sklearn.datasets.make_friedman3(n_samples=100, noise=0.0, random_state=None)

class sklearn.pipeline.FeatureUnion(transformer_list, n_jobs=1, transformer_weights=None)

class sklearn.model_selection.RandomizedSearchCV(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None

class sklearn.base.TransformerMixin

class sklearn.decomposition.ProjectedGradientNMF(*args, **kwargs)

sklearn.datasets.load_linnerud(return_X_y=False)

class sklearn.model_selection.PredefinedSplit(test_fold)

sklearn.metrics.pairwise.paired_distances(X, Y, metric='euclidean', **kwds)

class sklearn.linear_model.RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None

class sklearn.feature_selection.GenericUnivariateSelect(score_func=, mode='percentile', param=1e-05)