class sklearn.preprocessing.StandardScaler(copy=True, with_mean=True, with_std=True)

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

sklearn.metrics.median_absolute_error(y_true, y_pred)

class sklearn.svm.OneClassSVM(kernel='rbf', degree=3, gamma='auto', coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size=200, verbose=False

class sklearn.decomposition.SparseCoder(dictionary, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None

class sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None)

class sklearn.kernel_ridge.KernelRidge(alpha=1, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None)

sklearn.preprocessing.scale(X, axis=0, with_mean=True, with_std=True, copy=True)

sklearn.datasets.fetch_covtype(data_home=None, download_if_missing=True, random_state=None, shuffle=False)

class sklearn.linear_model.TheilSenRegressor(fit_intercept=True, copy_X=True, max_subpopulation=10000.0, n_subsamples=None,