sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None

sklearn.metrics.get_scorer(scoring)

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

sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)

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

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

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

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

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

sklearn.metrics.completeness_score(labels_true, labels_pred)