sklearn.metrics.log_loss(y_true, y_pred, eps=1e-15, normalize=True, sample_weight=None, labels=None)

sklearn.metrics.brier_score_loss(y_true, y_prob, sample_weight=None, pos_label=None)

sklearn.metrics.silhouette_samples(X, labels, metric='euclidean', **kwds)

sklearn.metrics.pairwise_distances_argmin(X, Y, axis=1, metric='euclidean', batch_size=500, metric_kwargs=None)

sklearn.metrics.get_scorer(scoring)

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

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

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

sklearn.metrics.pairwise.kernel_metrics()

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