sklearn.metrics.pairwise.pairwise_distances(X, Y=None, metric='euclidean', n_jobs=1, **kwds)
sklearn.metrics.matthews_corrcoef(y_true, y_pred, sample_weight=None)
sklearn.metrics.homogeneity_score(labels_true, labels_pred)
sklearn.metrics.f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None)
sklearn.metrics.pairwise_distances_argmin(X, Y, axis=1, metric='euclidean', batch_size=500, metric_kwargs=None)
sklearn.metrics.consensus_score(a, b, similarity='jaccard')
sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None
sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)
sklearn.metrics.pairwise.euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False, X_norm_squared=None)
sklearn.metrics.pairwise.sigmoid_kernel(X, Y=None, gamma=None, coef0=1)
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