sklearn.metrics.fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='binary', sample_weight=None)

sklearn.manifold.spectral_embedding(adjacency, n_components=8, eigen_solver=None, random_state=None, eigen_tol=0.0, norm_laplacian=True

class sklearn.feature_selection.SelectKBest(score_func=, k=10)

sklearn.datasets.fetch_lfw_people(data_home=None, funneled=True, resize=0.5, min_faces_per_person=0, color=False, slice_=(slice(70

sklearn.metrics.homogeneity_completeness_v_measure(labels_true, labels_pred)

class sklearn.decomposition.PCA(n_components=None, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None)

class sklearn.neighbors.NearestCentroid(metric='euclidean', shrink_threshold=None)

sklearn.datasets.make_friedman2(n_samples=100, noise=0.0, random_state=None)

class sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(100, ), activation='relu', solver='adam', alpha=0.0001, batch_size='auto'

class sklearn.linear_model.HuberRegressor(epsilon=1.35, max_iter=100, alpha=0.0001, warm_start=False, fit_intercept=True, tol=1e-05)