sklearn.metrics.completeness_score(labels_true, labels_pred)

class sklearn.semi_supervised.LabelPropagation(kernel='rbf', gamma=20, n_neighbors=7, alpha=1, max_iter=30, tol=0.001, n_jobs=1)

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

sklearn.preprocessing.maxabs_scale(X, axis=0, copy=True)

class sklearn.linear_model.LassoLarsCV(fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None

sklearn.datasets.load_svmlight_file(f, n_features=None, dtype=, multilabel=False, zero_based='auto', query_id=False)

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

sklearn.model_selection.learning_curve(estimator, X, y, groups=None, train_sizes=array([ 0.1, 0.33, 0.55, 0.78, 1. ]), cv=None

sklearn.metrics.pairwise.pairwise_kernels(X, Y=None, metric='linear', filter_params=False, n_jobs=1, **kwds)

class sklearn.linear_model.MultiTaskElasticNet(alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, copy_X=True,