 14, 14)
)
self.conv2 = nn.Sequential( # input shape (1, 14, 14)
nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
nn.ReLU(), # activation
nn.MaxPool2d(2), # output shape (32, 7, 7)
)
self.out = nn.Linear(32 * 7 * 7,
10) # fully connected layer, output 10 classes
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(
x.size(0),
-1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
output = self.out(x)
return output, x # return x for visualization
cnn = CNN()
# print(cnn) # net architecture
LR = 0.001 # learning rate
optimizer = torch.optim.Adam(
cnn.parameters(), lr=LR) # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
# training and testing
print(u'开始训练')
EPOCH = 5 # train the training data n times, to save time, we just train 1 epoch
for epoch in range(EPOCH):
running_loss = 0.0
for step, (x, y) in enumerate(
train_loader
): # gives batch data, normalize x when iterate train_loader
b_x = Variable(x) # batch x
b_y = Variable(y) # batch y
output = cnn(b_x)[0] # 输入训练数据
loss = loss_func(output, b_y) # 计算误差
optimizer.zero_grad() # 清空上一次梯度
loss.backward() # 误差反向传递
optimizer.step() # 优化器参数更新
# 每1000批数据打印一次平均loss值
running_loss += loss.data[
0] # loss本身为Variable类型，所以要使用data获取其Tensor，因为其为标量，所以取0
if step % 500 == 499: # 每2000批打印一次
print('[%d, %5d] loss: %.3f' %
(epoch + 1, step + 1, running_loss / 500))
running_loss = 0.0
print('Finished Training')
# correct = 0
# total = 0
# for img, label in test_loader:
# img = Variable(img, volatile=True)
# label = Variable(label, volatile=True)
# outputs = cnn(img)
# _, predicted = torch.max(outputs[0], 1)
# # print('1-', type(label), '-------', label)
# # print('2-', type(predicted), '-------', predicted)
# total += label.size(0)
# num_correct = (predicted == label).sum()
# correct += num_correct.data[0]
# print('Accuracy of the network on the %d test images: %.3f %%' % (total, 100 * correct / total))
# I just can't throw all of test data into the network,since it was so huge that my GPU memory cann't afford it
ans = torch.LongTensor() # build a tensor to concatenate answers
for img in test_loader:
img = Variable(img)
outputs = cnn(img)
_, predicted = torch.max(outputs[0], 1)
# print('type(predicted) = ', type(predicted), predicted)
ans = torch.cat([ans, predicted.data], 0)
testLabel = ans.numpy() # only tensor on cpu can transform to the numpy array
# # 结果输出保存
# def saveResult(result, csvName):
# with open(csvName, 'w') as myFile:
# myWriter = csv.writer(myFile)
# myWriter.writerow(["ImageId", "Label"])
# index = 0
# for r in result:
# index += 1
# myWriter.writerow([index, int(r)])
# print('Saved successfully...') # 保存预测结果
# saveResult(testLabel,
# '/opt/data/kaggle/getting-started/digit-recognizer/output/Result_pytorch_CNN.csv')
# 提交结果
submission_df = pd.DataFrame(
data={'ImageId': test_data.testID+1,
'Label': testLabel})
# print(submission_df.head(10))
submission_df.to_csv(
os.path.join(data_dir, 'output/Result_pytorch_CNN.csv'),
columns=["ImageId", "Label"],
index=False)
================================================
FILE: src/py3.x/kaggle/getting-started/digit-recognizer/knn-python3.6.py
================================================
#!/usr/bin/python
# coding: utf-8
'''
Created on 2017-10-26
Update on 2018-05-16
Author: 片刻/ccyf00
Github: https://github.com/apachecn/kaggle
'''
import os
import csv
import datetime
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.neighbors import KNeighborsClassifier
data_dir = '/opt/data/kaggle/getting-started/digit-recognizer/'
# 加载数据
def opencsv():
# 使用 pandas 打开
data = pd.read_csv(os.path.join(data_dir, 'input/train.csv'))
data1 = pd.read_csv(os.path.join(data_dir, 'input/test.csv'))
train_data = data.values[:, 1:] # 读入全部训练数据, [行，列]
train_label = data.values[:, 0] # 读取列表的第一列
test_data = data1.values[:, :] # 测试全部测试个数据
return train_data, train_label, test_data
# 数据预处理-降维 PCA主成成分分析
def dRPCA(x_train, x_test, COMPONENT_NUM):
print('dimensionality reduction...')
trainData = np.array(x_train)
testData = np.array(x_test)
'''
使用说明：https://www.cnblogs.com/pinard/p/6243025.html
n_components>=1
n_components=NUM 设置占特征数量比
0 < n_components < 1
n_components=0.99 设置阈值总方差占比
'''
pca = PCA(n_components=COMPONENT_NUM, whiten=False)
pca.fit(trainData) # Fit the model with X
pcaTrainData = pca.transform(trainData) # Fit the model with X and 在X上完成降维.
pcaTestData = pca.transform(testData) # Fit the model with X and 在X上完成降维.
# pca 方差大小、方差占比、特征数量
# print("方差大小:\n", pca.explained_variance_, "方差占比:\n", pca.explained_variance_ratio_)
print("特征数量: %s" % pca.n_components_)
print("总方差占比: %s" % sum(pca.explained_variance_ratio_))
return pcaTrainData, pcaTestData
def trainModel(trainData, trainLabel):
clf = KNeighborsClassifier() # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)
clf.fit(trainData, np.ravel(trainLabel)) # ravel Return a contiguous flattened array.
return clf
def saveResult(result, csvName):
with open(csvName, 'w') as myFile: # 创建记录输出结果的文件（w 和 wb 使用的时候有问题）
# python3里面对 str和bytes类型做了严格的区分，不像python2里面某些函数里可以混用。所以用python3来写wirterow时，打开文件不要用wb模式，只需要使用w模式，然后带上newline=''
myWriter = csv.writer(myFile)
myWriter.writerow(["ImageId", "Label"])
index = 0
for r in result:
index += 1
myWriter.writerow([index, int(r)])
print('Saved successfully...') # 保存预测结果
def dRecognition_knn():
# 开始时间
sta_time = datetime.datetime.now()
# 加载数据
trainData, trainLabel, testData = opencsv()
# print("trainData==>", type(trainData), shape(trainData))
# print("trainLabel==>", type(trainLabel), shape(trainLabel))
# print("testData==>", type(testData), shape(testData))
print("load data finish")
end_time_1 = datetime.datetime.now()
print('load data time used: %s' % end_time_1)
# 降维处理
trainDataPCA, testDataPCA = dRPCA(trainData, testData, 0.8)
# 模型训练
clf = trainModel(trainDataPCA, trainLabel)
# 结果预测
testLabel = clf.predict(testDataPCA)
# 结果的输出
saveResult(testLabel, os.path.join(data_dir, 'output/Result_knn.csv'))
print("finish!")
# 结束时间
end_time = datetime.datetime.now()
times = (end_time - sta_time).seconds
print("\n运行时间: %ss == %sm == %sh\n\n" % (times, times/60, times/60/60))
if __name__ == '__main__':
dRecognition_knn()
================================================
FILE: src/py3.x/kaggle/getting-started/digit-recognizer/nn-python3.6.py
================================================
#!/usr/bin/python
# coding: utf-8
'''
Created on 2018-05-14
Update on 2018-05-14
Author: 平淡的天
Github: https://github.com/apachecn/kaggle
'''
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import pandas as pd
train_data = pd.read_csv(r"C:\Users\312\Desktop\digit-recognizer\train.csv")
test_data = pd.read_csv(r"C:\Users\312\Desktop\digit-recognizer\test.csv")
data = pd.concat([train_data, test_data], axis=0).reset_index(drop=True)
data.drop(['label'], axis=1, inplace=True)
label = train_data.label
pca = PCA(n_components=100, random_state=34)
data_pca = pca.fit_transform(data)
Xtrain, Ytrain, xtest, ytest = train_test_split(
data_pca[0:len(train_data)], label, test_size=0.1, random_state=34)
clf = MLPClassifier(
hidden_layer_sizes=(100, ),
activation='relu',
alpha=0.0001,
learning_rate='constant',
learning_rate_init=0.001,
max_iter=200,
shuffle=True,
random_state=34)
clf.fit(Xtrain, xtest)
y_predict = clf.predict(Ytrain)
zeroLable = ytest - y_predict
rightCount = 0
for i in range(len(zeroLable)):
if list(zeroLable)[i] == 0:
rightCount += 1
print('the right rate is:', float(rightCount) / len(zeroLable))
result = clf.predict(data_pca[len(train_data):])
i = 0
fw = open("C:\\Users\\312\\Desktop\\digit-recognizer\\result.csv", 'w')
with open('C:\\Users\\312\\Desktop\\digit-recognizer\\sample_submission.csv'
) as pred_file:
fw.write('{},{}\n'.format('ImageId', 'Label'))
for line in pred_file.readlines()[1:]:
splits = line.strip().split(',')
fw.write('{},{}\n'.format(splits[0], result[i]))
i += 1
================================================
FILE: src/py3.x/kaggle/getting-started/digit-recognizer/rf-python3.6.py
================================================
#!/usr/bin/python
# coding: utf-8
'''
Created on 2018-05-14
Update on 2018-05-19
Author: 平淡的天/wang-sw
Github: https://github.com/apachecn/kaggle
'''
import os
import time
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import pandas as pd
import numpy as np
# from numpy import arange
# from lightgbm import LGBMClassifier
# from sklearn.model_selection import GridSearchCV
# 数据路径
data_dir = '/Users/wuyanxue/Documents/GitHub/datasets/getting-started/digit-recognizer/'
# 加载数据
def opencsv():
# 使用 pandas 打开
train_data = pd.read_csv(os.path.join(data_dir, 'input/train.csv'))
test_data = pd.read_csv(os.path.join(data_dir, 'input/test.csv'))
data = pd.concat([train_data, test_data], axis=0).reset_index(drop=True)
data.drop(['label'], axis=1, inplace=True)
label = train_data.label
return train_data, test_data, data, label
# 数据预处理-降维 PCA主成成分分析
def dRPCA(data, COMPONENT_NUM=100):
print('dimensionality reduction...')
data = np.array(data)
'''
使用说明：https://www.cnblogs.com/pinard/p/6243025.html
n_components>=1
n_components=NUM 设置占特征数量
0 < n_components < 1
n_components=0.99 设置阈值总方差占比
'''
pca = PCA(n_components=COMPONENT_NUM, random_state=34)
data_pca = pca.fit_transform(data)
# pca 方差大小、方差占比、特征数量
print(pca.explained_variance_, '\n', pca.explained_variance_ratio_, '\n',
pca.n_components_)
print(sum(pca.explained_variance_ratio_))
storeModel(data_pca, os.path.join(data_dir, 'output/Result_sklearn_rf.pcaData'))
return data_pca
# 训练模型
def trainModel(X_train, y_train):
print('Train RF...')
clf = RandomForestClassifier(
n_estimators=10,
max_depth=10,
min_samples_split=2,
min_samples_leaf=1,
random_state=34)
clf.fit(X_train, y_train) # 训练rf
# clf=LGBMClassifier(num_leaves=63, max_depth=7, n_estimators=80, n_jobs=20)
# param_test1 = {'n_estimators':arange(10,150,10),'max_depth':arange(1,21,1)}
# gsearch1 = GridSearchCV(estimator = clf, param_grid = param_test1, scoring='accuracy',iid=False,cv=5)
# gsearch1.fit(X_train, y_train)
# print(gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_)
# clf=gsearch1.best_estimator_
return clf
# 计算准确率
def printAccuracy(y_test, y_predict):
zeroLable = y_test - y_predict
rightCount = 0
for i in range(len(zeroLable)):
if list(zeroLable)[i] == 0:
rightCount += 1
print('the right rate is:', float(rightCount) / len(zeroLable))
# 存储模型
def storeModel(model, filename):
import pickle
with open(filename, 'wb') as fw:
pickle.dump(model, fw)
# 加载模型
def getModel(filename):
import pickle
fr = open(filename, 'rb')
return pickle.load(fr)
# 结果输出保存
def saveResult(result, csvName):
i = 0
n = len(result)
print('the size of test set is {}'.format(n))
with open(os.path.join(data_dir, 'output/Result_sklearn_RF.csv'), 'w') as fw:
fw.write('{},{}\n'.format('ImageId', 'Label'))
for i in range(1, n + 1):
fw.write('{},{}\n'.format(i, result[i - 1]))
print('Result saved successfully... and the path = {}'.format(csvName))
def trainRF():
start_time = time.time()
# 加载数据
train_data, test_data, data, label = opencsv()
print("load data finish")
stop_time_l = time.time()
print('load data time used:%f s' % (stop_time_l - start_time))
startTime = time.time()
# 模型训练 (数据预处理-降维)
data_pca = dRPCA(data,100)
X_train, X_test, y_train, y_test = train_test_split(
data_pca[0:len(train_data)], label, test_size=0.1, random_state=34)
clf = trainModel(X_train, y_train)
# 保存结果
storeModel(data_pca[len(train_data):], os.path.join(data_dir, 'output/Result_sklearn_rf.pcaPreData'))
storeModel(clf, os.path.join(data_dir, 'output/Result_sklearn_rf.model'))
# 模型准确率
y_predict = clf.predict(X_test)
printAccuracy(y_test, y_predict)
print("finish!")
stopTime = time.time()
print('TrainModel store time used:%f s' % (stopTime - startTime))
def preRF():
startTime = time.time()
# 加载模型和数据
clf = getModel(os.path.join(data_dir, 'output/Result_sklearn_rf.model'))
pcaPreData = getModel(os.path.join(data_dir, 'output/Result_sklearn_rf.pcaPreData'))
# 结果预测
result = clf.predict(pcaPreData)
# 结果的输出
saveResult(result, os.path.join(data_dir, 'output/Result_sklearn_rf.csv'))
print("finish!")
stopTime = time.time()
print('PreModel load time used:%f s' % (stopTime - startTime))
if __name__ == '__main__':
# 训练并保存模型
trainRF()
# 加载预测数据集
preRF()
================================================
FILE: src/py3.x/kaggle/getting-started/digit-recognizer/svm-python3.6.py
================================================
#!/usr/bin/python3
# coding: utf-8
'''
Created on 2017-10-26
Update on 2017-10-26
Author: 片刻
Github: https://github.com/apachecn/kaggle
'''
import os
import csv
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
# 数据路径
data_dir = '/opt/data/kaggle/getting-started/digit-recognizer/'
# 加载数据
def opencsv():
print('Load Data...')
# 使用 pandas 打开
dataTrain = pd.read_csv(os.path.join(data_dir, 'input/train.csv'))
dataPre = pd.read_csv(os.path.join(data_dir, 'input/test.csv'))
trainData = dataTrain.values[:, 1:] # 读入全部训练数据
trainLabel = dataTrain.values[:, 0]
preData = dataPre.values[:, :] # 测试全部测试个数据
return trainData, trainLabel, preData
# 数据预处理-降维 PCA主成成分分析
def dRCsv(x_train, x_test, preData, COMPONENT_NUM):
print('dimensionality reduction...')
trainData = np.array(x_train)
testData = np.array(x_test)
preData = np.array(preData)
'''
使用说明：https://www.cnblogs.com/pinard/p/6243025.html
n_components>=1
n_components=NUM 设置占特征数量比
0 < n_components < 1
n_components=0.99 设置阈值总方差占比
'''
pca = PCA(n_components=COMPONENT_NUM, whiten=True)
pca.fit(trainData) # Fit the model with X
pcaTrainData = pca.transform(trainData) # Fit the model with X and 在X上完成降维.
pcaTestData = pca.transform(testData) # Fit the model with X and 在X上完成降维.
pcaPreData = pca.transform(preData) # Fit the model with X and 在X上完成降维.
# pca 方差大小、方差占比、特征数量
print(pca.explained_variance_, '\n', pca.explained_variance_ratio_, '\n', pca.n_components_)
print(sum(pca.explained_variance_ratio_))
return pcaTrainData, pcaTestData, pcaPreData
# 训练模型
def trainModel(trainData, trainLabel):
print('Train SVM...')
clf = SVC(C=4, kernel='rbf')
clf.fit(trainData, trainLabel) # 训练SVM
return clf
# 结果输出保存
def saveResult(result, csvName):
with open(csvName, 'w') as myFile:
myWriter = csv.writer(myFile)
myWriter.writerow(["ImageId", "Label"])
index = 0
for r in result:
index += 1
myWriter.writerow([index, int(r)])
print('Saved successfully...') # 保存预测结果
# 分析数据,看数据是否满足要求（通过这些来检测数据的相关性，考虑在分类的时候提取出重要的特征）
def analyse_data(dataMat):
meanVals = np.mean(dataMat, axis=0) # np.mean 求出每列的平均值meanVals
meanRemoved = dataMat-meanVals # 每一列特征值减去该列的特征值均值
# 计算协方差矩阵，除数n-1是为了得到协方差的 无偏估计
# cov(X,0) = cov(X) 除数是n-1(n为样本个数)
# cov(X,1) 除数是n
covMat = np.cov(meanRemoved, rowvar=0) # cov 计算协方差的值,
# np.mat 是用来生成一个矩阵的
# 保存特征值(eigvals)和对应的特征向量(eigVects)
eigvals, eigVects = np.linalg.eig(np.mat(covMat)) # linalg.eig 计算的值是矩阵的特征值，保存在对应的矩阵中
eigValInd = np.argsort(eigvals) # argsort 对特征值进行排序，返回的是数值从小到大的索引值
topNfeat = 100 # 需要保留的特征维度，即要压缩成的维度数
# 从排序后的矩阵最后一个开始自下而上选取最大的N个特征值，返回其对应的索引
eigValInd = eigValInd[:-(topNfeat+1):-1]
# 计算特征值的总和
cov_all_score = float(sum(eigvals))
sum_cov_score = 0
for i in range(0, len(eigValInd)):
# 特征值进行相加
line_cov_score = float(eigvals[eigValInd[i]])
sum_cov_score += line_cov_score
'''
我们发现其中有超过20%的特征值都是0。
这就意味着这些特征都是其他特征的副本，也就是说，它们可以通过其他特征来表示，而本身并没有提供额外的信息。
最前面15个值的数量级大于10^5，实际上那以后的值都变得非常小。
这就相当于告诉我们只有部分重要特征，重要特征的数目也很快就会下降。
最后，我们可能会注意到有一些小的负值，他们主要源自数值误差应该四舍五入成0.
'''
print('主成分：%s, 方差占比：%s%%, 累积方差占比：%s%%' % (format(i+1, '2.0f'), format(line_cov_score/cov_all_score*100, '4.2f'), format(sum_cov_score/cov_all_score*100, '4.1f')))
# 找出最高准确率
def getOptimalAccuracy(trainData, trainLabel, preData):
# 分析数据 100个特征左右
# analyse_data(trainData)
x_train, x_test, y_train, y_test = train_test_split(trainData, trainLabel, test_size=0.1)
lineLen, featureLen = np.shape(x_test) # shape 返回矩阵或者数值的长度
# print(lineLen, type(lineLen), featureLen, type(featureLen))
minErr = 1
minSumErr = 0
optimalNum = 1
optimalLabel = []
optimalSVMClf = None
pcaPreDataResult = None
for i in range(30, 45, 1):
# 评估训练结果
pcaTrainData, pcaTestData, pcaPreData = dRCsv(x_train, x_test, preData, i)
clf = trainModel(pcaTrainData, y_train)
testLabel = clf.predict(pcaTestData)
errArr = np.mat(np.ones((lineLen, 1)))
sumErrArr = errArr[testLabel != y_test].sum()
sumErr = sumErrArr/lineLen
print('i=%s' % i, lineLen, sumErrArr, sumErr)
if sumErr <= minErr:
minErr = sumErr
minSumErr = sumErrArr
optimalNum = i
optimalSVMClf = clf
optimalLabel = testLabel
pcaPreDataResult = pcaPreData
print("i=%s >>>>> \t" % i, lineLen, int(minSumErr), 1-minErr)
'''
展现 准确率与召回率
precision 准确率
recall 召回率
f1-score 准确率和召回率的一个综合得分
support 参与比较的数量
参考链接：http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report
'''
# target_names 以 y的label分类为准
# target_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
target_names = [str(i) for i in list(set(y_test))]
print(target_names)
print(classification_report(y_test, optimalLabel, target_names=target_names))
print("特征数量= %s, 存在最优解：>>> \t" % optimalNum, lineLen, int(minSumErr), 1-minErr)
return optimalSVMClf, pcaPreDataResult
# 存储模型
def storeModel(model, filename):
import pickle
with open(filename, 'wb') as fw:
pickle.dump(model, fw)
# 加载模型
def getModel(filename):
import pickle
fr = open(filename, 'rb')
return pickle.load(fr)
def trainDRSVM():
startTime = time.time()
# 加载数据
trainData, trainLabel, preData = opencsv()
# 模型训练 (数据预处理-降维)
optimalSVMClf, pcaPreData = getOptimalAccuracy(trainData, trainLabel, preData)
storeModel(optimalSVMClf, os.path.join(data_dir, 'output/Result_sklearn_SVM.model'))
storeModel(pcaPreData, os.path.join(data_dir, 'output/Result_sklearn_SVM.pcaPreData'))
print("finish!")
stopTime = time.time()
print('TrainModel store time used:%f s' % (stopTime - startTime))
def preDRSVM():
startTime = time.time()
# 加载模型和数据
optimalSVMClf = getModel(os.path.join(data_dir, 'output/Result_sklearn_SVM.model'))
pcaPreData = getModel(os.path.join(data_dir, 'output/Result_sklearn_SVM.pcaPreData'))
# 结果预测
testLabel = optimalSVMClf.predict(pcaPreData)
# print("testLabel = %f" % testscore)
# 结果的输出
saveResult(testLabel, os.path.join(data_dir, 'output/Result_sklearn_SVM.csv'))
print("finish!")
stopTime = time.time()
print('PreModel load time used:%f s' % (stopTime - startTime))
# 数据可视化
def dataVisulization(data, labels):
pca = PCA(n_components=2, whiten=True) # 使用PCA方法降到2维
pca.fit(data)
pcaData = pca.transform(data)
uniqueClasses = set(labels)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
for cClass in uniqueClasses:
plt.scatter(pcaData[labels==cClass, 0], pcaData[labels==cClass, 1])
plt.xlabel('$x_1$')
plt.ylabel('$x_2$')
plt.title('MNIST visualization')
plt.show()
if __name__ == '__main__':
trainData, trainLabel, preData = opencsv()
dataVisulization(trainData, trainLabel)
# 训练并保存模型
# trainDRSVM()
# 分析数据
# analyse_data(trainData)
# 加载预测数据集
# preDRSVM()
================================================
FILE: src/py3.x/kaggle/getting-started/house-prices/base-model_lasso_python3.6.py
================================================
#!/usr/bin/python
# coding: utf-8
'''
Created on 2017-12-11
Update on 2017-12-11
Author: Usernametwo
Github: https://github.com/apachecn/kaggle
'''
import time
import pandas as pd
from sklearn.linear_model import Ridge
import os.path
data_dir = '/opt/data/kaggle/getting-started/house-prices'
# 加载数据
def opencsv():
# 使用 pandas 打开
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
return df_train, df_test
def saveResult(result):
result.to_csv(
os.path.join(data_dir, "submission.csv"), sep=',', encoding='utf-8')
def ridgeRegression(trainData, trainLabel, df_test):
ridge = Ridge(
alpha=10.0
) # default:k = 5,defined by yourself:KNeighborsClassifier(n_neighbors=10)
ridge.fit(trainData, trainLabel)
predict = ridge.predict(df_test)
pred_df = pd.DataFrame(predict, index=df_test["Id"], columns=["SalePrice"])
return pred_df
def dataProcess(df_train, df_test):
trainLabel = df_train['SalePrice']
df = pd.concat((df_train, df_test), axis=0, ignore_index=True)
df.dropna(axis=1, inplace=True)
df = pd.get_dummies(df)
trainData = df[:df_train.shape[0]]
test = df[df_train.shape[0]:]
return trainData, trainLabel, test
def Regression_ridge():
start_time = time.time()
# 加载数据
df_train, df_test = opencsv()
print("load data finish")
stop_time_l = time.time()
print('load data time used:%f' % (stop_time_l - start_time))
# 数据预处理
train_data, trainLabel, df_test = dataProcess(df_train, df_test)
# 模型训练预测
result = ridgeRegression(train_data, trainLabel, df_test)
# 结果的输出
saveResult(result)
print("finish!")
stop_time_r = time.time()
print('classify time used:%f' % (stop_time_r - start_time))
if __name__ == '__main__':
Regression_ridge()
================================================
FILE: src/py3.x/kaggle/getting-started/house-prices/deeplearning_method.py
================================================
# -*- coding: utf-8 -*-
__author__ = 'liudong'
__date__ = '2018/5/29 下午7:40'
import csv
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.python.framework import ops
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
def load_data(train_path, test_path):
"""
加载数据的方法
:param train_path: path for the train set file
:param test_path: path for the test set file
:return: a 'pandas' array for each set
"""
train_data = pd.read_csv(train_path)
test_data = pd.read_csv(test_path)
print("number of training examples = " + str(train_data.shape[0])) # 1460
print("number of test examples = " + str(test_data.shape[0])) # 1459
print("train shape: " + str(train_data.shape)) # (1460, 81)
print("test shape: " + str(test_data.shape)) # (1459, 80)
return train_data, test_data
def pre_process_data(df):
"""
Perform a number of pre process functions on the data set
:param df: pandas data frame
:return: processed data frame
"""
# one-hot encode categorical values
df = pd.get_dummies(df)
return df
def mini_batches(train_set, train_labels, mini_batch_size):
"""
Generate mini batches from the data set (data and labels)
:param train_set: data set with the examples
:param train_labels: data set with the labels
:param mini_batch_size: mini batch size
:return: mini batches
"""
set_size = train_set.shape[0]
batches = []
num_complete_minibatches = set_size // mini_batch_size
for k in range(0, num_complete_minibatches):
mini_batch_x = train_set[k * mini_batch_size: (k + 1) * mini_batch_size]
mini_batch_y = train_labels[k * mini_batch_size: (k + 1) * mini_batch_size]
mini_batch = (mini_batch_x, mini_batch_y)
batches.append(mini_batch)
# Handling the end case (last mini-batch < mini_batch_size)
if set_size % mini_batch_size != 0:
mini_batch_x = train_set[(set_size - (set_size % mini_batch_size)):]
mini_batch_y = train_labels[(set_size - (set_size % mini_batch_size)):]
mini_batch = (mini_batch_x, mini_batch_y)
batches.append(mini_batch)
return batches
def create_placeholders(input_size, output_size):
"""
Creates the placeholders for the tensorflow session.
:param input_size: scalar, input size
:param output_size: scalar, output size
:return: X placeholder for the data input, of shape [None, input_size] and dtype "float"
:return: Y placeholder for the input labels, of shape [None, output_size] and dtype "float"
"""
x = tf.placeholder(shape=(None, input_size), dtype=tf.float32, name="X")
y = tf.placeholder(shape=(None, output_size), dtype=tf.float32, name="Y")
return x, y
def forward_propagation(x, parameters, keep_prob=1.0, hidden_activation='relu'):
"""
Implement forward propagation with dropout for the [LINEAR->RELU]*(L-1)->LINEAR-> computation
:param x: data, pandas array of shape (input size, number of examples)
:param parameters: output of initialize_parameters()
:param keep_prob: probability to keep each node of the layer
:param hidden_activation: activation function of the hidden layers
:return: last LINEAR value
"""
a_dropout = x
n_layers = len(parameters) // 2 # number of layers in the neural network
for l in range(1, n_layers):
a_prev = a_dropout
a_dropout = linear_activation_forward(a_prev, parameters['w%s' % l], parameters['b%s' % l], hidden_activation)
if keep_prob < 1.0:
a_dropout = tf.nn.dropout(a_dropout, keep_prob)
al = tf.matmul(a_dropout, parameters['w%s' % n_layers]) + parameters['b%s' % n_layers]
return al
def linear_activation_forward(a_prev, w, b, activation):
"""
Implement the forward propagation for the LINEAR->ACTIVATION layer
:param a_prev: activations from previous layer (or input data): (size of previous layer, number of examples)
:param w: weights matrix: numpy array of shape (size of current layer, size of previous layer)
:param b: bias vector, numpy array of shape (size of the current layer, 1)
:param activation: the activation to be used in this layer, stored as a text string: "sigmoid" or "relu"
:return: the output of the activation function, also called the post-activation value
"""
a = None
if activation == "sigmoid":
z = tf.matmul(a_prev, w) + b
a = tf.nn.sigmoid(z)
elif activation == "relu":
z = tf.matmul(a_prev, w) + b
a = tf.nn.relu(z)
elif activation == "leaky relu":
z = tf.matmul(a_prev, w) + b
a = tf.nn.leaky_relu(z)
return a
def initialize_parameters(layer_dims):
"""
:param layer_dims: python array (list) containing the dimensions of each layer in our network
:return: python dictionary containing your parameters "w1", "b1", ..., "wn", "bn":
Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])
bl -- bias vector of shape (layer_dims[l], 1)
"""
parameters = {}
n_layers = len(layer_dims)
for l in range(1, n_layers):
parameters['w' + str(l)] = tf.get_variable('w' + str(l), [layer_dims[l - 1], layer_dims[l]],
initializer=tf.contrib.layers.xavier_initializer())
parameters['b' + str(l)] = tf.get_variable('b' + str(l), [layer_dims[l]], initializer=tf.zeros_initializer())
return parameters
def compute_cost(z3, y):
"""
:param z3: output of forward propagation (output of the last LINEAR unit)
:param y: "true" labels vector placeholder, same shape as Z3
:return: Tensor of the cost function (RMSE as it is a regression)
"""
cost = tf.sqrt(tf.reduce_mean(tf.square(y - z3)))
return cost
def predict(data, parameters):
"""
make a prediction based on a data set and parameters
:param data: based data set
:param parameters: based parameters
:return: array of predictions
"""
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
dataset = tf.cast(tf.constant(data), tf.float32)
fw_prop_result = forward_propagation(dataset, parameters)
prediction = fw_prop_result.eval()
return prediction
def rmse(predictions, labels):
"""
calculate cost between two data sets
:param predictions: data set of predictions
:param labels: data set of labels (real values)
:return: percentage of correct predictions
"""
prediction_size = predictions.shape[0]
prediction_cost = np.sqrt(np.sum(np.square(labels - predictions)) / prediction_size)
return prediction_cost
def rmsle(predictions, labels):
"""
calculate cost between two data sets
:param predictions: data set of predictions
:param labels: data set of labels (real values)
:return: percentage of correct predictions
"""
prediction_size = predictions.shape[0]
prediction_cost = np.sqrt(np.sum(np.square(np.log(predictions + 1) - np.log(labels + 1))) / prediction_size)
return prediction_cost
def l2_regularizer(cost, l2_beta, parameters, n_layers):
"""
Function to apply l2 regularization to the model
:param cost: usual cost of the model
:param l2_beta: beta value used for the normalization
:param parameters: parameters from the model (used to get weights values)
:param n_layers: number of layers of the model
:return: cost updated
"""
regularizer = 0
for i in range(1, n_layers):
regularizer += tf.nn.l2_loss(parameters['w%s' % i])
cost = tf.reduce_mean(cost + l2_beta * regularizer)
return cost
def build_submission_name(layers_dims, num_epochs, lr_decay,
learning_rate, l2_beta, keep_prob, minibatch_size, num_examples):
"""
builds a string (submission file name), based on the model parameters
:param layers_dims: model layers dimensions
:param num_epochs: model number of epochs
:param lr_decay: model learning rate decay
:param learning_rate: model learning rate
:param l2_beta: beta used on l2 normalization
:param keep_prob: keep probability used on dropout normalization
:param minibatch_size: model mini batch size (0 to do not use mini batches)
:param num_examples: number of model examples (training data)
:return: built string
"""
submission_name = 'ly{}-epoch{}.csv' \
.format(layers_dims, num_epochs)
if lr_decay != 0:
submission_name = 'lrdc{}-'.format(lr_decay) + submission_name
else:
submission_name = 'lr{}-'.format(learning_rate) + submission_name
if l2_beta > 0:
submission_name = 'l2{}-'.format(l2_beta) + submission_name
if keep_prob < 1:
submission_name = 'dk{}-'.format(keep_prob) + submission_name
if minibatch_size != num_examples:
submission_name = 'mb{}-'.format(minibatch_size) + submission_name
return submission_name
def plot_model_cost(train_costs, validation_costs, submission_name):
"""
:param train_costs: array with the costs from the model training
:param validation_costs: array with the costs from the model validation
:param submission_name: name of the submission (used for the plot title)
:return:
"""
plt.plot(np.squeeze(train_costs), label='Train cost')
plt.plot(np.squeeze(validation_costs), label='Validation cost')
plt.ylabel('cost')
plt.xlabel('iterations (per tens)')
plt.title("Model: " + submission_name)
plt.legend()
plt.show()
plt.close()
def model(train_set, train_labels, validation_set, validation_labels, layers_dims, learning_rate=0.01, num_epochs=1001,
print_cost=True, plot_cost=True, l2_beta=0., keep_prob=1.0, hidden_activation='relu', return_best=False,
minibatch_size=0, lr_decay=0):
"""
:param train_set: training set
:param train_labels: training labels
:param validation_set: validation set
:param validation_labels: validation labels
:param layers_dims: array with the layer for the model
:param learning_rate: learning rate of the optimization
:param num_epochs: number of epochs of the optimization loop
:param print_cost: True to print the cost every 500 epochs
:param plot_cost: True to plot the train and validation cost
:param l2_beta: beta parameter for the l2 regularization
:param keep_prob: probability to keep each node of each hidden layer (dropout)
:param hidden_activation: activation function to be used on the hidden layers
:param return_best: True to return the highest params from all epochs
:param minibatch_size: size of th mini batch
:param lr_decay: if != 0, sets de learning rate decay on each epoch
:return parameters: parameters learnt by the model. They can then be used to predict.
:return submission_name: name for the trained model
"""
ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables
input_size = layers_dims[0]
output_size = layers_dims[-1]
num_examples = train_set.shape[0]
n_layers = len(layers_dims)
train_costs = []
validation_costs = []
best_iteration = [float('inf'), 0]
best_params = None
if minibatch_size == 0 or minibatch_size > num_examples:
minibatch_size = num_examples
num_minibatches = num_examples // minibatch_size
if num_minibatches == 0:
num_minibatches = 1
submission_name = build_submission_name(layers_dims, num_epochs, lr_decay, learning_rate, l2_beta, keep_prob,
minibatch_size, num_examples)
x, y = create_placeholders(input_size, output_size)
tf_valid_dataset = tf.cast(tf.constant(validation_set), tf.float32)
parameters = initialize_parameters(layers_dims)
fw_output_train = forward_propagation(x, parameters, keep_prob, hidden_activation)
train_cost = compute_cost(fw_output_train, y)
fw_output_valid = forward_propagation(tf_valid_dataset, parameters, keep_prob, hidden_activation)
validation_cost = compute_cost(fw_output_valid, validation_labels)
if l2_beta > 0:
train_cost = l2_regularizer(train_cost, l2_beta, parameters, n_layers)
validation_cost = l2_regularizer(validation_cost, l2_beta, parameters, n_layers)
if lr_decay != 0:
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.inverse_time_decay(learning_rate, global_step=global_step, decay_rate=lr_decay,
decay_steps=1)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(train_cost, global_step=global_step)
else:
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(train_cost)
# uncomment to use tensorboard
# tf.summary.scalar('train cost', train_cost)
# tf.summary.scalar('validation cost', validation_cost)
init = tf.global_variables_initializer()
with tf.Session() as sess:
# uncomment to use tensorboard
# writer = tf.summary.FileWriter('logs/'+submission_name, sess.graph)
sess.run(init)
for epoch in range(num_epochs):
train_epoch_cost = 0.
validation_epoch_cost = 0.
minibatches = mini_batches(train_set, train_labels, minibatch_size)
for minibatch in minibatches:
# uncomment to use tensorboard
# merge = tf.summary.merge_all()
(minibatch_X, minibatch_Y) = minibatch
feed_dict = {x: minibatch_X, y: minibatch_Y}
# uncomment to use tensorboard
# _, summary, minibatch_train_cost, minibatch_validation_cost = sess.run(
# [optimizer, merge, train_cost, validation_cost], feed_dict=feed_dict)
# comment to use tensorboard
_, minibatch_train_cost, minibatch_validation_cost = sess.run(
[optimizer, train_cost, validation_cost], feed_dict=feed_dict)
train_epoch_cost += minibatch_train_cost / num_minibatches
validation_epoch_cost += minibatch_validation_cost / num_minibatches
if print_cost is True and epoch % 500 == 0:
print("Train cost after epoch %i: %f" % (epoch, train_epoch_cost))
print("Validation cost after epoch %i: %f" % (epoch, validation_epoch_cost))
if plot_cost is True and epoch % 10 == 0:
train_costs.append(train_epoch_cost)
validation_costs.append(validation_epoch_cost)
# uncomment to use tensorboard
# if epoch % 10 == 0:
# writer.add_summary(summary, epoch)
if return_best is True and validation_epoch_cost < best_iteration[0]:
best_iteration[0] = validation_epoch_cost
best_iteration[1] = epoch
best_params = sess.run(parameters)
if return_best is True:
parameters = best_params
else:
parameters = sess.run(parameters)
print("Parameters have been trained, getting metrics...")
train_rmse = rmse(predict(train_set, parameters), train_labels)
validation_rmse = rmse(predict(validation_set, parameters), validation_labels)
train_rmsle = rmsle(predict(train_set, parameters), train_labels)
validation_rmsle = rmsle(predict(validation_set, parameters), validation_labels)
print('Train rmse: {:.4f}'.format(train_rmse))
print('Validation rmse: {:.4f}'.format(validation_rmse))
print('Train rmsle: {:.4f}'.format(train_rmsle))
print('Validation rmsle: {:.4f}'.format(validation_rmsle))
submission_name = 'tr_cost-{:.2f}-vd_cost{:.2f}-'.format(train_rmse, validation_rmse) + submission_name
if return_best is True:
print('Lowest rmse: {:.2f} at epoch {}'.format(best_iteration[0], best_iteration[1]))
if plot_cost is True:
plot_model_cost(train_costs, validation_costs, submission_name)
return parameters, submission_name
TRAIN_PATH = '/Users/liudong/Desktop/house_price/train.csv'
TEST_PATH = '/Users/liudong/Desktop/house_price/test.csv'
train, test = load_data(TRAIN_PATH, TEST_PATH)
# get the labels values
train_raw_labels = train['SalePrice'].to_frame().as_matrix()
# pre process data sets
train_pre = pre_process_data(train)
test_pre = pre_process_data(test)
# drop unwanted columns
train_pre = train_pre.drop(['Id', 'SalePrice'], axis=1)
test_pre = test_pre.drop(['Id'], axis=1)
# align both data sets (by outer join), to make they have the same amount of features,
# this is required because of the mismatched categorical values in train and test sets
train_pre, test_pre = train_pre.align(test_pre, join='outer', axis=1)
# replace the nan values added by align for 0
train_pre.replace(to_replace=np.nan, value=0, inplace=True)
test_pre.replace(to_replace=np.nan, value=0, inplace=True)
train_pre = train_pre.as_matrix().astype(np.float)
test_pre = test_pre.as_matrix().astype(np.float)
# scale values
standard_scaler = preprocessing.StandardScaler()
train_pre = standard_scaler.fit_transform(train_pre)
test_pre = standard_scaler.fit_transform(test_pre)
X_train, X_valid, Y_train, Y_valid = train_test_split(train_pre, train_raw_labels, test_size=0.3, random_state=1)
# 模型的超参数设置
input_size = train_pre.shape[1]
output_size = 1
num_epochs = 10000
learning_rate = 0.01
layers_dims = [input_size, 500, 500, output_size]
parameters, submission_name = model(X_train, Y_train, X_valid, Y_valid, layers_dims, num_epochs=num_epochs,
learning_rate=learning_rate, print_cost=True, plot_cost=True, l2_beta=10,
keep_prob=0.7, minibatch_size=0, return_best=True)
print(submission_name)
prediction = list(map(lambda val: float(val), predict(test_pre, parameters)))
# uncomment if label was log transformed
# prediction = list(map(lambda val: np.expm1(val), prediction))
# output_submission(test.Id.values, prediction, 'Id', 'SalePrice', submission_name)
# 保存结果
result = pd.DataFrame()
result['Id'] = test.Id.values
result['SalePrice'] = prediction
# index=False 是用来除去行编号
result.to_csv('/Users/liudong/Desktop/house_price/result1.csv', index=False)
print('##########结束训练##########')
================================================
FILE: src/py3.x/kaggle/getting-started/house-prices/housePredice_335.py
================================================
# -*- coding: utf-8 -*-
__author__ = 'liudong'
__date__ = '2018/4/23 下午2:28'
#import some necessary librairies
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# %matplotlib inline
import matplotlib.pyplot as plt # Matlab-style plotting
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import warnings
def ignore_warn(*args, **kwargs):
pass
# ignore annoying warning (from sklearn and seaborn)
warnings.warn = ignore_warn
from sklearn.preprocessing import LabelEncoder
from scipy import stats
from scipy.stats import norm, skew #for some statistics
from sklearn.linear_model import ElasticNet, Lasso, BayesianRidge, LassoLarsIC
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.kernel_ridge import KernelRidge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone
from sklearn.model_selection import KFold, cross_val_score, train_test_split
from sklearn.metrics import mean_squared_error
import xgboost as xgb
import lightgbm as lgb
# Limiting floats output to 3 decimal points
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))
from subprocess import check_output
# check the files available in the directory
# print(check_output(["ls", "/Users/liudong/Desktop/house_price/train.csv"]).decode("utf8"))
# 加载数据
train = pd.read_csv('/opt/data/kaggle/getting-started/house-prices/train.csv')
test = pd.read_csv('/opt/data/kaggle/getting-started/house-prices/test.csv')
# 查看训练数据的特征
print(train.head(5))
# 查看测试数据的特征
print(test.head(5))
# 查看数据的数量和特征值的个数
print("The train data size before dropping Id feature is : {} ".format(
train.shape))
print("The test data size before dropping Id feature is : {} ".format(
test.shape))
# Save the 'Id' colum
train_ID = train['Id']
test_ID = test['Id']
# Now drop the 'Id' colum since it's unnecessary for the prediction process.
train.drop("Id", axis=1, inplace=True)
test.drop("Id", axis=1, inplace=True)
#check again the data size after dropping the 'Id' variable
print("\nThe train data size after dropping Id feature is : {} ".format(
train.shape))
print(
"The test data size after dropping Id feature is : {} ".format(test.shape))
# 删除那些异常数据值
train = train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<300000)].index)
# We use the numpy fuction log1p which applies log(1+x) to all elements of the column
# log(1+x)来处理所有的数值
train["SalePrice"] = np.log1p(train["SalePrice"])
# 特征工程
# 把训练集和测试集的数据contact一起放置在DataFrame当中
# 0 代表行数
ntrain = train.shape[0]
ntest = test.shape[0]
# SalesPrice的值
y_train = train.SalePrice.values
all_data = pd.concat((train, test)).reset_index(drop=True)
all_data.drop(['SalePrice'], axis=1, inplace=True)
print("all_data size is : {}".format(all_data.shape))
# 处理缺失数据
all_data_na = (all_data.isnull().sum() / len(all_data)) * 100
all_data_na = all_data_na.drop(
all_data_na[all_data_na == 0].index).sort_values(ascending=False)[:30]
missing_data = pd.DataFrame({'Missing Ratio': all_data_na})
print(missing_data.head(20))
all_data["PoolQC"] = all_data["PoolQC"].fillna("None")
all_data["MiscFeature"] = all_data["MiscFeature"].fillna("None")
all_data["Alley"] = all_data["Alley"].fillna("None")
all_data["Fence"] = all_data["Fence"].fillna("None")
all_data["FireplaceQu"] = all_data["FireplaceQu"].fillna("None")
# 通过neighborhood进行分组，同时使用median来填充缺失数据
all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(
lambda x: x.fillna(x.median()))
# 根据数值的类型不同，选择不同的填充值
# 使用None来填充缺失值 fillna('None')
for col in ('GarageType', 'GarageFinish', 'GarageQual', 'GarageCond'):
all_data[col] = all_data[col].fillna('None')
# 使用0来填充缺失值 fillna(0)
for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):
all_data[col] = all_data[col].fillna(0)
for col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF',
'BsmtFullBath', 'BsmtHalfBath'):
all_data[col] = all_data[col].fillna(0)
for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1',
'BsmtFinType2'):
all_data[col] = all_data[col].fillna('None')
all_data["MasVnrType"] = all_data["MasVnrType"].fillna("None")
all_data["MasVnrArea"] = all_data["MasVnrArea"].fillna(0)
all_data['MSZoning'] = all_data['MSZoning'].fillna(
all_data['MSZoning'].mode()[0])
all_data = all_data.drop(['Utilities'], axis=1)
all_data["Functional"] = all_data["Functional"].fillna("Typ")
# mode() [0]对行取众数 [1]是对列取众数(这里没用到）
all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0])
all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0])
all_data['Exterior1st'] = all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode()[0])
all_data['Exterior2nd'] = all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode()[0])
all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0])
all_data['MSSubClass'] = all_data['MSSubClass'].fillna("None")
#检查数值是否还有缺失
all_data_na = (all_data.isnull().sum() / len(all_data)) * 100
all_data_na = all_data_na.drop(
all_data_na[all_data_na == 0].index).sort_values(ascending=False)
missing_data = pd.DataFrame({'Missing Ratio': all_data_na})
print(missing_data.head())
# 另外的特征工程
# Transforming some numerical variables that are really categorical
# MSSubClass=The building class
all_data['MSSubClass'] = all_data['MSSubClass'].apply(str)
# Changing OverallCond into a categorical variable
all_data['OverallCond'] = all_data['OverallCond'].astype(str)
# Year and month sold are transformed into categorical features.
all_data['YrSold'] = all_data['YrSold'].astype(str)
all_data['MoSold'] = all_data['MoSold'].astype(str)
cols = ('FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond',
'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1',
'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',
'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond',
'YrSold', 'MoSold')
# 使用 LabelEncoder 转换上述特征
for c in cols:
lbl = LabelEncoder()
lbl.fit(list(all_data[c].values))
all_data[c] = lbl.transform(list(all_data[c].values))
# shape
print('Shape all_data: {}'.format(all_data.shape))
# 增加更多重要的特征
# Adding total sqfootage feature
all_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data[
'1stFlrSF'] + all_data['2ndFlrSF']
# Skewed features
numeric_feats = all_data.dtypes[all_data.dtypes != "object"].index
# Check the skew of all numerical features
skewed_feats = all_data[numeric_feats].apply(
lambda x: skew(x.dropna())).sort_values(ascending=False)
print("\nSkew in numerical features: \n")
skewness = pd.DataFrame({'Skew': skewed_feats})
print(skewness.head(10))
# Box Cox Transformation of (highly) skewed features
# We use the scipy function boxcox1p which computes the Box-Cox transformation of 1+x .
# Note that setting λ=0 is equivalent to log1p used above for the target variable.
skewness = skewness[abs(skewness) > 0.75]
print("There are {} skewed numerical features to Box Cox transform".format(
skewness.shape[0]))
from scipy.special import boxcox1p
skewed_features = skewness.index
lam = 0.15
for feat in skewed_features:
# all_data[feat] += 1
all_data[feat] = boxcox1p(all_data[feat], lam)
# Getting dummy categorical features
all_data = pd.get_dummies(all_data)
print(all_data.shape)
# Getting the new train and test sets.
train = all_data[:ntrain]
test = all_data[ntrain:]
#Validation function
n_folds = 5
def rmsle_cv(model):
kf = KFold(
n_folds, shuffle=True, random_state=42).get_n_splits(train.values)
rmse = np.sqrt(-cross_val_score(
model, train.values, y_train, scoring="neg_mean_squared_error", cv=kf))
print("rmse", rmse)
return (rmse)
# 模型
# LASSO Regression :
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=1))
# Elastic Net Regression
ENet = make_pipeline(
RobustScaler(), ElasticNet(
alpha=0.0005, l1_ratio=.9, random_state=3))
# Kernel Ridge Regression
KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)
# Gradient Boosting Regression
GBoost = GradientBoostingRegressor(
n_estimators=3000,
learning_rate=0.05,
max_depth=4,
max_features='sqrt',
min_samples_leaf=15,
min_samples_split=10,
loss='huber',
random_state=5)
# XGboost
model_xgb = xgb.XGBRegressor(
colsample_bytree=0.4603,
gamma=0.0468,
learning_rate=0.05,
max_depth=3,
min_child_weight=1.7817,
n_estimators=2200,
reg_alpha=0.4640,
reg_lambda=0.8571,
subsample=0.5213,
silent=1,
random_state=7,
nthread=-1)
# lightGBM
model_lgb = lgb.LGBMRegressor(
objective='regression',
num_leaves=5,
learning_rate=0.05,
n_estimators=720,
max_bin=55,
bagging_fraction=0.8,
bagging_freq=5,
feature_fraction=0.2319,
feature_fraction_seed=9,
bagging_seed=9,
min_data_in_leaf=6,
min_sum_hessian_in_leaf=11)
# Base models scores
score = rmsle_cv(lasso)
print("\nLasso score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(ENet)
print("ElasticNet score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(KRR)
print(
"Kernel Ridge score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(GBoost)
print("Gradient Boosting score: {:.4f} ({:.4f})\n".format(score.mean(),
score.std()))
score = rmsle_cv(model_xgb)
print("Xgboost score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(model_lgb)
print("LGBM score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
# 模型融合
class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):
def __init__(self, models):
self.models = models
# we define clones of the original models to fit the data in
def fit(self, X, y):
self.models_ = [clone(x) for x in self.models]
# Train cloned base models
for model in self.models_:
model.fit(X, y)
return self
# Now we do the predictions for cloned models and average them
def predict(self, X):
predictions = np.column_stack(
[model.predict(X) for model in self.models_])
return np.mean(predictions, axis=1)
# 评价这四个模型的好坏
averaged_models = AveragingModels(models=(ENet, GBoost, KRR, lasso))
score = rmsle_cv(averaged_models)
print(" Averaged base models score: {:.4f} ({:.4f})\n".format(score.mean(),
score.std()))
class StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):
def __init__(self, base_models, meta_model, n_folds=5):
self.base_models = base_models
self.meta_model = meta_model
self.n_folds = n_folds
# We again fit the data on clones of the original models
def fit(self, X, y):
self.base_models_ = [list() for x in self.base_models]
self.meta_model_ = clone(self.meta_model)
kfold = KFold(n_splits=self.n_folds, shuffle=True, random_state=156)
# Train cloned base models then create out-of-fold predictions
# that are needed to train the cloned meta-model
out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models)))
for i, model in enumerate(self.base_models):
for train_index, holdout_index in kfold.split(X, y):
instance = clone(model)
self.base_models_[i].append(instance)
instance.fit(X[train_index], y[train_index])
y_pred = instance.predict(X[holdout_index])
out_of_fold_predictions[holdout_index, i] = y_pred
# Now train the cloned meta-model using the out-of-fold predictions as new feature
self.meta_model_.fit(out_of_fold_predictions, y)
return self
# Do the predictions of all base models on the test data and use the averaged predictions as
# meta-features for the final prediction which is done by the meta-model
def predict(self, X):
meta_features = np.column_stack([
np.column_stack([model.predict(X) for model in base_models]).mean(
axis=1) for base_models in self.base_models_
])
return self.meta_model_.predict(meta_features)
stacked_averaged_models = StackingAveragedModels(
base_models=(ENet, GBoost, KRR), meta_model=lasso)
score = rmsle_cv(stacked_averaged_models)
print("Stacking Averaged models score: {:.4f} ({:.4f})".format(score.mean(),
score.std()))
# define a rmsle evaluation function
def rmsle(y, y_pred):
return np.sqrt(mean_squared_error(y, y_pred))
# Final Training and Prediction
# StackedRegressor
stacked_averaged_models.fit(train.values, y_train)
stacked_train_pred = stacked_averaged_models.predict(train.values)
stacked_pred = np.expm1(stacked_averaged_models.predict(test.values))
print(rmsle(y_train, stacked_train_pred))
# XGBoost
model_xgb.fit(train, y_train)
xgb_train_pred = model_xgb.predict(train)
xgb_pred = np.expm1(model_xgb.predict(test))
print(rmsle(y_train, xgb_train_pred))
# lightGBM
model_lgb.fit(train, y_train)
lgb_train_pred = model_lgb.predict(train)
lgb_pred = np.expm1(model_lgb.predict(test.values))
print(rmsle(y_train, lgb_train_pred))
'''RMSE on the entire Train data when averaging'''
print('RMSLE score on train data:')
print(rmsle(y_train, stacked_train_pred * 0.70 + xgb_train_pred * 0.15 +
lgb_train_pred * 0.15))
# 模型融合的预测效果
ensemble = stacked_pred * 0.70 + xgb_pred * 0.15 + lgb_pred * 0.15
# 保存结果
result = pd.DataFrame()
result['Id'] = test_ID
result['SalePrice'] = ensemble
# index=False 是用来除去行编号
result.to_csv('/Users/liudong/Desktop/house_price/result.csv', index=False)
print('##########结束训练##########')
================================================
FILE: src/py3.x/kaggle/getting-started/house-prices/jiangheng_houseprice.py
================================================
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 16 17:12:06 2017
@author: Administrator
"""
import pandas as pd
import numpy as np
from pandas import DataFrame, Series
from scipy.stats import boxcox
from sklearn.linear_model import Ridge
import warnings
import os.path
warnings.filterwarnings('ignore')
data_dir = '/opt/data/kaggle/getting-started/house-prices'
# 这里对数据做一些转换,原因要么是某些类别个数太少而且分布相近,要么是特征内的值之间有较为明显的优先级
mapper = {
'LandSlope': {
'Gtl': 'Gtl',
'Mod': 'unGtl',
'Sev': 'unGtl'
},
'LotShape': {
'Reg': 'Reg',
'IR1': 'IR1',
'IR2': 'other',
'IR3': 'other'
},
'RoofMatl': {
'ClyTile': 'other',
'CompShg': 'CompShg',
'Membran': 'other',
'Metal': 'other',
'Roll': 'other',
'Tar&Grv': 'Tar&Grv',
'WdShake': 'WdShake',
'WdShngl': 'WdShngl'
},
'Heating': {
'GasA': 'GasA',
'GasW': 'GasW',
'Grav': 'Grav',
'Floor': 'other',
'OthW': 'other',
'Wall': 'Wall'
},
'HeatingQC': {
'Po': 1,
'Fa': 2,
'TA': 3,
'Gd': 4,
'Ex': 5
},
'KitchenQual': {
'Fa': 1,
'TA': 2,
'Gd': 3,
'Ex': 4
}
}
# 对结果影响很小,或者与其他特征相关性较高的特征将被丢弃
to_drop = [
'Id', 'Street', 'Utilities', 'Condition2', 'PoolArea', 'PoolQC', 'Fence',
'YrSold', 'MoSold', 'BsmtHalfBath', 'BsmtFinSF2', 'GarageQual', 'MiscVal',
'EnclosedPorch', '3SsnPorch', 'GarageArea', 'TotRmsAbvGrd', 'GarageYrBlt',
'BsmtFinType2', 'BsmtUnfSF', 'GarageCond', 'GarageFinish', 'FireplaceQu',
'BsmtCond', 'BsmtQual', 'Alley'
]
# 特渣工程之瞎搞特征,别问我思路是什么,纯属乱拍脑袋搞出来,而且对结果貌似也仅有一点点影响
'''
data['house_remod']: 重新装修的年份与房建年份的差值
data['livingRate']: LotArea查了下是地块面积,这个特征是居住面积/地块面积*总体评价
data['lot_area']: LotFrontage是与房子相连的街道大小,现在想了下把GrLivArea换成LotArea会不会好点?
data['room_area']: 房间数/居住面积
data['fu_room']: 带有浴室的房间占总房间数的比例
data['gr_room']: 卧室与房间数的占比
'''
def create_feature(data):
# 是否拥有地下室
hBsmt_index = data.index[data['TotalBsmtSF'] > 0]
data['HaveBsmt'] = 0
data.loc[hBsmt_index, 'HaveBsmt'] = 1
data['house_remod'] = data['YearRemodAdd'] - data['YearBuilt']
data['livingRate'] = (data['GrLivArea'] /
data['LotArea']) * data['OverallCond']
data['lot_area'] = data['LotFrontage'] / data['GrLivArea']
data['room_area'] = data['TotRmsAbvGrd'] / data['GrLivArea']
data['fu_room'] = data['FullBath'] / data['TotRmsAbvGrd']
data['gr_room'] = data['BedroomAbvGr'] / data['TotRmsAbvGrd']
def processing(data):
# 构造新特征
create_feature(data)
# 丢弃特征
data.drop(to_drop, axis=1, inplace=True)
# 填充None值,因为在特征说明中,None也是某些特征的一个值,所以对于这部分特征的缺失值以None填充
fill_none = ['MasVnrType', 'BsmtExposure', 'GarageType', 'MiscFeature']
for col in fill_none:
data[col].fillna('None', inplace=True)
# 对其他缺失值进行填充,离散型特征填充众数,数值型特征填充中位数
na_col = data.dtypes[data.isnull().any()]
for col in na_col.index:
if na_col[col] != 'object':
med = data[col].median()
data[col].fillna(med, inplace=True)
else:
mode = data[col].mode()[0]
data[col].fillna(mode, inplace=True)
# 对正态偏移的特征进行正态转换,numeric_col就是数值型特征,zero_col是含有零值的数值型特征
# 因为如果对含零特征进行转换的话会有各种各种的小问题,所以干脆单独只对非零数值进行转换
numeric_col = data.skew().index
zero_col = data.columns[data.isin([0]).any()]
for col in numeric_col:
# 对于那些condition特征,例如取值是0,1,2,3...那些我不作变换,因为意义不大
if len(pd.value_counts(data[col])) <= 10: continue
# 如果是含有零值的特征,则只对非零值变换,至于用哪种形式变换,boxcox会自动根据数据来调整
if col in zero_col:
trans_data = data[data > 0][col]
before = abs(trans_data.skew())
cox, _ = boxcox(trans_data)
log_after = abs(Series(cox).skew())
if log_after < before:
data.loc[trans_data.index, col] = cox
# 如果是非零值的特征,则全部作转换
else:
before = abs(data[col].skew())
cox, _ = boxcox(data[col])
log_after = abs(Series(cox).skew())
if log_after < before:
data.loc[:, col] = cox
# mapper值的映射转换
for col, mapp in mapper.items():
data.loc[:, col] = data[col].map(mapp)
df_train = pd.read_csv(os.path.join(data_dir, "train.csv"))
df_test = pd.read_csv(os.path.join(data_dir, "test.csv"))
test_ID = df_test['Id']
# 去除离群点
GrLivArea_outlier = set(df_train.index[(df_train['SalePrice'] < 200000) & (
df_train['GrLivArea'] > 4000)])
LotFrontage_outlier = set(df_train.index[df_train['LotFrontage'] > 300])
df_train.drop(LotFrontage_outlier | GrLivArea_outlier, inplace=True)
# 因为删除了几行数据,所以index的序列不再连续,需要重新reindex
df_train.reset_index(drop=True, inplace=True)
prices = np.log1p(df_train.loc[:, 'SalePrice'])
df_train.drop(['SalePrice'], axis=1, inplace=True)
# 这里对训练集和测试集进行合并,然后再进行特征工程
all_data = pd.concat([df_train, df_test])
all_data.reset_index(drop=True, inplace=True)
# 进行特征工程
processing(all_data)
# dummy转换
dummy = pd.get_dummies(all_data, drop_first=True)
# 试了Ridge,Lasso,ElasticNet以及GBM,发现ridge的表现比其他的都好,参数alpha=6是调参结果
ridge = Ridge(6)
ridge.fit(dummy.iloc[:prices.shape[0], :], prices)
result = np.expm1(ridge.predict(dummy.iloc[prices.shape[0]:, :]))
pre = DataFrame(result, columns=['SalePrice'])
prediction = pd.concat([test_ID, pre], axis=1)
prediction.to_csv(os.path.join(data_dir, "submission_1.csv"), index=False)
================================================
FILE: src/py3.x/kaggle/getting-started/titanic/introduction-to-ensemblingStacking-in-python.py
================================================
# -*- coding: utf-8 -*-
"""
Created on 2017-12-4 15:45:02
@Team: 瑶瑶亲卫队
"""
# 加载用到的库
import pandas as pd
import numpy as np
import re
import sklearn
import seaborn as sns
import matplotlib.pyplot as plt
import csv
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls
import warnings
warnings.filterwarnings('ignore')
# 打算使用 5 种 sklearn 中的模型来拟合
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier
from sklearn.svm import SVC
from sklearn.cross_validation import KFold
# 加载最初的 train 和 test 数据集
train = pd.read_csv('D:/titanic/titanic_dataset/train.csv')
test = pd.read_csv('D:/titanic/titanic_dataset/test.csv')
# 存储 test 数据集的PassengerId 以便后面生成 submission 文件
PassengerId = test['PassengerId']
# train.head(3)
# 整体的全部数据
full_data = [train, test]
# 一些从原始 features 中衍生出的 features ，我认为可以算是一个 feature
# Name_length 特征
train['Name_length'] = train['Name'].apply(len)
test['Name_length'] = test['Name'].apply(len)
# 在 Titanic 上是否具有一个 cabin
train['Has_Cabin'] = train["Cabin"].apply(lambda x: 0 if type(x) == float else 1)
test['Has_Cabin'] = test["Cabin"].apply(lambda x: 0 if type(x) == float else 1)
# 创建一个新的 feature FamilySize 作为 SibSp 和 Parch 的混合 features
for dataset in full_data:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
# 从 FamilySize 特征中 创建一个新的 feature IsAlone
for dataset in full_data:
dataset['IsAlone'] = 0
dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1
# 删除 Embarked 列中的所有的 NULLS值，使用 S 来填充（算是超级暴力的吧）
for dataset in full_data:
dataset['Embarked'] = dataset['Embarked'].fillna('S')
# 删除 Fare 中所有的 NULLS 值，并生成一个新的 feature 列 CategoricalFare
for dataset in full_data:
dataset['Fare'] = dataset['Fare'].fillna(train['Fare'].median())
train['CategoricalFare'] = pd.qcut(train['Fare'], 4)
# 创建一个新特征 CategoricalAge
for dataset in full_data:
age_avg = dataset['Age'].mean()
age_std = dataset['Age'].std()
age_null_count = dataset['Age'].isnull().sum()
age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)
dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list
dataset['Age'] = dataset['Age'].astype(int)
train['CategoricalAge'] = pd.cut(train['Age'], 5)
# 定义函数从 passenger 名字中获取 titles
def get_title(name):
title_search = re.search(' ([A-Za-z]+)\.', name)
# 如果 title 存在，返回
if title_search:
return title_search.group(1)
return ""
# 创建一个新的特征 Title, 包含乘客的名字的 titles
for dataset in full_data:
dataset['Title'] = dataset['Name'].apply(get_title)
# 将所有的非常见的 title 分类到一个 “Rare” 特征
for dataset in full_data:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
# 将数据集中的数据映射成离散型数据
for dataset in full_data:
# 映射 Sex
dataset['Sex'] = dataset['Sex'].map( {'female': 0, 'male': 1} ).astype(int)
# 映射 titles
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
# 映射 Embarked
dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)
# 映射 Fare
dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0
dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1
dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2
dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3
dataset['Fare'] = dataset['Fare'].astype(int)
# 映射 Age
dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0
dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1
dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2
dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3
dataset.loc[ dataset['Age'] > 64, 'Age'] = 4
# train.head(3)
# 生成一个临时文件，专门存储我们已经转化/映射完成之后的 train 和 test 文件，我写的很烂，，，
def saveTmpTrainFile(tmpFile,csvName):
with open(csvName, 'w', newline='') as myFile:
myWriter=csv.writer(myFile)
myWriter.writerow(["Survived","Pclass","Sex","Age","Parch","Fare","Embarked","Name_length","Has_cabin","FamilySize","IsAlone","Title"])
for lines in tmpFile.index:
tmp=[]
tmp.append(tmpFile.loc[lines].values[1])
tmp.append(tmpFile.loc[lines].values[2])
tmp.append(tmpFile.loc[lines].values[4])
tmp.append(tmpFile.loc[lines].values[5])
tmp.append(tmpFile.loc[lines].values[7])
tmp.append(tmpFile.loc[lines].values[9])
tmp.append(tmpFile.loc[lines].values[11])
tmp.append(tmpFile.loc[lines].values[12])
tmp.append(tmpFile.loc[lines].values[13])
tmp.append(tmpFile.loc[lines].values[14])
tmp.append(tmpFile.loc[lines].values[15])
tmp.append(tmpFile.loc[lines].values[-1])
myWriter.writerow(tmp)
saveTmpTrainFile(train,'D:/titanic/titanic_dataset/train_later.csv')
# 生成一个临时文件，专门存储我们已经转化/映射完成之后的 train 和 test 文件
def saveTmpTestFile(tmpFile,csvName):
with open(csvName, 'w', newline='') as myFile:
myWriter=csv.writer(myFile)
myWriter.writerow(["Pclass","Sex","Age","Parch","Fare","Embarked","Name_length","Has_cabin","FamilySize","IsAlone","Title"])
for lines in tmpFile.index:
tmp=[]
tmp.append(tmpFile.loc[lines].values[1])
tmp.append(tmpFile.loc[lines].values[3])
tmp.append(tmpFile.loc[lines].values[4])
tmp.append(tmpFile.loc[lines].values[6])
tmp.append(tmpFile.loc[lines].values[8])
tmp.append(tmpFile.loc[lines].values[10])
tmp.append(tmpFile.loc[lines].values[11])
tmp.append(tmpFile.loc[lines].values[12])
tmp.append(tmpFile.loc[lines].values[13])
tmp.append(tmpFile.loc[lines].values[14])
tmp.append(tmpFile.loc[lines].values[15])
myWriter.writerow(tmp)
# 存储 test 文件
saveTmpFile(test,'D:/titanic/titanic_dataset/test_later.csv')
# # ------------------------------------------------------------------------
# 加载我们生成的 train 和 test 文件
# # -----------注意一下哈，这个地方，需要调整一下生成的文件-------------------------------------------------------------
train_later = pd.read_csv('D:/titanic/titanic_dataset/train_later.csv')
test_later = pd.read_csv('D:/titanic/titanic_dataset/test_later.csv')
# train.head(3)
# test.head(3)
# # 使用 seaborn 将 特征的 Person 系数画出来
# colormap = plt.cm.RdBu
# plt.figure(figsize=(14,12))
# plt.title('Pearson Correlation of Features', y=1.05, size=15)
# sns.heatmap(train.astype(float).corr(),linewidths=0.1,vmax=1.0,
# square=True, cmap=colormap, linecolor='white', annot=True)
# 一些比较有用参数，后边会派上用场
ntrain = train_later.shape[0]
ntest = test_later.shape[0]
SEED = 0 # 重现性
NFOLDS = 5 # 设置交叉验证的折数，以便后面的 K 折交叉验证
kf = KFold(ntrain, n_folds= NFOLDS, random_state=SEED)
# sklearn 的分类器
class SklearnHelper(object):
def __init__(self, clf, seed=0, params=None):
params['random_state'] = seed
self.clf = clf(**params)
def train(self, x_train, y_train):
self.clf.fit(x_train, y_train)
def predict(self, x):
return self.clf.predict(x)
def fit(self,x,y):
return self.clf.fit(x,y)
def feature_importances(self,x,y):
print(self.clf.fit(x,y).feature_importances_)
# 获取最后的 预测结果
def get_oof(clf, x_train, y_train, x_test):
oof_train = np.zeros((ntrain,))
oof_test = np.zeros((ntest,))
oof_test_skf = np.empty((NFOLDS, ntest))
for i, (train_index, test_index) in enumerate(kf):
x_tr = x_train[train_index]
y_tr = y_train[train_index]
x_te = x_train[test_index]
clf.train(x_tr, y_tr)
oof_train[test_index] = clf.predict(x_te)
oof_test_skf[i, :] = clf.predict(x_test)
oof_test[:] = oof_test_skf.mean(axis=0)
return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)
# 将参数加载到调用的分类器中，进行调试
# Random Forest 的参数
rf_params = {
'n_jobs': -1,
'n_estimators': 500,
'warm_start': True,
#'max_features': 0.2,
'max_depth': 6,
'min_samples_leaf': 2,
'max_features' : 'sqrt',
'verbose': 0
}
# Extra Trees 的参数
et_params = {
'n_jobs': -1,
'n_estimators':500,
#'max_features': 0.5,
'max_depth': 8,
'min_samples_leaf': 2,
'verbose': 0
}
# AdaBoost 的参数
ada_params = {
'n_estimators': 500,
'learning_rate' : 0.75
}
# Gradient Boosting 的参数
gb_params = {
'n_estimators': 500,
#'max_features': 0.2,
'max_depth': 5,
'min_samples_leaf': 2,
'verbose': 0
}
# Support Vector Classifier 的参数
svc_params = {
'kernel' : 'linear',
'C' : 0.025
}
# 创建代表模型的 5 个对象
rf = SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=rf_params)
et = SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)
ada = SklearnHelper(clf=AdaBoostClassifier, seed=SEED, params=ada_params)
gb = SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=gb_params)
svc = SklearnHelper(clf=SVC, seed=SEED, params=svc_params)
# 创建 Numpy arrays 来代表 train, test 和 target
y_train = train['Survived'].ravel()
train = train.drop(['Survived'], axis=1)
x_train = train.values # 创建 train 的 array
x_test = test.values # 创建 test 的 array
# 创建您的 OOF 的 train 和 test 预测
et_oof_train, et_oof_test = get_oof(et, x_train, y_train, x_test) # Extra Trees
rf_oof_train, rf_oof_test = get_oof(rf,x_train, y_train, x_test) # Random Forest
ada_oof_train, ada_oof_test = get_oof(ada, x_train, y_train, x_test) # AdaBoost
gb_oof_train, gb_oof_test = get_oof(gb,x_train, y_train, x_test) # Gradient Boost
svc_oof_train, svc_oof_test = get_oof(svc,x_train, y_train, x_test) # Support Vector Classifier
print("Training is complete")
# 创建 submission 文件，接下来就是提交到 kaggle 页面看一下得分和排名啦
def saveResult(result,csvName):
with open(csvName,'w',newline='') as myFile:
myWriter=csv.writer(myFile)
myWriter.writerow(["PassengerId","Survived"])
index=891
for i in result:
tmp=[]
index=index+1
tmp.append(index)
tmp.append(int(i))
myWriter.writerow(tmp)
saveResult(et_oof_test,'D:/titanic/titanic_dataset/result/et.csv')
saveResult(rf_oof_test,'D:/titanic/titanic_dataset/result/rf.csv')
saveResult(ada_oof_test,'D:/titanic/titanic_dataset/result/ada.csv')
saveResult(gb_oof_test,'D:/titanic/titanic_dataset/result/gb.csv')
saveResult(svc_oof_test,'D:/titanic/titanic_dataset/result/svc.csv')
================================================
FILE: src/py3.x/kaggle/getting-started/titanic/titanic-python3.6.py
================================================
#!/usr/bin/python
# coding: utf-8
'''
Created on 2019-08-14
Update on 2019-08-31
Author: 片刻
Github: https://github.com/apachecn/Interview
'''
import re
import datetime
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.decomposition import PCA
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import KFold, cross_val_score, train_test_split
from xgboost import XGBClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, VotingClassifier
# 加载数据
def opencsv():
root_path = '/opt/data/kaggle/getting-started/titanic'
tr_data = pd.read_csv('%s/%s' % (root_path, 'input/train.csv'), header=0)
te_data = pd.read_csv('%s/%s' % (root_path, 'input/test.csv'), header=0)
# print(tr_data.head(5))
# print(tr_data.info())
# # 返回数值型变量的统计量
# print(tr_data.describe())
# 数据预处理（清洗、缺失值）
do_DataPreprocessing(tr_data)
# print(tr_data.head(5))
# print(tr_data.dtypes)
# print(te_data.describe())
do_DataPreprocessing(te_data)
# print(te_data.head(5))
# print(te_data.dtypes)
# print(te_data.describe())
# # 相关性分析
# # 相关性协方差表, corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关.
# train_corr = tr_data.corr()
# print(train_corr)
# # 画出相关性热力图
# a = plt.subplots(figsize=(15,9))#调整画布大小
# a = sns.heatmap(train_corr, vmin=-1, vmax=1 , annot=True , square=True) #画热力图
# plt.show()
# """
# Survived Pclass Sex Age Parch Fare Embarked Title NameLength
# Survived 1.000000 -0.338481 0.543351 -0.064910 0.081629 0.257307 0.106811 0.354072 0.332350
# Pclass -0.338481 1.000000 -0.131900 -0.339898 0.018443 -0.549500 0.045702 -0.211552 -0.220001
# Sex 0.543351 -0.131900 1.000000 -0.081163 0.245489 0.182333 0.116569 0.419760 0.448759
# Age -0.064910 -0.339898 -0.081163 1.000000 -0.172482 0.096688 -0.009165 -0.037174 0.039702
# Parch 0.081629 0.018443 0.245489 -0.172482 1.000000 0.216225 -0.078665 0.235164 0.252282
# Fare 0.257307 -0.549500 0.182333 0.096688 0.216225 1.000000 0.062142 0.122872 0.155832
# Embarked 0.106811 0.045702 0.116569 -0.009165 -0.078665 0.062142 1.000000 0.055788 -0.107749
# Title 0.354072 -0.211552 0.419760 -0.037174 0.235164 0.122872 0.055788 1.000000 0.436099
# NameLength 0.332350 -0.220001 0.448759 0.039702 0.252282 0.155832 -0.107749 0.436099 1.000000
# """
# 对于PessengerId 忽略，这个是自增长没意义
pids = te_data['PassengerId'].tolist()
tr_data.drop(['PassengerId'], axis=1,inplace=True)
te_data.drop(['PassengerId'], axis=1,inplace=True)
train_data = tr_data.values[:, 1:] # 读入全部训练数据, [行，列]
train_label = tr_data.values[:, 0] # 读取列表的第一列
test_data = te_data.values[:, :] # 测试全部测试个数据
return train_data, train_label, test_data, pids
def do_DataPreprocessing(titanic):
"""
| Survival | 生存 | 0 = No, 1 = Yes |
| Pclass | 票类别-社会地位 | 1 = 1st, 2 = 2nd, 3 = 3rd |
| Name | 姓名 | |
| Sex | 性别 | |
| Age | 年龄 | |
| SibSp | 船上的兄弟姐妹/配偶 | |
| Parch | 船上的父母/孩子的数量 | |
| Ticket | 票号 | |
| Fare | 乘客票价 | |
| Cabin | 客舱号码 | |
| Embarked | 登船港口 | C=Cherbourg, Q=Queenstown, S=Southampton |
>>> print(titanic.describe())
PassengerId Survived Pclass Age SibSp Parch Fare
count 891.000000 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000
mean 446.000000 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208
std 257.353842 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429
min 1.000000 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000
25% 223.500000 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400
50% 446.000000 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200
75% 668.500000 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000
max 891.000000 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200
Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 S
1 Cumings, Mrs. John Bradley female 38 1 0 PC 17599 71.2833 C85 C
"""
# 组合特征(特征组合相关性变差了)
# titanic["FamilySize"] = titanic["SibSp"] + titanic["Parch"]
# 对缺失值处理（Age 中位数不错）
titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())
titanic["Fare"] = titanic["Fare"].fillna(titanic["Fare"].median())
# 对文本特征进行处理（性别， 登船港口）
# print(titanic["Sex"].unique())
titanic.loc[titanic["Sex"]=="male", "Sex"] = 0
titanic.loc[titanic["Sex"]=="female", "Sex"] = 1
# S的概率最大，当然我们也可以按照概率随机算，都可以
# print(titanic["Embarked"].unique())
"""
titanic[["Embarked"]].groupby("Embarked").agg({"Embarked": "count"})
Embarked
Embarked
C 168
Q 77
S 644
"""
titanic["Embarked"] = titanic["Embarked"].fillna('S')
titanic.loc[titanic["Embarked"] == "S", "Embarked"] = 0
titanic.loc[titanic["Embarked"] == "C", "Embarked"] = 1
titanic.loc[titanic["Embarked"] == "Q", "Embarked"] = 2
def get_title(name):
# 名字的尊称
title_search = re.search(' ([A-Za-z]+)\.', name)
if title_search:
return title_search.group(1)
return ""
titles = titanic["Name"].apply(get_title)
# print(pd.value_counts(titles))
# 对尊称建立mapping字典
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Dr": 5, "Rev": 6, "Major": 7, "Col": 7, "Mlle": 8, "Mme": 8, "Don": 9, "Dona": 9, "Lady": 10, "Countess": 10, "Jonkheer": 10, "Sir": 9, "Capt": 7, "Ms": 2}
for k, v in title_mapping.items():
titles[titles == k] = v
# print(pd.value_counts(titles))
# 添加一个新特征表示拥护尊称
titanic["Title"] = [int(i) for i in titles.values.tolist()]
# 添加一个新特征表示名字长度
titanic["NameLength"] = titanic["Name"].apply(lambda x: len(x))
# 相关性太差，删除
# titanic.drop(['PassengerId'], axis=1,inplace=True)
titanic.drop(['Cabin'], axis=1,inplace=True)
titanic.drop(['SibSp'], axis=1,inplace=True)
# titanic.drop(['Parch'],axis=1,inplace=True)
titanic.drop(['Ticket'], axis=1,inplace=True)
titanic.drop(['Name'], axis=1,inplace=True)
def do_FeatureEngineering(data, COMPONENT_NUM=0.9):
# scale values 归一化
scaler = preprocessing.StandardScaler()
s_data = scaler.fit_transform(data)
return s_data
# # 降维(不降维，准确率还上升了)
# '''
# 使用说明：https://www.cnblogs.com/pinard/p/6243025.html
# n_components>=1
# n_components=NUM 设置占特征数量比
# 0 < n_components < 1
# n_components=0.99 设置阈值总方差占比
# '''
# pca = PCA(n_components=COMPONENT_NUM, whiten=False)
# # 只训练 训练集数据
# pca.fit(s_data) # Fit the model with X
# # 训练集 和 测试集 保持一直，进行 transform
# pca_data = pca.transform(s_data) # Fit the model with X and 在X上完成降维.
# # pca 方差大小、方差占比、特征数量
# # print("方差大小:\n", pca.explained_variance_, "方差占比:\n", pca.explained_variance_ratio_)
# print("特征数量: %s" % pca.n_components_)
# print("总方差占比: %s" % sum(pca.explained_variance_ratio_))
# return pca_data
def trainModel(trainData, trainLabel):
# 模拟测试
# 0.881994680700563 [0.87480519 0.91168831 0.89090909 0.85294118 0.87962963]
# model = LogisticRegression(random_state=1)
# 0.8819594261947202 [0.87532468 0.91272727 0.89298701 0.84912854 0.87962963]
# model = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2)
# 0.8812371898254252 [0.87428571 0.90857143 0.89402597 0.84803922 0.88126362]
# model = RandomForestClassifier(random_state=1, n_estimators=50, min_samples_split=8, min_samples_leaf=4)
# # 网格搜索 #####
# from sklearn.model_selection import GridSearchCV
# param_test = {
# # 'n_estimators': np.arange(190, 240, 2),
# 'max_depth': np.arange(4, 7, 1),
# 'learning_rate': np.array([0.01, 0.03, 0.05, 0.08, 0.1, 0.15, 0.2]),
# 'n_estimators': np.array([222]),
# # 'max_depth': np.array([4]),
# # 'learning_rate': np.array([0.01, 0.02, 0.03, 0.04, 0.05]),
# # 'min_child_weight': np.arange(1, 6, 2),
# # 'C': (1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9)
# }
# 0.8294499549079417 [0.82122905 0.80446927 0.86516854 0.82022472 0.83615819]
# model = XGBClassifier()
# grid_search = GridSearchCV(estimator=model, param_grid=param_test, scoring='roc_auc', cv=5)
# grid_search.fit(trainData, trainLabel)
# print("最优得分 >>>", grid_search.best_score_)
# print("最优参数 >>>", grid_search.best_params_)
# model = XGBClassifier(learning_rate = 0.1, n_estimators= 202, max_depth= 4,
# min_child_weight= 5, gamma=0, subsample=0.8, colsample_bytree=0.8,
# objective= 'binary:logistic', scale_pos_weight=1
# )
# 0.8825292702939761 [0.87272727 0.91376623 0.89194805 0.84912854 0.88507625]
# 0.8812472625413802 [0.87480519 0.91012987 0.89090909 0.85239651 0.87799564]
# model = XGBClassifier(n_estimators=222, max_depth=4, learning_rate=0.03)
# scores = cross_val_score(model, trainData, trainLabel, cv=5, scoring='roc_auc')
# print(scores.mean(), "\n", scores)
print("模型融合")
"""
Bagging: 同一模型的投票选举
Boosting: 同一模型的再学习
Voting: 不同模型的投票选举
Stacking: 分层预测 – K-1份数据预测1份模型拼接，得到 预测结果*算法数（作为特征） => 从而预测最终结果
Blending: 分层预测 – 将数据分成2部分（A部分训练B部分得到预测结果），得到 预测结果*算法数（作为特征） => 从而预测最终结果
"""
# 1. Bagging 算法实现
# 0.8818756755227344 [0.87324675 0.91428571 0.89090909 0.85076253 0.88017429]
# model = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2)
# 2. Boosting 算法实现
# 0.8488710896477386 [0.8198946 0.82285903 0.87780749 0.84906417 0.87473017]
# model = AdaBoostClassifier(random_state=1, n_estimators=100, learning_rate=1)
# 3. Voting
# 0.8815388054211584 [0.87480519 0.91272727 0.89194805 0.84912854 0.87908497]
# model = VotingClassifier(
# estimators=[
# ('log_clf', LogisticRegression()),
# ('ab_clf', AdaBoostClassifier()),
# ('svm_clf', SVC(probability=True)),
# ('rf_clf', RandomForestClassifier()),
# ('gbdt_clf', GradientBoostingClassifier()),
# ('rb_clf', AdaBoostClassifier())
# ], voting='soft') # , voting='hard')
# scores = cross_val_score(model, trainData, trainLabel, cv=5, scoring='roc_auc')
# print(scores.mean(), "\n", scores)
# # 4. Stacking
# # 0.8813662677192088 [0.87532468 0.90805195 0.89142857 0.85348584 0.87854031]
# clfs = [
# AdaBoostClassifier(),
# SVC(probability=True),
# AdaBoostClassifier(),
# LogisticRegression(C=0.1,max_iter=100),
# XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),
# RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),
# GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)
# ]
# kf = KFold(n_splits=5, shuffle=True, random_state=1)
# # 创建零矩阵
# dataset_stacking_train = np.zeros((trainData.shape[0], len(clfs)))
# # dataset_stacking_label = np.zeros((trainLabel.shape[0], len(clfs)))
# for j, clf in enumerate(clfs):
# '''依次训练各个单模型'''
# for i,(train, test) in enumerate(kf.split(trainLabel)):
# '''使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。'''
# # print("Fold", i)
# X_train, y_train, X_test, y_test = trainData[train], trainLabel[train], trainData[test], trainLabel[test]
# clf.fit(X_train, y_train)
# y_submission = clf.predict_proba(X_test)[:, 1]
# # j 表示每一次的算法，而 test是交叉验证得到的每一行（也就是每一个算法把测试机和都预测了一遍）
# dataset_stacking_train[test, j] = y_submission
# # 用建立第二层模型
# model = LogisticRegression(C=0.1, max_iter=100)
# model.fit(dataset_stacking_train, trainLabel)
# scores = cross_val_score(model, dataset_stacking_train, trainLabel, cv=5, scoring='roc_auc')
# print(scores.mean(), "\n", scores)
# 5. Blending
# 0.8838950287185581 [0.87584416 0.91064935 0.89714286 0.85294118 0.8828976 ]
clfs = [
AdaBoostClassifier(),
SVC(probability=True),
AdaBoostClassifier(),
LogisticRegression(C=0.1,max_iter=100),
XGBClassifier(max_depth=6,n_estimators=100,num_round = 5),
RandomForestClassifier(n_estimators=100,max_depth=6,oob_score=True),
GradientBoostingClassifier(learning_rate=0.3,max_depth=6,n_estimators=100)
]
X_d1, X_d2, y_d1, y_d2 = train_test_split(trainData, trainLabel, test_size=0.5, random_state=2017)
dataset_d1 = np.zeros((X_d2.shape[0], len(clfs)))
dataset_d2 = np.zeros((trainLabel.shape[0], len(clfs)))
for j, clf in enumerate(clfs):
#依次训练各个单模型
# 对于测试集，直接用这k个模型的预测值作为新的特征。
clf.fit(X_d1, y_d1)
dataset_d1[:, j] = clf.predict_proba(X_d2)[:, 1]
# 用建立第二层模型
model = LogisticRegression(C=0.1, max_iter=100)
model.fit(dataset_d1, y_d2)
scores = cross_val_score(model, dataset_d1, y_d2, cv=5, scoring='roc_auc')
print(scores.mean(), "\n", scores)
return model
def main():
# 开始时间
sta_time = datetime.datetime.now()
# 1.加载数据和预处理
train_data, train_label, test_data, pids = opencsv()
# 2. 特征工程
pca_tr_data = do_FeatureEngineering(train_data)
pca_te_data = do_FeatureEngineering(test_data)
# 3. 模型训练/模型融合（分类问题： lr、rf、adboost、xgboost、lightgbm）
model = trainModel(pca_tr_data, train_label)
model.fit(pca_tr_data, train_label)
labels = model.predict(pca_te_data)
# 4. 数据导出
print(type(pids), type(labels.tolist()))
result = pd.DataFrame({
'PassengerId': pids,
'Survived': [int(i) for i in labels.tolist()]
})
result.to_csv('Result_titanic.csv', index=False)
# 结束时间
end_time = datetime.datetime.now()
times = (end_time - sta_time).seconds
print("\n运行时间: %ss == %sm == %sh\n\n" % (times, times/60, times/60/60))
if __name__ == "__main__":
main()
================================================
FILE: src/py3.x/kaggle/getting-started/titanic/titanic.py
================================================
# -*- coding: utf-8 -*-
"""
Created on 2017-12-07
Update on 2017-12-24
Team: 一把梭
Github: https://github.com/apachecn/kaggle
"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
# 0. 数据读入及预处理
root_path = 'datasets/getting-started/titanic/input'
data_train = pd.read_csv('%s/%s' % (root_path, 'train.csv'))
# data_train.info()
# print(data_train.describe())
# 1. 去除唯一属性特
data_train.drop(['PassengerId', 'Ticket'], axis=1, inplace=True)
# 2. 类别特征One-Hot编码
data_train['Sex'] = data_train['Sex'].map({'female': 0, 'male': 1}).astype(np.int64)
data_train.loc[data_train.Embarked.isnull(), 'Embarked'] = 'S' # 2个Embarked缺失值直接填充为S
data_train = pd.concat([data_train, pd.get_dummies(data_train.Embarked)], axis=1)
data_train = data_train.rename(columns={'C': 'Cherbourg','Q': 'Queenstown','S': 'Southampton'})
# 将名字转换
def replace_name(x):
if 'Mrs' in x: return 'Mrs'
elif 'Mr' in x: return 'Mr'
elif 'Miss' in x: return 'Miss'
else: return 'Other'
data_train['Name'] = data_train['Name'].map(lambda x:replace_name(x))
data_train = pd.concat([data_train, pd.get_dummies(data_train.Name)], axis=1)
data_train = data_train.rename(columns={'Miss': 'Name_Miss','Mr': 'Name_Mr',
'Mrs': 'Name_Mrs','Other': 'Name_Other'})
# 3. 数值特征标准化
def fun_scale(df_feature):
np_feature = df_feature.values.reshape(-1,1).astype(np.float64)
feature_scale = StandardScaler().fit(np_feature)
feature_scaled = StandardScaler().fit_transform(np_feature, feature_scale)
return feature_scale, feature_scaled
Pclass_scale, data_train['Pclass_scaled'] = fun_scale(data_train['Pclass'])
Fare_scale, data_train['Fare_scaled'] = fun_scale(data_train['Fare'])
SibSp_scale, data_train['SibSp_scaled'] = fun_scale(data_train['SibSp'])
Parch_scale, data_train['Parch_scaled'] = fun_scale(data_train['Parch'])
# 4. 缺失值补全及相应处理
# 处理Age缺失值并标准化
# 缺失值处理函数
def set_missing_feature(train_for_missingkey, data, info):
known_feature = train_for_missingkey[train_for_missingkey.Age.notnull()].as_matrix()
unknown_feature = train_for_missingkey[train_for_missingkey.Age.isnull()].as_matrix()
y = known_feature[:, 0] # 第1列作为待补全属性
x = known_feature[:, 1:] # 第2列及之后的属性作为预测属性
rf = RandomForestRegressor(random_state=0, n_estimators=100)
rf.fit(x, y)
print(info, "缺失值预测得分", rf.score(x, y))
predictage = rf.predict(unknown_feature[:, 1:])
data.loc[data.Age.isnull(), 'Age'] = predictage
return data
train_for_missingkey_train = data_train[['Age','Survived','Sex','Name_Miss','Name_Mr','Name_Mrs',
'Name_Other','Fare_scaled','SibSp_scaled','Parch_scaled']]
data_train = set_missing_feature(train_for_missingkey_train, data_train,'Train_Age')
Age_scale, data_train['Age_scaled'] = fun_scale(data_train['Age'])
# 处理Cabin特征
def set_Cabin_type(df):
df.loc[ (df.Cabin.notnull()), 'Cabin' ] = 1.
df.loc[ (df.Cabin.isnull()), 'Cabin' ] = 0.
return df
data_train = set_Cabin_type(data_train)
# 5. 整合数据
train_X = data_train[['Sex','Cabin','Cherbourg','Queenstown','Southampton','Name_Miss','Name_Mr','Name_Mrs','Name_Other',
'Pclass_scaled','Fare_scaled','SibSp_scaled','Parch_scaled','Age_scaled']].as_matrix()
train_y = data_train['Survived'].as_matrix()
# 6. 模型搭建及交叉验证
lr = LogisticRegression(C=1.0, tol=1e-6)
svc = SVC(C=1.1, kernel='rbf', decision_function_shape='ovo')
adaboost = AdaBoostClassifier(n_estimators=490, random_state=0)
randomf = RandomForestClassifier(n_estimators=185, max_depth=5, random_state=0)
gbdt = GradientBoostingClassifier(n_estimators=436, max_depth=2, random_state=0)
VotingC = VotingClassifier(estimators=[('LR',lr),('SVC',svc),('AdaBoost',adaboost),
('RandomF',randomf),('GBDT',gbdt)])
'''
# 交叉验证部分 #####
param_test = {
'n_estimators': np.arange(200, 240, 1),
'max_depth': np.arange(4, 7, 1),
#'min_child_weight': np.arange(1, 6, 2),
#'C': (1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9)
}
from sklearn.grid_search import GridSearchCV
grid_search = GridSearchCV(estimator=xgbClassifier, param_grid=param_test, scoring='roc_auc', cv=5)
grid_search.fit(train_X,train_y)
grid_search.grid_scores_, grid_search.best_params_, grid_search.best_score_
# 交叉验证部分 #####
'''
# 模型训练及交叉验证
classifierlist = [('LR',lr),('SVC',svc),('AdaBoost',adaboost),('RandomF',randomf),
('GBDT',gbdt),('VotingC',VotingC)]
for name, classifier in classifierlist:
# 分类器训练与下一步交叉验证无关，训练是为下面测试集预测使用
classifier.fit(train_X, train_y)
print(name, "Mean_Cross_Val_Score is:",
cross_val_score(classifier, train_X, train_y, cv=5, scoring='accuracy').mean(), "\n")
# 7. 测试集处理
data_test = pd.read_csv('test.csv')
data_test.drop(['Ticket'], axis=1, inplace=True)
data_test['Sex'] = data_test['Sex'].map({'female': 0, 'male': 1}).astype(np.int64)
data_test = pd.concat([data_test, pd.get_dummies(data_test.Embarked)], axis=1)
data_test = data_test.rename(columns={'C': 'Cherbourg','Q': 'Queenstown','S': 'Southampton'})
data_test['Name'] = data_test['Name'].map(lambda x:replace_name(x))
data_test = pd.concat([data_test, pd.get_dummies(data_test.Name)], axis=1)
data_test = data_test.rename(columns={'Miss': 'Name_Miss','Mr': 'Name_Mr',
'Mrs': 'Name_Mrs','Other': 'Name_Other'})
# 测试集标准化函数
def fun_test_scale(feature_scale, df_feature):
np_feature = df_feature.values.reshape(-1,1).astype(np.float64)
feature_scaled = StandardScaler().fit_transform(np_feature, feature_scale)
return feature_scaled
data_test['Pclass_scaled'] = fun_test_scale(Pclass_scale, data_test['Pclass'])
data_test.loc[data_test.Fare.isnull(),'Fare'] = 0 # 缺失值置为0
data_test['Fare_scaled'] = fun_test_scale(Fare_scale, data_test['Fare'])
data_test['SibSp_scaled'] = fun_test_scale(SibSp_scale, data_test['SibSp'])
data_test['Parch_scaled'] = fun_test_scale(Parch_scale, data_test['Parch'])
# 处理测试集Age缺失值并归一化
train_for_missingkey_test = data_test[['Age','Sex','Name_Miss','Name_Mr','Name_Mrs','Name_Other',
'Fare_scaled','SibSp_scaled','Parch_scaled']]
data_test = set_missing_feature(train_for_missingkey_test, data_test, 'Test_Age')
data_test['Age_scaled'] = fun_test_scale(Age_scale, data_test['Age'])
data_test = set_Cabin_type(data_test)
test_X = data_test[['Sex','Cabin','Cherbourg','Queenstown','Southampton','Name_Miss','Name_Mr','Name_Mrs','Name_Other',
'Pclass_scaled','Fare_scaled','SibSp_scaled','Parch_scaled','Age_scaled']].as_matrix()
# 8. 模型预测
model = classifierlist[4] # 选择分类器
print("Test in %s!" % model[0])
predictions = model[1].predict(test_X).astype(np.int32)
result = pd.DataFrame({'PassengerId':data_test['PassengerId'].as_matrix(), 'Survived':predictions})
result.to_csv('Result_with_%s.csv' % model[0], index=False)
print('...\nAll Finish!')
# 9. XGBoost
import xgboost as xgb
from sklearn.model_selection import train_test_split
x_train, x_valid, y_train, y_valid = train_test_split(train_X, train_y, test_size=0.1, random_state=0)
print(x_train.shape, x_valid.shape)
xgbClassifier = xgb.XGBClassifier(learning_rate = 0.1,
n_estimators= 234,
max_depth= 6,
min_child_weight= 5,
gamma=0,
subsample=0.8,
colsample_bytree=0.8,
objective= 'binary:logistic',
scale_pos_weight=1)
xgbClassifier.fit(train_X, train_y)
xgbpred_test = xgbClassifier.predict(test_X).astype(np.int32)
result = pd.DataFrame({'PassengerId':data_test['PassengerId'].as_matrix(), 'Survived':xgbpred_test})
result.to_csv('Result_with_%s.csv' % 'XGBoost', index=False)
================================================
FILE: src/py3.x/kaggle/getting-started/word2vec-nlp-tutorial/delete-tmp.py
================================================
# coding: utf-8
from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
iris = datasets.load_iris()
parameters = {"kernel": ("linear", "rbf"), "C": range(1, 10)}
svr = svm.SVC()
'''
clf.fit(): 运行网格搜索
grid_scores_: 给出不同参数情况下的评价结果
best_params_: 描述了已取得最佳结果的参数的组合
best_score_: 成员提供优化过程期间观察到的最好的评分
'''
clf = GridSearchCV(svr, parameters)
clf.fit(iris.data, iris.target)
print(clf.grid_scores_) # 所有情况的评价结果
print(clf.best_params_) # 最好的参数
print(clf.best_score_) # 最好的参数的平均得分
================================================
FILE: src/py3.x/kaggle/getting-started/word2vec-nlp-tutorial/test.py
================================================
#!/usr/bin/python
# coding: utf-8
'''
Created on 2017-12-26
Update on 2017-12-26
Author: xiaomingnio
Github: https://github.com/apachecn/kaggle
Project: https://www.kaggle.com/c/word2vec-nlp-tutorial
'''
import pandas as pd
import numpy as np
import re
from bs4 import BeautifulSoup
def review_to_wordlist(review):
'''
把IMDB的评论转成词序列
参考：http://blog.csdn.net/longxinchen_ml/article/details/50629613
'''
# 去掉HTML标签，拿到内容
review_text = BeautifulSoup(review, "html.parser").get_text()
# 用正则表达式取出符合规范的部分
review_text = re.sub("[^a-zA-Z]", " ", review_text)
# 小写化所有的词，并转成词list
words = review_text.lower().split()
# 返回words
return words
root_dir = "/opt/data/kaggle/getting-started/word2vec-nlp-tutorial"
# 载入数据集
train = pd.read_csv('%s/%s' % (root_dir, 'labeledTrainData.tsv'), header=0, delimiter="\t", quoting=3)
test = pd.read_csv('%s/%s' % (root_dir, 'testData.tsv'), header=0, delimiter="\t", quoting=3)
print(train.head())
print(test.head())
================================================
FILE: src/py3.x/kaggle/playground/dogs-vs-cats/Pytorch-CNN.py
================================================
# -*- coding: utf-8 -*-
'''
PyTorch 版本的 CNN 实现 Dogs vs Cats
'''
# 引入相应的库函数
import os
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
from torch.autograd import Variable
from torch.utils import data
from torchvision import transforms as T
# 我们暂时使用 AlexNet 模型做第一次测试
from models import AlexNet
'''
参考链接：
https://github.com/pytorch/examples/blob/27e2a46c1d1505324032b1d94fc6ce24d5b67e97/imagenet/main.py
'''
'''
这里我们使用了 torch.utils.data 中的一些函数，比如 Dataset
class torch.utils.data.Dataset
表示 Dataset 的抽象类
所有其他的数据集都应继承该类。所有子类都应该重写 __len__ ，提供数据集大小的方法，和 __getitem__ ，支持从 0 到 len(self) 整数索引的方法
'''
# --------------------------- 1.加载数据 start ----------------------------------------------------------------
class GetData(data.Dataset):
def __init__(self, root, transforms=None, train=True, test=Flase):
'''
Desc:
获取全部的数据，并根据我们的要求，将数据划分为 训练、验证、测试数据集
Args:
self --- none
root --- 数据存在路径
transforms --- 对数据的转化，这里默认是 None
train ---- 标注是否是训练集
test ---- 标注是否是测试集
Returns:
None
'''
# 设置 测试集数据
self.test = test
imgs = [os.path.join(root, img) for img in os.listdir(root)]
# test1：即测试数据集， D:/dataset/dogs-vs-cats/test1
# train: 即训练数据集，D:/dataset/dogs-vs-cats/train
if self.test:
# 提取 测试数据集的序号，
# 如 x = 'd:/path/123.jpg'，
# x.split('.') 得到 ['d:/path/123', 'jpg']
# x.split('.')[-2] 得到 d:/path/123
# x.split('.')[-2].split('/') 得到 ['d:', 'path', '123']
# x.split('.')[-2].split('/')[-1] 得到 123
imgs = sorted(imgs, key=lambda x: int(x.split('.')[-2].split('/')[-1]))
else:
# 如果不是测试集的话，就是训练集，我们只切分一下，仍然得到序号，123
imgs = sorted(imgs, key=lambda x: int(x.split('.')[-2]))
# 获取图片的数量
imgs_num = len(imgs)
# 划分训练、验证集，验证集:训练集 = 3:7
# 判断是否为测试集
if self.test:
# 如果是 测试集，那么 就直接赋值
self.imgs = imgs
# 判断是否为 训练集
elif train:
# 如果是训练集，那么就把数据集的开始位置的数据 到 70% 部分的数据作为训练集
self.imgs = imgs[:int(0.7 * imgs_num)]
else:
# 这种情况就是划分验证集啦,从 70% 部分的数据 到达数据的末尾，全部作为验证集
self.imgs = imgs[int(0.7 * imgs_num):]
# 数据的转换操作，测试集，验证集，和训练集的数据转换有所区别
if transforms is None:
# 如果转换操作没有设置，那我们设置一个转换
'''
几个常见的 transforms 用的转换：
1、数据归一化 --- Normalize(mean, std) 是通过下面公式实现数据归一化 channel = (channel-mean)/std
2、class torchvision.transforms.Resize(size, interpolation=2) 将输入的 PIL 图像调整为给定的大小
3、class torchvision.transforms.CenterCrop(size) 在中心裁剪给定的 PIL 图像，参数 size 是期望的输出大小
4、ToTensor() 是将 PIL.Image(RGB) 或者 numpy.ndarray(H X W X C) 从 0 到 255 的值映射到 0~1 的范围内，并转化为 Tensor 形式
5、transforms.Compose() 这个是将多个 transforms 组合起来使用
'''
normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
# 测试集 和 验证集 的转换
# 判断如果是测试集或者不是训练集（也就是说是验证集），就应用我们下边的转换
if self.test or not train:
self.trainsforms = T.Compose([T.Resize(224), T.CenterCrop(224), T.ToTensor(), normalize])
else:
# 如果是测试集的话，使用另外的转换
self.transforms = T.Compose([T.Resize(256), T.RandomResizedCrop(224), T.RandomHorizontalFlip(), T.ToTensor(), normalize])
def __len__(self):
'''
Desc:
继承 Dataset 基类，重写 __len__ 方法，提供数据集的大小
Args:
self --- 无
Return:
数据集的长度
'''
return len(self.imgs)
def __getitem__(self, index):
'''
Desc:
继承 Dataset 基类，重写 __getitem__ 方法，支持整数索引，范围从 0 到 len(self)
返回一张图片的数据
对于测试集，没有label，返回图片 id，如 985.jpg 返回 985
对于训练集，是具有 label ，返回图片 id ，以及相对应的 label，如 dog.211.jpg 返回 id 为 211，label 为 dog
Args:
self --- none
index --- 索引
Return:
返回一张图片的数据
对于测试集，没有label，返回图片 id，如 985.jpg 返回 985
对于训练集，是具有 label ，返回图片 id ，以及相对应的 label，如 dog.211.jpg 返回 id 为 211，label 为 dog
'''
img_path = self.imgs[index]
# 判断，如果是测试集的数据的话，那就返回对应的序号，比如 d:path/123.jpg 返回 123
if self.test:
label = int(self.imgs[index].split('.')[-2].split('/')[-1])
else:
# 如果不是测试集的数据，那么会有相应的类别（label），也就是对应的dog 和 cat，dog 为 1，cat 为0
label = 1 if 'dog' in img_path.split('/')[-1] else 0
# 这里使用 Pillow 模块，使用 Image 打开一个图片
data = Image.open(img_path)
# 使用我们定义的 transforms ，将图片转换，详情参考：https://pytorch.org/docs/stable/torchvision/transforms.html#transforms-on-pil-image
# 默认的 transforms 设置的是 none
data = self.transforms(data)
# 将转换完成的 data 以及对应的 label（如果有的话），返回
return data,label
# 训练数据集的路径
train_path = 'D:/dataset/dogs-vs-cats/train'
# 从训练数据集的存储路径中提取训练数据集
train_dataset = GetData(train_path, train=True)
# 将训练数据转换成 mini-batch 形式
load_train = data.DataLoader(train_dataset, batch_size=20, shuffle=True, num_workers=1)
'''
utils.data.DataLoader() 解析
class torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None)
数据加载器。组合数据集和采样器，并在数据集上提供单个或多个进程迭代器。
参数：
dataset(Dataset) --- 从这之中加载数据的数据集。
batch_size (int, 可选) --- 每个 batch 加载多少个样本（默认值为：1）
shuffle (bool, 可选) --- 设置为 True 时，会在每个 epoch 时期重新组合数据（默认值：False）
sampler (Sampler, 可选) --- 定义从数据集中抽取样本的策略。如果指定，那么 shuffle 必须是 False 。
batch_sampler (Sampler, 可选) --- 类似采样器，但一次返回一批量的索引（index）。与 batch_size, shuffle, sampler 和 drop_last 相互排斥。
num_workers (int, 可选) --- 设置有多少个子进程用于数据加载。0 表示数据将在主进程中加载。（默认：0）
collate_fn (callable, 可选) --- 合并样本列表以形成 mini-batch
pin_memory (bool, 可选) --- 如果为 True，数据加载器会在 tensors 返回之前将 tensors 复制到 CUDA 固定内存中。
drop_last (bool, 可选) --- 如果 dataset size （数据集大小）不能被 batch size （批量大小）整除，则设置为 True 以删除最后一个 incomplete batch（未完成批次）。
如果设置为 False 和 dataset size（数据集大小）不能被 batch size（批量大小）整除，则最后一批将会更小。（默认：False）
timeout (numeric, 可选) --- 如果是正值，则为从 worker 收集 batch 的超时值。应该始终是非负的。（默认：0）
worker_init_fn (callable, 可选) --- 如果不是 None，那么将在每个工人子进程上使用 worker id（在 [0，num_workers - 1] 中的 int）作为输入，在 seeding 和加载数据之前调用这个子进程。（默认：无）
'''
# 测试数据的获取
# 首先设置测试数据的路径
test_path = 'D:/dataset/dogs-vs-cats/test1'
# 从测试数据集的存储路径中提取测试数据集
test_path = GetData(test_path, test=True)
# 将测试数据转换成 mini-batch 形式
loader_test = data.DataLoader(test_dataset, batch_size=3, shuffle=True, num_workers=1)
# --------------------------- 1.加载数据 end ----------------------------------------------------------------
# --------------------------- 2.构建 CNN 模型 start ----------------------------------------------------------------
# 调用我们现成的 AlexNet() 模型
cnn = AlexNet()
# 将模型打印出来观察一下
print(cnn)
# --------------------------- 2.构建 CNN 模型 end ------------------------------------------------------------------
# --------------------------- 3.设置相应的优化器和损失函数 start ------------------------------------------------------------------
'''
1、torch.optim 是一个实现各种优化算法的软件包。
比如我们这里使用的就是 Adam() 这个方法
class torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False) 这个类就实现了 adam 算法。
params(iterable) --- 可迭代的参数来优化或取消定义参数组
lr(float, 可选) --- 学习率（默认值 1e-3）
beta(Tuple[float, float], 可选) --- 用于计算梯度及其平方的运行平均值的系数（默认值：（0.9，0.999））
eps (float, 可选) ---- 添加到分母以提高数值稳定性（默认值：1e-8）
weight_decay (float, 可选) --- 权重衰减（L2 惩罚）（默认值：0）
amsgrad (boolean, 可选) ---- 是否使用该算法的AMSGrad变体来自论文关于 Adam 和 Beyond 的融合
2、还有这里我们使用的损失函数
class torch.nn.CrossEntropyLoss(weight=None, size_average=True, ignore_index=-100, reduce=True)
交叉熵损失函数
具体的请看：http://pytorch.apachecn.org/cn/docs/0.3.0/nn.html
'''
# 3. 设置优化器和损失函数
# 这里我们使用 Adam 优化器，使用的损失函数是 交叉熵损失
optimizer = torch.optim.Adam(cnn.parameters(), lr=0.005, betas=(0.9, 0.99)) # 优化所有的 cnn 参数
loss_func = nn.CrossEntropyLoss() # 目标 label 不是 one-hotted 类型的
# --------------------------- 3.设置相应的优化器和损失函数 end ------------------------------------------------------------------
# --------------------------- 4.训练 CNN 模型 start ------------------------------------------------------------------
# 4. 训练模型
# 设置训练模型的次数，这里我们设置的是 10 次，也就是用我们的训练数据集对我们的模型训练 10 次，为了节省时间，我们可以只训练 1 次
EPOCH = 10
# 训练和测试
for epoch in range(EPOCH):
num = 0
# 给出 batch 数据，在迭代 train_loader 的时候对 x 进行 normalize
for step, (x, y) in enumerate(loader_train):
b_x = Variable(x) # batch x
b_y = Variable(y) # batch y
output = cnn(b_x) # cnn 的输出
loss = loss_func(output, b_y) # 交叉熵损失
optimizer.zero_grad() # 在这一步的训练步骤上，进行梯度清零
loss.backward() # 反向传播，并进行计算梯度
optimizer.step() # 应用梯度
# 可以打印一下
# print('-'*30, step)
if step % 20 == 0:
num += 1
for _, (x_t, y_test) in enumerate(loader_test):
x_test = Variable(x_t) # batch x
test_output = cnn(x_test)
pred_y = torch.max(test_output, 1)[1].data.squeeze()
accuracy = sum(pred_y == y_test) / float(y_test.size(0))
print('Epoch: ', epoch, '| Num: ', num, '| Step: ', step, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.4f' % accuracy)
# --------------------------- 4. 训练 CNN 模型 end ------------------------------------------------------------------
================================================
FILE: src/py3.x/kaggle/playground/dogs-vs-cats/main的副本.py
================================================
import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
model_names = sorted(
name for name in models.__dict__
if name.islower() and not name.startswith("__") and callable(
models.__dict__[name]))
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR', help='path to dataset')
parser.add_argument(
'--arch',
'-a',
metavar='ARCH',
default='resnet18',
choices=model_names,
help='model architecture: ' + ' | '.join(model_names) +
' (default: resnet18)')
parser.add_argument(
'-j',
'--workers',
default=4,
type=int,
metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument(
'--epochs',
default=90,
type=int,
metavar='N',
help='number of total epochs to run')
parser.add_argument(
'--start-epoch',
default=0,
type=int,
metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument(
'-b',
'--batch-size',
default=256,
type=int,
metavar='N',
help='mini-batch size (default: 256)')
parser.add_argument(
'--lr',
'--learning-rate',
default=0.1,
type=float,
metavar='LR',
help='initial learning rate')
parser.add_argument(
'--momentum', default=0.9, type=float, metavar='M', help='momentum')
parser.add_argument(
'--weight-decay',
'--wd',
default=1e-4,
type=float,
metavar='W',
help='weight decay (default: 1e-4)')
parser.add_argument(
'--print-freq',
'-p',
default=10,
type=int,
metavar='N',
help='print frequency (default: 10)')
parser.add_argument(
'--resume',
default='',
type=str,
metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument(
'-e',
'--evaluate',
dest='evaluate',
action='store_true',
help='evaluate model on validation set')
parser.add_argument(
'--pretrained',
dest='pretrained',
action='store_true',
help='use pre-trained model')
parser.add_argument(
'--world-size',
default=1,
type=int,
help='number of distributed processes')
parser.add_argument(
'--dist-url',
default='tcp://224.66.41.62:23456',
type=str,
help='url used to set up distributed training')
parser.add_argument(
'--dist-backend', default='gloo', type=str, help='distributed backend')
best_prec1 = 0
def main():
global args, best_prec1
args = parser.parse_args()
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(
backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size)
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
if not args.distributed:
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
## model.cuda()
else:
model = torch.nn.DataParallel(model) ##.cuda()
else:
## model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss() ##.cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(traindir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.evaluate:
validate(val_loader, model, criterion)
return
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
prec1 = validate(val_loader, model, criterion)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
}, is_best)
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
##target = target.cuda(async=True)
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5))
def validate(val_loader, model, criterion):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target) in enumerate(val_loader):
##target = target.cuda(async=True)
input_var = torch.autograd.Variable(input, volatile=True)
target_var = torch.autograd.Variable(target, volatile=True)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i,
len(val_loader),
batch_time=batch_time,
loss=losses,
top1=top1,
top5=top5))
print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(
top1=top1, top5=top5))
return top1.avg
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best.pth.tar')
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = args.lr * (0.1**(epoch // 30))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def accuracy(output, target, topk=(1, )):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
if __name__ == '__main__':
main()
================================================
FILE: src/py3.x/kaggle/playground/dogs-vs-cats/models/AlexNet.py
================================================
# coding:utf8
from torch import nn
from .BasicModule import BasicModule
class AlexNet(BasicModule):
'''
code from torchvision/models/alexnet.py
结构参考
'''
def __init__(self, num_classes=2):
super(AlexNet, self).__init__()
self.model_name = 'alexnet'
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, num_classes),
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), 256 * 6 * 6)
x = self.classifier(x)
return x
================================================
FILE: src/py3.x/kaggle/playground/dogs-vs-cats/models/BasicModule.py
================================================
# coding:utf8
import torch as t
import time
class BasicModule(t.nn.Module):
'''
封装了nn.Module,主要是提供了save和load两个方法
'''
def __init__(self):
super(BasicModule, self).__init__()
self.model_name = str(type(self)) # 默认名字
def load(self, path):
'''
可加载指定路径的模型
'''
self.load_state_dict(t.load(path))
def save(self, name=None):
'''
保存模型，默认使用“模型名字+时间”作为文件名
'''
if name is None:
prefix = 'checkpoints/' + self.model_name + '_'
name = time.strftime(prefix + '%m%d_%H:%M:%S.pth')
t.save(self.state_dict(), name)
return name
class Flat(t.nn.Module):
'''
把输入reshape成（batch_size,dim_length）
'''
def __init__(self):
super(Flat, self).__init__()
# self.size = size
def forward(self, x):
return x.view(x.size(0), -1)
================================================
FILE: src/py3.x/kaggle/playground/dogs-vs-cats/models/ResNet34.py
================================================
#coding:utf8
from .BasicModule import BasicModule
from torch import nn
from torch.nn import functional as F
class ResidualBlock(nn.Module):
'''
实现子module: Residual Block
'''
def __init__(self, inchannel, outchannel, stride=1, shortcut=None):
super(ResidualBlock, self).__init__()
self.left = nn.Sequential(
nn.Conv2d(inchannel, outchannel, 3, stride, 1, bias=False),
nn.BatchNorm2d(outchannel),
nn.ReLU(inplace=True),
nn.Conv2d(outchannel, outchannel, 3, 1, 1, bias=False),
nn.BatchNorm2d(outchannel))
self.right = shortcut
def forward(self, x):
out = self.left(x)
residual = x if self.right is None else self.right(x)
out += residual
return F.relu(out)
class ResNet34(BasicModule):
'''
实现主module：ResNet34
ResNet34包含多个layer，每个layer又包含多个Residual block
用子module来实现Residual block，用_make_layer函数来实现layer
'''
def __init__(self, num_classes=2):
super(ResNet34, self).__init__()
self.model_name = 'resnet34'
# 前几层: 图像转换
self.pre = nn.Sequential(
nn.Conv2d(3, 64, 7, 2, 3, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 1))
# 重复的layer，分别有3，4，6，3个residual block
self.layer1 = self._make_layer(64, 128, 3)
self.layer2 = self._make_layer(128, 256, 4, stride=2)
self.layer3 = self._make_layer(256, 512, 6, stride=2)
self.layer4 = self._make_layer(512, 512, 3, stride=2)
# 分类用的全连接
self.fc = nn.Linear(512, num_classes)
def _make_layer(self, inchannel, outchannel, block_num, stride=1):
'''
构建layer,包含多个residual block
'''
shortcut = nn.Sequential(
nn.Conv2d(inchannel,outchannel,1,stride, bias=False),
nn.BatchNorm2d(outchannel))
layers = []
layers.append(ResidualBlock(inchannel, outchannel, stride, shortcut))
for i in range(1, block_num):
layers.append(ResidualBlock(outchannel, outchannel))
return nn.Sequential(*layers)
def forward(self, x):
x = self.pre(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = F.avg_pool2d(x, 7)
x = x.view(x.size(0), -1)
return self.fc(x)
================================================
FILE: src/py3.x/kaggle/playground/dogs-vs-cats/models/__init__.py
================================================
from .AlexNet import AlexNet
from .ResNet34 import ResNet34
================================================
FILE: src/py3.x/kaggle/playground/dogs-vs-cats/使用迁移学习进行猫狗识别98%.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 根据Pytorch迁移学习教程改编而得\n",
"原文地址：\n",
"\n",
"http://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html\n",
"\n",
"使用迁移学习进行猫狗识别\n",
"==========================\n",
"\n",
"这个教程将教你如何使用迁移学习训练你的网络. 你可以在 cs231n 笔记中阅读更多有关迁移学习的信息.\n",
"\n",
"http://cs231n.github.io/transfer-learning/\n",
"\n",
"引用自该笔记,\n",
"\n",
" 事实上, 很少有人从头(随机初始化)开始训练一个卷积网络, 因为拥有一个足够大的数据库是比较少见的. 替代的是, 通常会从一个大的数据集(例如 ImageNet, 包含120万的图片和1000个分类)预训练一个卷积网络, 然后将这个卷积网络作为初始化的网络, 或者是感兴趣任务的固定的特征提取器.\n",
"\n",
"如下是两种主要的迁移学习的使用场景:\n",
"\n",
"- **微调卷积网络**: 取代随机初始化网络, 我们从一个预训练的网络初始化, 比如从 imagenet 1000 数据集预训练的网络. 其余的训练就像往常一样.\n",
"- **卷积网络作为固定的特征提取器**: 在这里, 我们固定网络中的所有权重, 最后的全连接层除外. 最后的全连接层被新的随机权重替换, 并且, 只有这一层是被训练的.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# License: BSD\n",
"# Author: Sasank Chilamkurthy\n",
"\n",
"from __future__ import print_function, division\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.optim import lr_scheduler\n",
"import numpy as np\n",
"import torchvision\n",
"from torchvision import datasets, models, transforms\n",
"import matplotlib.pyplot as plt\n",
"import time\n",
"import os\n",
"import copy\n",
"\n",
"plt.ion() # interactive mode"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load Data\n",
"---------\n",
"数据集地址\n",
"\n",
"https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data\n",
"\n",
"# 比赛说明\n",
"+ 在本次比赛中，您将编写一个算法来分类图像是否包含狗或猫。这对人类，狗和猫来说很容易。你的电脑会觉得有点困难\n",
"\n",
"## 特征说明\n",
"+ Dogs vs. Cats是一个传统的二分类问题。\n",
" + 训练集包含25000张图片，命名格式为..jpg, 如cat.10000.jpg、dog.100.jpg\n",
" + 测试集包含12500张图片，命名为.jpg，如1000.jpg。\n",
"+ 参赛者需根据训练集的图片训练模型，并在测试集上进行预测，输出它是狗的概率。\n",
"+ 最后提交的csv文件如下，第一列是图片的，第二列是图片为狗的概率。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 数据目录结构为\n",
"+ train\n",
" + dog\n",
" + cat\n",
" \n",
"+ val\n",
" + dog\n",
" + cat\n",
"+ test\n",
" + test\n",
" \n",
"其中使用train中dog和cat各2500张图像组成Val数据集"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Data augmentation and normalization for training\n",
"# Just normalization for validation\n",
"data_transforms = {\n",
" 'train': transforms.Compose([\n",
" transforms.RandomResizedCrop(224),\n",
" transforms.RandomHorizontalFlip(),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
" ]),\n",
" 'val': transforms.Compose([\n",
" transforms.Resize(256),\n",
" transforms.CenterCrop(224),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
" ]),\n",
" 'test': transforms.Compose([\n",
" transforms.Resize(256),\n",
" transforms.CenterCrop(224),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
" ]),\n",
"}\n",
"\n",
"data_dir = '../dogvscat/'\n",
"image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
" data_transforms[x])\n",
" for x in ['train', 'val','test']}\n",
"dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
" shuffle=True, num_workers=4)\n",
" for x in ['train', 'val']}\n",
"testdataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
" shuffle=False, num_workers=4)\n",
" for x in ['test']}\n",
"dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val','test']}\n",
"class_names = image_datasets['train'].classes\n",
"\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 可视化一些图片\n",
"\n",
"让我们显示一些训练中的图片, 以便了解数据增强.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def imshow(inp, title=None):\n",
" \"\"\"Imshow for Tensor.\"\"\"\n",
" inp = inp.numpy().transpose((1, 2, 0))\n",
" mean = np.array([0.485, 0.456, 0.406])\n",
" std = np.array([0.229, 0.224, 0.225])\n",
" inp = std * inp + mean\n",
" inp = np.clip(inp, 0, 1)\n",
" plt.imshow(inp)\n",
" if title is not None:\n",
" plt.title(title)\n",
" plt.pause(0.001) # pause a bit so that plots are updated\n",
"\n",
"\n",
"# Get a batch of training data\n",
"inputs, classes = next(iter(dataloaders['train']))\n",
"\n",
"# Make a grid from batch\n",
"out = torchvision.utils.make_grid(inputs)\n",
"\n",
"imshow(out, title=[class_names[x] for x in classes])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"训练模型\n",
"------------------\n",
"\n",
"现在, 让我们写一个通用的函数来训练模型. 这里, 我们将会举例说明:\n",
"\n",
"- 调度学习率\n",
"- 保存最佳的学习模型\n",
"\n",
"下面函数中, 参数 ``scheduler`` 是\n",
"``torch.optim.lr_scheduler`` 中的 LR scheduler 对象.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
" since = time.time()\n",
"\n",
" best_model_wts = copy.deepcopy(model.state_dict())\n",
" best_acc = 0.0\n",
"\n",
" for epoch in range(num_epochs):\n",
" print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
" print('-' * 10)\n",
"\n",
" # Each epoch has a training and validation phase\n",
" for phase in ['train', 'val']:\n",
" if phase == 'train':\n",
" scheduler.step()\n",
" model.train() # Set model to training mode\n",
" else:\n",
" model.eval() # Set model to evaluate mode\n",
"\n",
" running_loss = 0.0\n",
" running_corrects = 0\n",
"\n",
" # Iterate over data.\n",
" for inputs, labels in dataloaders[phase]:\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" # zero the parameter gradients\n",
" optimizer.zero_grad()\n",
"\n",
" # forward\n",
" # track history if only in train\n",
" with torch.set_grad_enabled(phase == 'train'):\n",
" outputs = model(inputs)\n",
" _, preds = torch.max(outputs, 1)\n",
" loss = criterion(outputs, labels)\n",
"\n",
" # backward + optimize only if in training phase\n",
" if phase == 'train':\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" # statistics\n",
" running_loss += loss.item() * inputs.size(0)\n",
" running_corrects += torch.sum(preds == labels.data)\n",
"\n",
" epoch_loss = running_loss / dataset_sizes[phase]\n",
" epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
"\n",
" print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
" phase, epoch_loss, epoch_acc))\n",
"\n",
" # deep copy the model\n",
" if phase == 'val' and epoch_acc > best_acc:\n",
" best_acc = epoch_acc\n",
" best_model_wts = copy.deepcopy(model.state_dict())\n",
"\n",
" print()\n",
"\n",
" time_elapsed = time.time() - since\n",
" print('Training complete in {:.0f}m {:.0f}s'.format(\n",
" time_elapsed // 60, time_elapsed % 60))\n",
" print('Best val Acc: {:4f}'.format(best_acc))\n",
"\n",
" # load best model weights\n",
" model.load_state_dict(best_model_wts)\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 显示模型的预测结果\n",
"写一个处理少量图片, 并显示预测结果的通用函数\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def visualize_model(model, num_images=6):\n",
" was_training = model.training\n",
" model.eval()\n",
" images_so_far = 0\n",
" fig = plt.figure()\n",
"\n",
" with torch.no_grad():\n",
" for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" outputs = model(inputs)\n",
" _, preds = torch.max(outputs, 1)\n",
"\n",
" for j in range(inputs.size()[0]):\n",
" images_so_far += 1\n",
" ax = plt.subplot(num_images//2, 2, images_so_far)\n",
" ax.axis('off')\n",
" ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
" imshow(inputs.cpu().data[j])\n",
"\n",
" if images_so_far == num_images:\n",
" model.train(mode=was_training)\n",
" return\n",
" model.train(mode=was_training)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 微调卷积网络\n",
"加载一个预训练的网络, 并重置最后一个全连接层.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"model_ft = models.resnet18(pretrained=True)\n",
"num_ftrs = model_ft.fc.in_features\n",
"model_ft.fc = nn.Linear(num_ftrs, 2)\n",
"\n",
"model_ft = model_ft.to(device)\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"\n",
"# Observe that all parameters are being optimized\n",
"optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
"\n",
"# Decay LR by a factor of 0.1 every 7 epochs\n",
"exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 训练和评估\n",
"使用 GPU 的话, 需要的时间约为70分钟.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'train_model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n\u001b[0m\u001b[0;32m 2\u001b[0m num_epochs=25)\n",
"\u001b[1;31mNameError\u001b[0m: name 'train_model' is not defined"
]
}
],
"source": [
"model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
" num_epochs=25)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"visualize_model(model_ft)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"卷积网络作为固定的特征提取器\n",
"----------------------------------\n",
"\n",
"这里, 我们固定网络中除最后一层外的所有权重. 为了固定这些参数, 我们需要设置 ``requires_grad == False`` ，然后在``backward()``中就不会计算梯度..\n",
"\n",
"你可以在这里阅读更多相关信息\n",
"\n",
"http://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"model_conv = torchvision.models.resnet18(pretrained=True)\n",
"for param in model_conv.parameters():\n",
" param.requires_grad = False\n",
"\n",
"# Parameters of newly constructed modules have requires_grad=True by default\n",
"num_ftrs = model_conv.fc.in_features\n",
"model_conv.fc = nn.Linear(num_ftrs, 2)\n",
"\n",
"model_conv = model_conv.to(device)\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"\n",
"# Observe that only parameters of final layer are being optimized as\n",
"# opoosed to before.\n",
"optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)\n",
"\n",
"# Decay LR by a factor of 0.1 every 7 epochs\n",
"exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 训练和评估\n",
"在使用 CPU 的情况下, 和前一个方案相比, 这将花费的时间是它的一半. 期望中, 网络的大部分是不需要计算梯度的. 前向传递依然要计算梯度.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0/24\n",
"----------\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#仅训练最后一层，时间较训练全部参数下降一半，1080TI约30min\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m model_conv = train_model(model_conv, criterion, optimizer_conv,\n\u001b[1;32m----> 3\u001b[1;33m exp_lr_scheduler, num_epochs=25)\n\u001b[0m",
"\u001b[1;32m\u001b[0m in \u001b[0;36mtrain_model\u001b[1;34m(model, criterion, optimizer, scheduler, num_epochs)\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[1;31m# statistics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m \u001b[0mrunning_loss\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 44\u001b[0m \u001b[0mrunning_corrects\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreds\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"model_conv = train_model(model_conv, criterion, optimizer_conv,\n",
" exp_lr_scheduler, num_epochs=25)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"visualize_model(model_conv)\n",
"\n",
"plt.ioff()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 测试集预测及提交"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"preds = []\n",
"for i, (inputs, labels) in enumerate(testdataloaders['test']):\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" outputs = model_conv(inputs)\n",
" preds.append(torch.max(outputs, 1)[1])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"torch.save(model_conv, 'dogsvscat.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"labels=[]\n",
"for i in range(len(preds)):\n",
" labels.extend(preds[i].cpu().numpy())"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"fileindex = []\n",
"for i in range(12500):\n",
" fileindex.append(testdataloaders['test'].dataset.imgs[i][0].split('\\\\')[-1].split('.')[0])"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"result = pd.Series(labels,index=fileindex)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"result.to_csv('submit.csv')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
================================================
FILE: src/py3.x/list2iteration.py
================================================
#!/usr/bin/python
# coding:utf8
'''
迭代使用的是循环结构。
递归使用的是选择结构。
'''
from __future__ import print_function
# 递归求解
def calculate(l):
if len(l) <= 1:
return l[0]
value = calculate(l[1:])
return 10**(len(l) - 1) * l[0] + value
# 迭代求解
def calculate2(l):
result = 0
while len(l) >= 1:
result += 10 ** (len(l)-1) * l[0]
l = l[1:]
return result
l1 = [1, 2, 3]
l2 = [4, 5]
sum = 0
result = calculate(l1) + calculate(l2)
# result = calculate2(l1) + calculate2(l2)
print(result)
================================================
FILE: src/script.py
================================================
# coding: utf-8
import os
import sys
def format_file(filename, str1, str2):
"""
文件内容的替换功能
:return:
"""
with open(filename, 'r') as f:
var_object = f.read()
if "gitalk" not in var_object:
var_object = var_object.replace(str1, str2)
# print(var_object)
f = open(filename, "w")
f.write(var_object)
if __name__ == "__main__":
if len(sys.argv) == 3:
version, u_type = sys.argv[1], sys.argv[2]
else:
print("Usage: 参数个数为%s - 错误，应该改为3" % len(sys.argv))
sys.exit(-1)
tag = True
if u_type == "index":
tag = False
# if version == "home":
# filename = "_book/index.html"
# else:
# filename = "_book/docs/%s/index.html" % version
# str1 = """
#
#
# """
# str2 = """
#
#
#
# """
elif u_type == "book":
if version == "home":
filename = "book.json"
tag = False
else:
filename = "docs/%s/book.json" % version
str1 = "https://github.com/apachecn/Interview/blob/master"
str2 = "https://github.com/apachecn/Interview/blob/master/docs/%s" % version
elif u_type == "powered":
if version == "home":
filename = "node_modules/gitbook-plugin-tbfed-pagefooter/index.js"
else:
filename = "docs/%s/node_modules/gitbook-plugin-tbfed-pagefooter/index.js" % version
str1 = "powered by Gitbook"
str2 = "由 ApacheCN 团队提供支持"
elif u_type == "gitalk":
if version == "home":
filename = "node_modules/gitbook-plugin-tbfed-pagefooter/index.js"
else:
filename = "docs/%s/node_modules/gitbook-plugin-tbfed-pagefooter/index.js" % version
str1 = """ var str = ' \\n\\n'"""
str2 = """
var str = '\\n\\n'+
'\\n
'+
'\\n'+
'\\n '+
'\\n '+
'\\n '+
'\\n '+
'\\n '+
'\\n'+
'\\n '+
'\\n'+
'\\n '+
'\\n '+
'\\n'+
'\\n'+
'\\n'+
'\\n'+
'\\n
'
str += '\\n\\n'+
'\\n'+
'\\n '+
'\\n '+
'\\n '+
'\\n '+
'\\n '+
'\\n'
str += '\\n\\n'
"""
# 状态为 True 就进行替换
if tag: format_file(filename, str1, str2)
================================================
FILE: src/test.py
================================================
class Solution(object):
def searchInsert(self, nums, target):
"""
:type nums: List[int]
:type target: int
:rtype: int
"""
left = 0
right = len(nums) - 1
while left <= right:
mid = (left + right) // 2
print(">>> %s: %s[%s], %s[%s], %s[%s]" % (target, nums[left], left, nums[right], right, nums[mid], mid))
if nums[mid] > target:
right = mid - 1
elif nums[mid] < target:
left = mid + 1
else:
break
mid = mid+1 if left > mid else mid
print("结果: ", mid, left)
if __name__ == "__main__":
nums = [1, 2, 4, 5, 6, 7, 8, 9, 11, 15]
target = -2
s = Solution()
s.searchInsert(nums, target)
================================================
FILE: src/其他/58.md
================================================
================================================
FILE: src/其他/aiqiyi.md
================================================
```python
"""第一题"""
id = input()
first, second = sum(int(i) for i in id[:3]), sum(int(i) for i in id[3:])
diff = second-first
if diff == 0:
print(0)
else:
nums = [int(i) for i in id]
res = 0
if diff > 0:
tmp = [9-i for i in nums[:3]] + [i for i in nums[3:]]
while diff > 0:
res += 1
diff -= max(tmp)
tmp[tmp.index(max(tmp))] = 0
print(res)
else:
tmp = [9-i for i in nums[3:]] + [i for i in nums[:3]]
while diff < 0:
res += 1
diff += max(tmp)
tmp[tmp.index(max(tmp))] = 0
print(res)
"""第二题"""
tmp = input().split()
N, M, P = int(tmp[0]), int(tmp[1]), int(tmp[2])
tmp = input().split()
foods = [int(i) for i in tmp]
# N, M, P = int(tmp[0]), int(tmp[1]), int(tmp[2])
for i in range(M):
tmp = input().split()
if tmp[0] == 'B':
foods[int(tmp[1])-1] -= 1
else:
foods[int(tmp[1])-1] += 1
# print(foods)
back_foods = sorted(foods)[::-1]
# print(foods[P-1])
print(back_foods.index(foods[P-1])+1)
```
================================================
FILE: src/其他/baicizhanxiaomi.md
================================================
```python
一晚上做了好几家的，头疼，这些捞b公司贼烦
我自己没有参加笔试，就是纯粹A着玩，纯属娱乐
"""百词斩第一题"""
"""自我实现版本"""
def get_time_diff(time1, time2):
res = 0
res += (int(time2[:2]) - int(time1[:2])) * 3600
res += (int(time2[3:5]) - int(time1[3:5])) * 60
res += int(time2[-2:]) - int(time1[-2:])
return res
time1 = input()
time2 = input()
sec = get_time_diff(time1, time2)
print(int(30*sec//3600))
print(int(6*sec//60))
print(int(sec*6))
"""工业版本
可惜笔试不支持
"""
from dateutil.parser import parse
def get_time_diff(time1, time2):
time1 = parse(time1)
time2 = parse(time2)
return (time2-time1).total_seconds()
time1 = '2018-09-20/' + input()
time2 = '2018-09-20/' + input()
sec = get_time_diff(time1, time2)
print(int(30*sec//3600))
print(int(6*sec//60))
print(int(sec*6))
"""
14:52:11
21:41:14
00:00:00
18:00:00
"""
"""百词斩第二题"""
"""百词斩第二题"""
n = int(input())
nums = [int(i) for i in input().split()]
res = ''
# tmp = nums[0]
i = 0
start = 0
while i <= n-1:
i += 1
tmp = 0
while i <= n-1 and nums[i] == nums[i-1]+1:
i += 1
tmp += 1
# print(tmp, i)
if tmp >= 2:
# print(111)
res += str(nums[start]) + '-' + str(nums[i-1]) + ' '
# start = i
elif tmp == 0:
# print(222)
res += str(nums[start]) + ' '
else:
# print(333)
# print(nums[start:i+1])
res += ' '.join([str(i) for i in nums[start:i]]) + ' '
start = i
# tmp = 0
print(res[:-1])
"""
6
1 2 3 4 6 7
6
1 2 4 5 6 7
"""
"""不知道谁的题目"""
lookup = {}
inputs = []
flag = True
while True:
user_input = input()
if user_input == 'END':
break
inputs.append(user_input)
user_input = user_input.split('#')
n, m = user_input[0], user_input[1]
num = int(m, int(n))
lookup[num] = lookup.get(num, 0) + 1
res = []
for i in range(len(inputs)):
tmp = inputs[i].split('#')
n, m = tmp[0], tmp[1]
num = int(m, int(n))
if lookup[num] == 1:
res.append(inputs[i])
if not res:
print('None')
else:
for i in res:
print(i)
print(int('33', 4))
"""
10#15
4#32
4#33
8#17
END
"""
“”“不知道谁的题目
三种颜色的球
”“”
class Solution(object):
res = 0
def cal_next(self, p, q, r, prev):
tmp = [p, q, r]
cur_max = max(tmp)
# 此时前面排好了且都满足，想要不相邻，数量最多的球中间的所有空格
# 必须小于或等于另外两个球的数量，否则说明此时排列不合法直接return
if cur_max - 1 > sum(tmp) - cur_max:
return
if p == q == r == 0:
self.res += 1
return
if p > 0 and prev != 'p':
self.cal_next(p-1, q, r, 'p')
if q > 0 and prev != 'q':
self.cal_next(p, q-1, r, 'q')
if r > 0 and prev != 'r':
self.cal_next(p, q, r-1, 'r')
s = Solution()
p, q, r = [int(i) for i in input().split()]
s.cal_next(p, q, r, '')
print(s.res)
```
================================================
FILE: src/其他/data_structure.md
================================================
# 我们来一起每周学习数据结构吧，可以在群里一起讨论问题
# qq群号码：303677416
================================================
FILE: src/其他/didi.md
================================================
```python
"""第一题"""
lookup1 = set('qwertasdfgzxcv')
lookup2 = set('yuiophjklbnm')
def minDistance(word1, word2):
if len(word1) == 0 or len(word2) == 0:
return max(len(word1), len(word2))
dp = [[0 for j in range(len(word2)+1)] for i in range(len(word1)+1)]
for i in range(1, len(word1)+1):
for j in range(1, len(word2)+1):
tmp_dist = 0
if word1[i-1] == word2[j-1]:
tmp_dist == 0
elif (word1[i-1] in lookup1 and word2[j-1] in lookup1) or (word1[i-1] in lookup2 and word2[j-1] in lookup2):
tmp_dist = 1
else:
tmp_dist = 2
dp[i][j] = min(dp[i-1][j]+3, dp[i][j-1]+3, dp[i-1][j-1]+tmp_dist)
# print(dp)
return dp[-1][-1]
"""
slep slap sleep step shoe shop snap slep
"""
strs = input().split(' ')
# print(strs)
dists = [0]
for i in range(1, len(strs)):
dists.append((minDistance(strs[0], strs[i])))
# print(dists)
tmp = [0] * len(dists)
for i in range(1, len(dists)):
tmp[i] = [dists[i], strs[i]]
tmp = tmp[1:]
tmp = sorted(tmp, key=lambda x:x[0])
# print(tmp)
res = tmp[0][1]+' ' + tmp[1][1]+' '+tmp[2][1]
print(res)
"""第二题""" PS: 这题感谢算法群里的kkk大佬指点我
class Solution(object):
res = 0
def cal_next(self, p, q, r, prev):
tmp = [p, q, r]
cur_max = max(tmp)
# 此时前面排好了且都满足，想要不相邻，数量最多的球中间的所有空格
# 必须小于或等于另外两个球的数量，否则说明此时排列不合法直接return
if cur_max - 1 > sum(tmp) - cur_max:
return
if p == q == r == 0:
self.res += 1
return
if p > 0 and prev != 'p':
self.cal_next(p-1, q, r, 'p')
if q > 0 and prev != 'q':
self.cal_next(p, q-1, r, 'q')
if r > 0 and prev != 'r':
self.cal_next(p, q, r-1, 'r')
s = Solution()
p, q, r = [int(i) for i in input().split()]
s.cal_next(p, q, r, '')
print(s.res)
```
题目图片见下方：
### 第一题
### 第二题
================================================
FILE: src/其他/glodon.md
================================================
```python
"""第一题"""
def f(n):
res = 0
for i in range(1, n+1, 2):
res += i
for i in range(2, n+1, 2):
res -= i
return res
"""第二题"""
res = []
nums = ['9','8','7','6','5','4','3','2','1']
def convert(formula):
if not formula or len(formula) == 0:
return 0
tmp = ''
for i in range(len(formula)):
if formula[i] == '+' formula[i] != '+' and formula[i] != '-':
return int(tmp) + valid(formula[i+1:])
elif formula[i] == '-':
return int(tmp) - valid(formula[i+1:])
else:
tmp += formula[i]
def valid(formula):
if convert(formula) == 100:
return True
return False
def cal(formu, idx):
if idx == len(nums) - 1:
formula = ''.join(formu)
if valid(formula):
res.append(formula)
for i in range(idx, len(nums)-1):
cal(formu, i+1)
cal(formu[:i] + [formu[i]+'+'] + formu[i+1:], i+1)
cal(formu[:i] + [formu[i]+'-'] + formu[i+1:], i+1)
cal(nums, 0)
print('Results of formulas below all equal to 100')
for i in res:
print(i)
"""第三题"""
假设我们开始排列是1,2,3,4,5
然后首先我们要让第一个位置变成2，3，4或者5才满足条件，而在其中我们又会遇到原位置没有变化的情况，则我们知道每次变换2个位置的时候总有重复，因为两边计算出的所有情况中总有一半是不满足的，并且是对于所有位置不同的情况下，那么我们不妨从第一个位置开始，则公式为 ，其中n为小朋友的个数
开始编码：
from math import factorial
def cal(n):
return (n-1) * factorial(n-1) // 2
"""第四题"""
import sys
def maxSum(nums, window_len):
res = -sys.maxsize
for i in range(len(nums)-window_len):
res = max(res, sum(nums[i:i+k]))
return res
"""第五题"""
class ListNode(object):
def __init__(self, val, next, sib):
self.val = val
self.next = next
self.sib = sib
class Clone(object)
def cloneComplicateListNode(self, head):
if not head:
return head
dummy = cur = ListNode(head.val, None, None)
while head:
cur.next = self.cloneComplicateListNode(head.next)
cur.sib = self.cloneComplicateListNode(head.sib)
head = head.next
cur = cur.next
return dummy
```
================================================
FILE: src/其他/huaweiliuxuesheng.md
================================================
"""ip 地址转换"""
```python
db_num = int(input())
def dec2addr(dec):
res = []
for i in range(3, -1, -1):
if dec // (256 ** i) > 256 ** (4-i):
return '0.0.0.0'
res.append(str(dec // (256 ** i) % 256))
return '.'.join(res)
print(dec2addr(db_num))
```
"""页码问题"""
```python
def pageCount(n):
res = [0] * 10
for i in range(10):
page, base, cnt = n, 0, 0
while (page // (10 ** (base)) > 0):
tmp = (page % (10 ** (base + 1))) // (10 ** base)
left = page // (10 ** (base + 1))
right = page % (10 ** (base))
if (i == 0):
left -= 1
if (i > tmp):
cnt += left * (10 ** base)
elif (i < tmp):
cnt += (left + 1) * (10 ** base)
else:
cnt += (left * (10 ** base) + right + 1)
base += 1
res[i] = cnt
return ' '.join(str(i) for i in res[::-1])
n = int(input())
if 1 <= n <= 10 ** 9:
print(pageCount(n))
else:
print(-1)
```
"""约瑟夫问题"""
```python
n, m = map(int, input().split(','))
def joseph(people, cnt):
queue = list(range(1, people + 1))
death = (cnt - 1) % len(queue)
for i in range(people - 1):
del queue[death]
death = (death + cnt - 1) % len(queue)
return queue[0]
print(joseph(n, m))
```
================================================
FILE: src/其他/indeed_tokyo.md
================================================
```python
"""第一题"""
tmp = input().split()
n, q = int(tmp[0]), int(tmp[1])
nums = [int(i) for i in input().split()]
idx_ms = []
for i in range(q):
tmp = input().split()
idx, m = int(tmp[0]), int(tmp[1])
idx_ms.append([idx, m])
sums = sum([i for i in nums if i & 1 == 0])
res = [sums] * (q+1)
for i in range(q):
flag = nums[idx_ms[i][0]-1] & 1 == 0
if flag and idx_ms[i][1] + nums[idx_ms[i][0]-1] & 1 == 0:
res[i+1] = res[i] + idx_ms[i][1]
elif flag and idx_ms[i][1] + nums[idx_ms[i][0]-1] & 1 != 0:
res[i+1] = res[i] - nums[idx_ms[i][0] - 1]
elif not flag and idx_ms[i][1] + nums[idx_ms[i][0]-1] & 1 == 0:
res[i + 1] = res[i] + idx_ms[i][1] + nums[idx_ms[i][0] - 1]
else:
res[i+1] = res[i]
nums[idx_ms[i][0] - 1] += idx_ms[i][1]
# print(res[1:])
for i in range(q):
print(res[i+1])
"""第二题"""
import sys
N = int(input())
nums = [-sys.maxsize] + [int(i) for i in input().split()]
res = 0
flag = 1
for i in range(len(nums)):
if nums[i] >= nums[i-1]:
continue
elif nums[i] <= 0 and -nums[i] >= nums[i-1]:
res += 1
nums[i] = -nums[i]
elif nums[i] <= 0 and -nums[i] < nums[i-1]:
flag = 0
print(-1)
break
elif nums[i] < nums[i-1] and nums[i] > 0:
flag = 0
print(-1)
break
if flag:
print(res)
"""第三题"""
class Solution():
def __init__(self):
self.res = 0
self.lst = []
def sol(self):
tmp = input().split()
row, col = int(tmp[0]), int(tmp[1])
grid = []
for i in range(row):
grid.append([i for i in input()])
def bfs_paths(grid, start, goal):
if start[0] == goal[0] and start[1] == goal[1]:
self.res += 1
if start[0] == row-1 and start[1] == col-1:
return
# print(start, goal)
if 0 <= start[0] + 1 < row and grid[start[0]+1][start[1]] == '.':
bfs_paths(grid, [start[0]+1,start[1]], goal)
if 0 <= start[1] + 1 < col and grid[start[0]][start[1]+1] == '.':
bfs_paths(grid, [start[0],start[1]+1], goal)
# return res
n = int(input())
for i in range(n):
tmp = input().split()
goal1, goal2 = int(tmp[0]), int(tmp[1])
# print(goal1, goal2)
bfs_paths(grid, [0,0], [goal1-1,goal2-1])
# print([goal1-1,goal2-1])
self.lst.append(self.res % (pow(10, 9) + 7))
self.res = 0
s = Solution()
s.sol()
for i in s.lst:
print(i)
"""第四题"""
tmp = input().split()
n, m = int(tmp[0]), int(tmp[1])
requi = []
for i in range(m):
nums = [int(i) for i in input().split()]
requi.append(nums)
keys =set([1])
rooms = set([1])
# flag = True
while True:
flag1 = False
for i in requi:
if i[0] in rooms and i[1] not in rooms:
if i[2] in keys:
flag1 = True
rooms.add(i[1])
keys.add(i[1])
if not flag1:
break
print(len(rooms))
```
================================================
FILE: src/其他/kuaishou.md
================================================
```python
"""第一题"""
tmp = input().split()
numerator = int(tmp[0])
denominator = int(tmp[1])
def fractionToDecimal(numerator, denominator):
if numerator == 0:
return '0'
res = ''
if numerator * denominator < 0:
res += '-'
numerator, denominator = abs(numerator), abs(denominator)
res += str(numerator // denominator)
if numerator % denominator == 0:
return res
res += '.'
r = numerator % denominator
m = {}
# print(res)
while r:
if r in m:
res = res[:m[r]] + '(' + res[m[r]:] + ')'
break
m[r] = len(res)
r *= 10
res += str(r // denominator)
r %= denominator
return res
res = fractionToDecimal(numerator, denominator)
# print(res)
if '(' not in res:
if '.' not in res:
print(int(res))
else:
print(res)
else:
print(res)
"""第二题"""
tmp = input().split(',')
if not tmp:
print(0)
else:
costs = [int(i) for i in tmp]
if len(costs) == 1:
print(costs[-1])
elif len(costs) == 2:
print(costs[-1])
else:
dp = [0] * len(costs)
dp[0] = costs[0]
dp[1] = costs[1]
for i in range(2, len(dp)):
dp[i] = min(dp[i-1], dp[i-2]) + costs[i]
print(min(dp[-3], dp[-2]) + costs[-1])
"""第三题"""
def max_sub_array(nums):
n = len(nums)
maxSum = [nums[0] for i in range(n)]
for i in range(1, n):
maxSum[i] = max(maxSum[i - 1] + nums[i], nums[i])
return (maxSum.index(max(maxSum)), max(maxSum))
def min_sub_array(nums):
n = len(nums)
minSum = [nums[0] for i in range(n)]
for i in range(1, n):
minSum[i] = min(minSum[i - 1] + nums[i], nums[i])
return (minSum.index(min(minSum)), min(minSum))
tmp = input().split()
N, M = int(tmp[0]), int(tmp[1])
tmp = input().split(' ')
levels = [int(i) for i in tmp]
backup_levels = [i for i in levels]
idxes = []
res = 0
k = 0
for i in range(M):
idx, cur_max = max_sub_array(levels)
if cur_max >= 0:
cur_sum = 0
left_idx = 0
for j in range(idx, -1, -1):
cur_sum += levels[j]
if cur_sum == cur_max:
left_idx = j
idxes.append([left_idx, idx])
break
levels = levels[:left_idx] + levels[idx+1:]
# print(cur_max)
res += cur_max
# print(res)
else:
# tmp = min_sub_array(backup_levels[idxes[i][0]:idxes[i][1] + 1])[1]
idx, cur_min = min_sub_array(backup_levels[idxes[k][0]:idxes[k][1] + 1])
if cur_min <= 0:
cur_sum = 0
left_idx = 0
for j in range(idx, -1, -1):
cur_sum += backup_levels[j]
if cur_sum == cur_max:
left_idx = j
# idxes.append([left_idx, idx])
break
# print(left_idx, idx)
k += 1
res += -cur_min if cur_min <= 0 else 0
# print(res)
# print(backup_levels)
# print(idxes)
print(res)
# print()
# print(min_sub_array(backup_levels[0:5]))
```
================================================
FILE: src/其他/liulishuo.md
================================================
```python
"""第一题"""
n = int(input())
nums = []
for i in range(n):
nums.append(int(input()))
max_sum, max_end = nums[0], nums[0]
for i in range(1, len(nums)):
max_end = max(max_end + nums[i], nums[i])
max_sum = max(max_sum, max_end)
print(max_sum)
"""问答题
Let's say the egg1 and egg2
Firstly, use egg1 from 2nd floor and drop it, if egg1 break, then use egg2 to check 1th floor, if egg2 doesn't break, then the highest floor we need is 1th, otherwise 2nd floor is what we want. If from neither floor two eggs don't break, we can do this from 4th and 3rd, 6th and 5th, 8th and 7th,,,,,,100th and 99th.
From this method, we can easily get this problem reduced by 2 and totally time complexity will be O(lgn) if in programming.
"""
```
================================================
FILE: src/其他/pdd.md
================================================
```python
"""第一题"""
mat = [[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]]
def maxOne(mat):
if not mat or len(mat) == 0:
return []
row = len(mat)
col = len(mat[0]) if row else 0
i, j = -1, -1
for idx in range(row):
if mat[idx][-1] == 1:
i, j = idx, col - 1
break
if i == -1:
return []
fin_max = 0
while j >= 0 and mat[i][j] == 1:
j -= 1
fin_max += 1
res = [[i+1, fin_max]]
tmp_j = j + 1
while i + 1 < row:
i += 1
j = tmp_j
if mat[i][j] == 1:
cur_max = fin_max - 1
while j >= 0 and mat[i][j] == 1:
j -= 1
cur_max += 1
if cur_max > fin_max:
res = [[i+1, cur_max]]
fin_max = cur_max
elif cur_max == fin_max:
res.append([i+1, cur_max])
return res
print(maxOne(mat))
"""第三题"""
class Solution(object):
def __init__(self):
self.stack = []
self.res = 0
def dfs(self, arr, x, y, c):
m = len(arr)
n = len(arr[0]) if m else 0
if x - 1 >= 0 and y >= 0 and arr[x-1][y] == 'x':
self.stack.append((x-1)*100+y)
arr[x-1][y] = '#'
self.res += 1
if x + 1 < m and y >= 0 and arr[x+1][y] == 'x':
self.stack.append((x+1)*100+y)
arr[x+1][y] = '#'
self.res += 1
if x >= 0 and y - 1 >= 0 and arr[x][y-1] == 'x':
self.stack.append(x*100+y-1)
arr[x][y-1] = '#'
self.res += 1
if x >= 0 and y + 1 < n and arr[x][y+1] == 'x':
self.stack.append(x*100+y+1)
arr[x][y+1] = '#'
self.res += 1
while self.stack:
tmp = self.stack.pop()
self.dfs(arr, tmp //100, tmp % 100, 'x')
return self.res
if __name__ == '__main__':
s = Solution()
arr = [['x',' ',' ', ' ',' '],['x','x','x',' ','x'],['x','x','x','x','x']]
print(s.dfs(arr, 0, 0, 'x'))
"""第四题"""
import heapq
import sys
def solution(nums):
iters = [iter(l) for l in nums]
heap = [(next(it), i) for i, it in enumerate(iters)]
heapq.heapify(heap)
lo, hi = 0, sys.maxsize
r = max(heap)[0]
while True:
l, i = heap[0]
if r - l < hi - lo:
lo, hi = l, r
nxt = next(iters[i], None)
if nxt is None:
return [lo, hi]
r = max(r, nxt)
heapq.heappushpop(heap, (nxt, i))
nums = [[1,3,5],[4,8],[2,5]]
print(solution(nums))
```
================================================
FILE: src/其他/qqchangE_problem.md
================================================
```python
def canPartition(nums):
def dfs(nums, target, num):
n = len(nums)
for i in range(n):
B = nums[:i] + nums[i + 1:]
if num + nums[i] == target:
return True
elif num + nums[i] < target:
if dfs(B, target, num + nums[i]):
return True
elif num == 0: # 有一个数比sum/2还大，直接返回False
return False
return False
total = sum(nums)
if total % 2 != 0:
return False
target = total // 2
nums.sort(reverse=True) # 逆序排序，先从大的开始判断，速度会更快
res = dfs(nums, target, 0)
return res
print(canPartition([3, 3, 3, 4, 5]))
print(canPartition([1,1,2]))
print(canPartition([1,2,3,4]))
print(canPartition([2,3]))
print(canPartition([2,2,2,2,6]))
print(canPartition([1,2,2,2,2,7]))
output:
True
True
True
False
False
True
```
================================================
FILE: src/其他/shopee.md
================================================
```python
"""第一题千分位"""
n = input()[::-1]
if not n or len(n) == 0:
print('')
else:
if n[-1] == '-':
n = n[:-1]
tmp = []
for i in range(0, len(n), 3):
tmp.append(n[i:i+3])
# print(tmp)
print('-'+','.join(i[::-1] for i in tmp[::-1]))
else:
tmp = []
for i in range(0, len(n), 3):
tmp.append(n[i:i + 3])
print(','.join(i[::-1] for i in tmp[::-1]))
"""第二题通配符"""
p = input()
s = input()
def isMatch(s, p):
def helper(s, i, p, j):
if j == -1:
return i == -1
if i == -1:
if p[j] == '*' or p[j] == '#':
return helper(s, i, p, j - 1)
return False
if p[j] == '*':
return helper(s, i - 1, p, j) or helper(s, i, p, j-1)
if p[j] == '#':
return helper(s, i - 1, p, j-1) or helper(s, i, p, j - 1)
if p[j] == '?' or p[j] == s[i]:
return helper(s, i - 1, p, j - 1)
return False
return helper(s, len(s) - 1, p, len(p) - 1)
# print(1 if isMatch(s, p) else 0)
tmp = [['a?c', 'abc'], ['a?c', 'ac'], ['a#c', 'ac'], ['a#c', 'abc'], ['a#c', 'abbc'], ['a*c', 'ac'],
['a*c', 'abc'], ['a*c', 'abbc'], ['a*c', 'abd'], ['a?c#e*f', 'abcdef'], ['a?c#e*f', 'acdef']]
# for i in tmp:
# p, s = i[0], i[1]
# print(1 if isMatch(s, p) else 0)
print(1 if isMatch(s, p) else 0)
"""
输出应该为：
1
0
1
1
0
1
1
1
0
1
0
"""
```
================================================
FILE: src/其他/sohuchangyouweipinhui.md
================================================
```python
"""搜狐畅游第一题"""
nums = [int(i) for i in input().split()]
print(int(sum(nums) - (len(nums)-1)*(len(nums)-2)/2))
"""唯品会第一题"""
import heapq
[k, n] = [int(i) for i in input().split()]
topk = []
for i in range(n):
nums = [int(i) for i in input().split()]
for num in nums:
heapq.heappush(topk, num)
res = 0
for i in range(k):
res = heapq.heappop(topk)
print(res)
"""唯品会第二题"""
print(bin(sum([int(i, 2) for i in input().split()]))[2:])
```
================================================
FILE: src/其他/tecent.md
================================================
```python
# k = int(input())
# a = input()
# b = input()
# set_a = set()
# for i in range(0, len(a)-k+1):
# set_a.add(a[i:i+k])
# res = 0
# for i in range(0, len(b)-k+1):
# if b[i:i+k] in set_a:
# res += 1
# print(res)
"""
2
abab
ababab
"""
"""
7 14
"""
from math import sqrt
nums = [int(i) for i in input().split()]
x, y = nums[0], nums[1]
# n = 1
# while (n+1)*n/2 != sum(nums):
# n += 1
n = int((sqrt(8*sum(nums)+1)-1)/2)
permu = [i+1 for i in range(n)]
res = []
def exist(permu, x, path):
if not permu and x != 0:
return (False, -1)
if x == 0 or x in permu:
res.append(path+1)
return (True, path + 1)
if exist(permu[1:], x-permu[0], path+1)[0]:
res.append(exist(permu[1:], x-permu[0], path+1)[1])
return (True, exist(permu[1:], x-permu[0], path+1)[1])
if exist(permu[1:], x, path+1)[0]:
res.append(exist(permu[1:], x, path+1)[1])
return (True, exist(permu[1:], x, path+1)[1])
exist(permu, x, 0)
print(res)
print(min(res) if res else -1)
"""
2 3 3
"""
# res = 0
# def tri(a, b, c):
# if a + b > c and a + c > b and b + c > a:
# return True
# return False
#
# nums = [int(i) for i in input().split()]
# res = 0
# for i in range(1, nums[0]+1):
# for j in range(i+1, nums[1]+1):
# for k in range(j, nums[2]+1):
# if tri(i, j, k):
# res += 3
#
# print(res)
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
# from math import sqrt
# nums = [int(i) for i in input().split()]
# x, y = nums[0], nums[1]
#
# n = 1
# while (n+1)*n/2 != sum(nums):
# n += 1
#
# n = int((sqrt(8*sum(nums)+1)-1)/2)
#
# permu = [i+1 for i in range(n)]
# res = 0
# while x > 0:
# x -= permu[-1]
# permu = permu[:-1]
# res += 1
# print(res)
```
================================================
FILE: src/其他/times.md
================================================
http://bigocheatsheet.com/
https://algs4.cs.princeton.edu/cheatsheet/
https://wiki.python.org/moin/TimeComplexity
================================================
FILE: src/其他/vmware.md
================================================
```python
"""第一题： 线段数和三角形数"""
n = int(input())
if n == 1:
print(0, 0)
elif n == 2:
print(1, 0)
elif n == 3:
print(3, 1)
else:
# print(int(6+(n-4)/2*(2+(n-4)*2)), int(6+(n-4)/2*(2+(n-4)*2))-2)
print((n-3)*3+3, (n-3)*3+1)
"""第二题：最小交换次数"""
n = int(input())
tmp = input().split()
nums = [int(i) for i in tmp]
length = len(nums)
def sort1(nums):
res = 0
for i in range(length):
swapped = False
for j in range(length-1):
if nums[j] > nums[j+1]:
swapped = True
nums[j], nums[j+1] = nums[j+1], nums[j]
res += 1
if not swapped:
break
return res
def sort2(nums):
res = 0
for i in range(length):
swapped = False
for j in range(length-1):
if nums[j] < nums[j+1]:
swapped = True
nums[j], nums[j+1] = nums[j+1], nums[j]
res += 1
if not swapped:
break
return res
print(min(sort1(nums), sort2(nums)))
"""第三题： 搬家"""
tmp = input().split()
N, W = int(tmp[0]), int(tmp[1])
sums = 0
for i in range(N):
sums += int(input())
if sums == 0:
print(0)
else:
if sums <= W:
print(1)
elif sums == N * W:
print(N)
else:
print(sums//W + 1)
```
================================================
FILE: src/其他/wangyihuyudierti.java
================================================
import java.util.*;
public class Main {
public static void main(String[] args) {
Scanner sc = new Scanner(System.in);
int n = sc.nextInt();
for (int i=0; i<=n; i++){
String s = sc.nextLine();
// System.out.println(s);
String[] xynum = s.split(" ");
// System.out.println(xynum[0]);
int x, y;
try{
x = Integer.parseInt(xynum[0]);
}catch(NumberFormatException ex){ // handle your exception
continue;
}
try{
y = Integer.valueOf(xynum[1]);
}catch(NumberFormatException ex){ // handle your exception
continue;
}
// System.out.println((Helper(xynum[2].substring(0,2),x)));
for (int j=1; j=0;i--){
char chr = s.charAt(i);
int tmp;
if (chr == 'A') {
tmp = 10;
}else if(chr == 'B') {
tmp = 11;
}else if(chr == 'C') {
tmp = 12;
}else if(chr == 'D') {
tmp = 13;
}else if(chr == 'E') {
tmp = 14;
}else if(chr == 'F') {
tmp = 15;
}
else{
tmp = Integer.parseInt(Character.toString(chr));
}
// System.out.println("tmp"+tmp);
res += tmp * Math.pow(k, s.length()-1-i);
// System.out.println("res"+res);
}
return res;
}
}
/*
1
ABCDE
*/
================================================
FILE: src/其他/xunlei.md
================================================
```python
"""第一题"""
N = int(input())
def gcd(big, small):
if big < small:
small, big = big, small
remainder = big % small
if remainder == 0:
return small
else:
return gcd(small,remainder)
res = 0
from math import sqrt
for c in range(1, N+1):
for a in range(1, c):
for b in range(max(c-a+1,a), c):
if a * a + b * b == c * c:
if gcd(a, b) == 1 and gcd(b, c) == 1 and gcd(a, c) == 1:
res += 1
# print(a,b,c)
print(res)
"""第二题"""
tmp = input().split()
positive, negative = int(tmp[0]), int(tmp[1])
# print(positive, negative)
idx = 0
for i in range(6, -1, -1):
if i*positive+(7-i)*negative < 0:
idx = i
break
# print(idx)
res = (negative*(7-idx)+positive*idx) * 2
# print(res)
if idx >= 3:
res += 3 * positive
else:
res += idx * positive + (3 - idx) * negative
print(res)
```
================================================
FILE: src/其他/zhaoyincreditcard.md
================================================
```python
"""第一题"""
gi = [int(i) for i in input().split()]
sj = [int(i) for i in input().split()]
res = 0
for i in sj:
tmp = [j for j in gi if j <= i]
if tmp:
res += 1
gi.remove(max(tmp))
print(res)
"""第二题"""
n = int(input())
if n == 1:
print(1)
elif n == 2:
print(2)
else:
dp = [0] * n
dp[0] = 1
dp[1] = 2
for i in range(2, n):
dp[i] = dp[i-1] + dp[i-2]
print(dp[-1])
"""第三题"""
invalid = ['3', '4', '7']
nochange = ['0', '1', '8']
def good(n):
n = str(n)
for i in invalid:
if i in n:
return False
res = ''
for i in n:
if i in nochange:
res += i
if i == '2':
res += '5'
elif i == '5':
res += '2'
elif i == '6':
res += '9'
elif i == '9':
res += '6'
return res != n
n = int(input())
res = 0
for i in range(n+1):
if good(i):
res += 1
print(res)
```
================================================
FILE: src/其他/zhaoyinwangluokeji.java
================================================
import java.util.Scanner;
public class Main {
public static void main(String[] args) {
Scanner sc = new Scanner(System.in);
int n = sc.nextInt();
Helper(n);
}
private static void Helper(int n) {
int count = 90;
int start = 1;
String res = "";
// System.out.print((count * 1.0 - (count-1)*count/2.0)/count);
while ((count * 1.0 - (count-1)*count/2.0)/count < start*1.0){
for (int i = 0; i <= n / count; i++) {
if (count * i + (count-1) * count /2.0 == n) {
System.out.print("[");
res += "[";
for (int j =i; j <= i+count-1; j++){
if (j==i+count-1){
System.out.println(j+"]");
res += Integer.toString(j) + "]";
break;
}
System.out.print(j+", ");
res += Integer.toString(j) + ", ";
}
// System.out.print("]");
// System.out.println("");
}
}
count -= 1;
}
res = new StringBuffer(res).reverse().toString();
// System.out.print(res);
}
}
================================================
FILE: update.sh
================================================
git add -A
git commit -am "$(date "+%Y-%m-%d %H:%M:%S")"
git push