sklearn.metrics.zero_one_loss(y_true, y_pred, normalize=True, sample_weight=None) [source] Zero-one classification loss. If normalize is True, return the fraction of misclassifications (float), else it returns the number of misclassifications (int). The best performance is 0. Read more in the User Guide. Parameters: y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) labels. y_pred : 1d array-like, or label indicator array / sparse matrix Predicted la

sklearn.metrics.v_measure_score(labels_true, labels_pred) [source] V-measure cluster labeling given a ground truth. This score is identical to normalized_mutual_info_score. The V-measure is the harmonic mean between homogeneity and completeness: v = 2 * (homogeneity * completeness) / (homogeneity + completeness) This metric is independent of the absolute values of the labels: a permutation of the class or cluster label values won?t change the score value in any way. This metric is furtherm

sklearn.metrics.silhouette_score(X, labels, metric='euclidean', sample_size=None, random_state=None, **kwds) [source] Compute the mean Silhouette Coefficient of all samples. The Silhouette Coefficient is calculated using the mean intra-cluster distance (a) and the mean nearest-cluster distance (b) for each sample. The Silhouette Coefficient for a sample is (b - a) / max(a, b). To clarify, b is the distance between a sample and the nearest cluster that the sample is not a part of. Note that

sklearn.metrics.silhouette_samples(X, labels, metric='euclidean', **kwds) [source] Compute the Silhouette Coefficient for each sample. The Silhouette Coefficient is a measure of how well samples are clustered with samples that are similar to themselves. Clustering models with a high Silhouette Coefficient are said to be dense, where samples in the same cluster are similar to each other, and well separated, where samples in different clusters are not very similar to each other. The Silhouett

sklearn.metrics.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True) [source] Compute Receiver operating characteristic (ROC) Note: this implementation is restricted to the binary classification task. Read more in the User Guide. Parameters: y_true : array, shape = [n_samples] True binary labels in range {0, 1} or {-1, 1}. If labels are not binary, pos_label should be explicitly given. y_score : array, shape = [n_samples] Target scores, can either be pr

sklearn.metrics.roc_auc_score(y_true, y_score, average='macro', sample_weight=None) [source] Compute Area Under the Curve (AUC) from prediction scores Note: this implementation is restricted to the binary classification task or multilabel classification task in label indicator format. Read more in the User Guide. Parameters: y_true : array, shape = [n_samples] or [n_samples, n_classes] True binary labels in binary label indicators. y_score : array, shape = [n_samples] or [n_samples, n_cl

sklearn.metrics.recall_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None) [source] Compute the recall The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples. The best value is 1 and the worst value is 0. Read more in the User Guide. Parameters: y_true : 1d array-like, or label indicator array / sparse matr

sklearn.metrics.precision_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None) [source] Compute the precision The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0. Read more in the User Guide. Parameters: y_true : 1d array-like, or label

sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None, warn_for=('precision', 'recall', 'f-score'), sample_weight=None) [source] Compute precision, recall, F-measure and support for each class The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The recall

sklearn.metrics.r2_score(y_true, y_pred, sample_weight=None, multioutput=None) [source] R^2 (coefficient of determination) regression score function. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0. Read more in the User Guide. Parameters: y_true : array-like of shape = (n_samples) or (n_samples, n_outputs) Ground trut