A 1D regression with decision tree. The decision trees is used to fit a sine curve with addition noisy observation. As a result, it learns local linear regressions approximating the sine curve. We can see that if the maximum depth of the tree (controlled by the max_depth parameter) is set too high, the decision trees learn too fine details of the training data and learn from the noise, i.e. they overfit. print(__doc__) # Import the necessary modules and libraries import numpy as np from s

Comparison for decision boundary generated on iris dataset between Label Propagation and SVM. This demonstrates Label Propagation learning a good boundary even with a small amount of labeled data. print(__doc__) # Authors: Clay Woolam <clay@woolam.org> # License: BSD import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn import svm from sklearn.semi_supervised import label_propagation rng = np.random.RandomState(0) iris = datasets.load_iris()

Warning DEPRECATED class sklearn.cross_validation.StratifiedShuffleSplit(y, n_iter=10, test_size=0.1, train_size=None, random_state=None) [source] Stratified ShuffleSplit cross validation iterator Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.StratifiedShuffleSplit instead. Provides train/test indices to split data in train test sets. This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified ra

Warning DEPRECATED class sklearn.cross_validation.StratifiedKFold(y, n_folds=3, shuffle=False, random_state=None) [source] Stratified K-Folds cross validation iterator Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.StratifiedKFold instead. Provides train/test indices to split data in train test sets. This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of sampl

Warning DEPRECATED class sklearn.cross_validation.ShuffleSplit(n, n_iter=10, test_size=0.1, train_size=None, random_state=None) [source] Random permutation cross-validation iterator. Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.ShuffleSplit instead. Yields indices to split data into training and test sets. Note: contrary to other cross-validation strategies, random splits do not guarantee that all folds will be different, although this

Warning DEPRECATED class sklearn.cross_validation.PredefinedSplit(test_fold) [source] Predefined split cross validation iterator Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.PredefinedSplit instead. Splits the data into training/test set folds according to a predefined scheme. Each sample can be assigned to at most one test set fold, as specified by the user through the test_fold parameter. Read more in the User Guide. Parameters: test

Warning DEPRECATED class sklearn.cross_validation.LeavePOut(n, p) [source] Leave-P-Out cross validation iterator Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.LeavePOut instead. Provides train/test indices to split data in train test sets. This results in testing on all distinct samples of size p, while the remaining n - p samples form the training set in each iteration. Note: LeavePOut(n, p) is NOT equivalent to KFold(n, n_folds=n // p)

Warning DEPRECATED class sklearn.cross_validation.LeavePLabelOut(labels, p) [source] Leave-P-Label_Out cross-validation iterator Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.LeavePGroupsOut instead. Provides train/test indices to split data according to a third-party provided label. This label information can be used to encode arbitrary domain specific stratifications of the samples as integers. For instance the labels could be the year

Warning DEPRECATED class sklearn.cross_validation.LeaveOneOut(n) [source] Leave-One-Out cross validation iterator. Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.LeaveOneOut instead. Provides train/test indices to split data in train test sets. Each sample is used once as a test set (singleton) while the remaining samples form the training set. Note: LeaveOneOut(n) is equivalent to KFold(n, n_folds=n) and LeavePOut(n, p=1). Due to the hig

Warning DEPRECATED class sklearn.cross_validation.LeaveOneLabelOut(labels) [source] Leave-One-Label_Out cross-validation iterator Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.LeaveOneGroupOut instead. Provides train/test indices to split data according to a third-party provided label. This label information can be used to encode arbitrary domain specific stratifications of the samples as integers. For instance the labels could be the ye