class sklearn.kernel_approximation.RBFSampler(gamma=1.0, n_components=100, random_state=None)

class sklearn.linear_model.RidgeClassifier(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001

sklearn.cluster.affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200, damping=0.5, copy=True, verbose=False

sklearn.model_selection.train_test_split(*arrays, **options)

sklearn.metrics.pairwise.paired_euclidean_distances(X, Y)

sklearn.ensemble.partial_dependence.plot_partial_dependence(gbrt, X, features, feature_names=None, label=None

sklearn.covariance.ledoit_wolf(X, assume_centered=False, block_size=1000)

class sklearn.linear_model.OrthogonalMatchingPursuit(n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize=True

sklearn.cluster.ward_tree(X, connectivity=None, n_clusters=None, return_distance=False)

class sklearn.neighbors.BallTree BallTree for fast generalized N-point problems BallTree(X, leaf_size=40, metric=