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

class sklearn.linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, precompute=False, max_iter=1000

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

sklearn.metrics.calinski_harabaz_score(X, labels)

class sklearn.feature_extraction.text.CountVectorizer(input=u'content', encoding=u'utf-8', decode_error=u'strict',

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

sklearn.metrics.median_absolute_error(y_true, y_pred)

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

class sklearn.cluster.MeanShift(bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, n_jobs=1)

class sklearn.ensemble.ExtraTreesRegressor(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1