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

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

sklearn.metrics.get_scorer(scoring)

sklearn.metrics.pairwise.kernel_metrics()

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

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

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

sklearn.metrics.pairwise.sigmoid_kernel(X, Y=None, gamma=None, coef0=1)

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

sklearn.metrics.completeness_score(labels_true, labels_pred)