sklearn.metrics.fowlkes_mallows_score(labels_true, labels_pred, sparse=False)

sklearn.datasets.make_low_rank_matrix(n_samples=100, n_features=100, effective_rank=10, tail_strength=0.5, random_state=None)

Warning DEPRECATED

sklearn.datasets.fetch_mldata(dataname, target_name='label', data_name='data', transpose_data=True, data_home=None)

sklearn.datasets.make_moons(n_samples=100, shuffle=True, noise=None, random_state=None)

The goal of ensemble methods is to combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability

Warning DEPRECATED class sklearn

class sklearn.neighbors.KernelDensity(bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True

This example aims at showing characteristics of different clustering algorithms on datasets that are ?interesting? but still in 2D

class sklearn.linear_model.MultiTaskLasso(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=1000, tol=0.0001