class sklearn.base.RegressorMixin

class sklearn.covariance.LedoitWolf(store_precision=True, assume_centered=False, block_size=1000)

sklearn.model_selection.fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, error_score='raise', **fit_params)

class sklearn.gaussian_process.kernels.Product(k1, k2)

class sklearn.gaussian_process.kernels.Exponentiation(kernel, exponent)

sklearn.metrics.pairwise.distance_metrics()

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

sklearn.linear_model.lars_path(X, y, Xy=None, Gram=None, max_iter=500, alpha_min=0, method='lar', copy_X=True, eps=2.2204460492503131e-16

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

sklearn.decomposition.sparse_encode(X, dictionary, gram=None, cov=None, algorithm='lasso_lars', n_nonzero_coefs=None, alpha=None