sklearn.isotonic.check_increasing(x, y)

class sklearn.multiclass.OutputCodeClassifier(estimator, code_size=1.5, random_state=None, n_jobs=1)

sklearn.datasets.make_sparse_spd_matrix(dim=1, alpha=0.95, norm_diag=False, smallest_coef=0.1, largest_coef=0.9, random_state=None)

class sklearn.decomposition.IncrementalPCA(n_components=None, whiten=False, copy=True, batch_size=None)

sklearn.preprocessing.minmax_scale(X, feature_range=(0, 1), axis=0, copy=True)

Warning DEPRECATED class sklearn

class sklearn.model_selection.ShuffleSplit(n_splits=10, test_size=0.1, train_size=None, random_state=None)

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

class sklearn.model_selection.LeavePGroupsOut(n_groups)

sklearn.metrics.pairwise_distances(X, Y=None, metric='euclidean', n_jobs=1, **kwds)