Plot the decision surface of a decision tree trained on pairs of features of the iris dataset. See

Demonstration of several covariances types for Gaussian mixture models. See

class sklearn.manifold.LocallyLinearEmbedding(n_neighbors=5, n_components=2, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100

sklearn.metrics.coverage_error(y_true, y_score, sample_weight=None)

sklearn.cluster.estimate_bandwidth(X, quantile=0.3, n_samples=None, random_state=0, n_jobs=1)

Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. The diagonal elements represent the number of points for which

An example illustrating the approximation of the feature map of an RBF kernel. It shows how to use

This example simulates a multi-label document classification problem. The dataset is generated randomly based on the following process: pick

class sklearn.model_selection.LeaveOneGroupOut

class sklearn.decomposition.DictionaryLearning(n_components=None, alpha=1, max_iter=1000, tol=1e-08, fit_algorithm='lars'