sklearn.metrics.pairwise.kernel_metrics()
sklearn.metrics.get_scorer(scoring)
sklearn.metrics.hinge_loss(y_true, pred_decision, labels=None, sample_weight=None)
sklearn.metrics.make_scorer(score_func, greater_is_better=True, needs_proba=False, needs_threshold=False, **kwargs)
sklearn.metrics.mean_absolute_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average')
sklearn.metrics.label_ranking_average_precision_score(y_true, y_score)
sklearn.metrics.silhouette_samples(X, labels, metric='euclidean', **kwds)
sklearn.metrics.brier_score_loss(y_true, y_prob, sample_weight=None, pos_label=None)
sklearn.metrics.log_loss(y_true, y_pred, eps=1e-15, normalize=True, sample_weight=None, labels=None)
sklearn.metrics.pairwise.manhattan_distances(X, Y=None, sum_over_features=True, size_threshold=500000000.0)
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