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

class sklearn.decomposition.MiniBatchSparsePCA(n_components=None, alpha=1, ridge_alpha=0.01, n_iter=100, callback=None, batch_size=3

sklearn.pipeline.make_pipeline(*steps)

class sklearn.kernel_approximation.Nystroem(kernel='rbf', gamma=None, coef0=1, degree=3, kernel_params=None, n_components=100

sklearn.metrics.calinski_harabaz_score(X, labels)

class sklearn.neighbors.KDTree KDTree for fast generalized N-point problems KDTree(X, leaf_size=40, metric=?minkowski

sklearn.linear_model.lasso_path(X, y, eps=0.001, n_alphas=100, alphas=None, precompute='auto', Xy=None, copy_X=True, coef_init=None

class sklearn.linear_model.ElasticNetCV(l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False,

class sklearn.gaussian_process.kernels.Matern(length_scale=1.0, length_scale_bounds=(1e-05, 100000.0), nu=1.5)

class sklearn.ensemble.GradientBoostingRegressor(loss='ls', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse'