sklearn.metrics.consensus_score(a, b, similarity='jaccard')

sklearn.datasets.make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=2, n_repeated=0, n_classes=2

sklearn.metrics.pairwise.euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False, X_norm_squared=None)

class sklearn.pipeline.Pipeline(steps)

sklearn.preprocessing.normalize(X, norm='l2', axis=1, copy=True, return_norm=False)

sklearn.metrics.make_scorer(score_func, greater_is_better=True, needs_proba=False, needs_threshold=False, **kwargs)

class sklearn.linear_model.MultiTaskLassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, max_iter=1000

class sklearn.model_selection.LeavePOut(p)

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

sklearn.metrics.explained_variance_score(y_true, y_pred, sample_weight=None, multioutput='uniform_average')