sklearn.metrics.pairwise.pairwise_distances(X, Y=None, metric='euclidean', n_jobs=1, **kwds)

sklearn.metrics.pairwise.rbf_kernel(X, Y=None, gamma=None)

sklearn.metrics.fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='binary', sample_weight=None)

sklearn.metrics.homogeneity_score(labels_true, labels_pred)

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

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

sklearn.metrics.label_ranking_average_precision_score(y_true, y_score)

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

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

sklearn.metrics.hinge_loss(y_true, pred_decision, labels=None, sample_weight=None)