class sklearn.decomposition.FactorAnalysis(n_components=None, tol=0.01, copy=True, max_iter=1000, noise_variance_init=None, s

sklearn.covariance.graph_lasso(emp_cov, alpha, cov_init=None, mode='cd', tol=0.0001, enet_tol=0.0001, max_iter=100, verbose=False

sklearn.utils.shuffle(*arrays, **options)

class sklearn.gaussian_process.kernels.Kernel

class sklearn.exceptions.ChangedBehaviorWarning

sklearn.preprocessing.normalize(X, norm='l2', axis=1, copy=True, return_norm=False)

sklearn.preprocessing.robust_scale(X, axis=0, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)

class sklearn.preprocessing.LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False)

class sklearn.multioutput.MultiOutputRegressor(estimator, n_jobs=1)

class sklearn.gaussian_process.kernels.DotProduct(sigma_0=1.0, sigma_0_bounds=(1e-05, 100000.0))