The decision tree structure can be analysed to gain further insight on the relation between the features and the target to predict. In this example, we show how to retrieve: the binary tree structure; the depth of each node and whether or not it?s a leaf; the nodes that were reached by a sample using the decision_path method; the leaf that was reached by a sample using the apply method; the rules that were used to predict a sample; the decision path shared by a group of samples. Out: The b

This example fits an AdaBoosted decision stump on a non-linearly separable classification dataset composed of two ?Gaussian quantiles? clusters (see sklearn.datasets.make_gaussian_quantiles) and plots the decision boundary and decision scores. The distributions of decision scores are shown separately for samples of class A and B. The predicted class label for each sample is determined by the sign of the decision score. Samples with decision scores greater than zero are classified as B, and are

class sklearn.tree.ExtraTreeRegressor(criterion='mse', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', random_state=None, min_impurity_split=1e-07, max_leaf_nodes=None) [source] An extremely randomized tree regressor. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the m

class sklearn.tree.ExtraTreeClassifier(criterion='gini', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', random_state=None, max_leaf_nodes=None, min_impurity_split=1e-07, class_weight=None) [source] An extremely randomized tree classifier. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are d

class sklearn.tree.DecisionTreeRegressor(criterion='mse', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_split=1e-07, presort=False) [source] A decision tree regressor. Read more in the User Guide. Parameters: criterion : string, optional (default=?mse?) The function to measure the quality of a split. Supported criteria are ?mse? for the mean squared error, whic

class sklearn.tree.DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_split=1e-07, class_weight=None, presort=False) [source] A decision tree classifier. Read more in the User Guide. Parameters: criterion : string, optional (default=?gini?) The function to measure the quality of a split. Supported criteria are ?gini? for the

Illustration of how the performance of an estimator on unseen data (test data) is not the same as the performance on training data. As the regularization increases the performance on train decreases while the performance on test is optimal within a range of values of the regularization parameter. The example with an Elastic-Net regression model and the performance is measured using the explained variance a.k.a. R^2. print(__doc__) # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr&g

The Johnson-Lindenstrauss lemma states that any high dimensional dataset can be randomly projected into a lower dimensional Euclidean space while controlling the distortion in the pairwise distances. Theoretical bounds The distortion introduced by a random projection p is asserted by the fact that p is defining an eps-embedding with good probability as defined by: Where u and v are any rows taken from a dataset of shape [n_samples, n_features] and p is a projection by a random Gaussian N(0

Computes a Theil-Sen Regression on a synthetic dataset. See Theil-Sen estimator: generalized-median-based estimator for more information on the regressor. Compared to the OLS (ordinary least squares) estimator, the Theil-Sen estimator is robust against outliers. It has a breakdown point of about 29.3% in case of a simple linear regression which means that it can tolerate arbitrary corrupted data (outliers) of up to 29.3% in the two-dimensional case. The estimation of the model is done by calcu

This is an example of applying Non-negative Matrix Factorization and Latent Dirichlet Allocation on a corpus of documents and extract additive models of the topic structure of the corpus. The output is a list of topics, each represented as a list of terms (weights are not shown). The default parameters (n_samples / n_features / n_topics) should make the example runnable in a couple of tens of seconds. You can try to increase the dimensions of the problem, but be aware that the time complexity