SYMBOL INDEX (30 symbols across 14 files)

FILE: fetch_data.py
  function get_datasets_folder (line 15) | def get_datasets_folder():
  function check_imdb (line 35) | def check_imdb(datasets_folder):

FILE: notebooks/figures/ML_flow_chart.py
  function create_base (line 12) | def create_base(box_bg='#CCCCCC',
  function plot_supervised_chart (line 106) | def plot_supervised_chart(annotate=False):
  function plot_unsupervised_chart (line 124) | def plot_unsupervised_chart():

FILE: notebooks/figures/plot_2d_separator.py
  function plot_2d_separator (line 5) | def plot_2d_separator(classifier, X, fill=False, ax=None, eps=None):

FILE: notebooks/figures/plot_digits_dataset.py
  function digits_plot (line 14) | def digits_plot():

FILE: notebooks/figures/plot_interactive_forest.py
  function plot_forest (line 11) | def plot_forest(max_depth=1):
  function plot_forest_interactive (line 36) | def plot_forest_interactive():

FILE: notebooks/figures/plot_interactive_tree.py
  function tree_image (line 17) | def tree_image(tree, fout=None):
  function plot_tree (line 38) | def plot_tree(max_depth=1):
  function plot_tree_interactive (line 68) | def plot_tree_interactive():

FILE: notebooks/figures/plot_kneighbors_regularization.py
  function make_dataset (line 7) | def make_dataset(n_samples=100):
  function plot_regression_datasets (line 15) | def plot_regression_datasets():
  function plot_kneighbors_regularization (line 22) | def plot_kneighbors_regularization():

FILE: notebooks/figures/plot_linear_svc_regularization.py
  function plot_linear_svc_regularization (line 8) | def plot_linear_svc_regularization():

FILE: notebooks/figures/plot_pca.py
  function plot_pca_illustration (line 6) | def plot_pca_illustration():
  function plot_pca_whitening (line 64) | def plot_pca_whitening():

FILE: notebooks/figures/plot_rbf_svm_parameters.py
  function make_handcrafted_dataset (line 8) | def make_handcrafted_dataset():
  function plot_rbf_svm_parameters (line 18) | def plot_rbf_svm_parameters():
  function plot_svm (line 37) | def plot_svm(log_C, log_gamma):
  function plot_svm_interactive (line 52) | def plot_svm_interactive():

FILE: notebooks/figures/plot_scaling.py
  function plot_scaling (line 9) | def plot_scaling():
  function plot_relative_scaling (line 44) | def plot_relative_scaling():

FILE: notebooks/helpers.py
  function process_titanic_line (line 8) | def process_titanic_line(line):
  function load_titanic (line 40) | def load_titanic(test_size=.25, feature_skip_tuple=(), random_state=1999):
  function read_sentiment_csv (line 98) | def read_sentiment_csv(csv_file, fieldnames=FIELDNAMES, max_count=None,

FILE: notebooks/solutions/16A_avg_per_class_acc.py
  function accuracy (line 1) | def accuracy(true, pred):
  function macro (line 5) | def macro(true, pred):

FILE: notebooks/solutions/23_batchtrain.py
  function batch_train (line 9) | def batch_train(clf, fnames, labels, iterations=1,