Metrics

Classification Metrics

metrics.accuracy_score(y_true, y_pred[, ...])	Accuracy classification score.
metrics.auc(x, y[, execute, session, run_kwargs])	Compute Area Under the Curve (AUC) using the trapezoidal rule
metrics.f1_score(y_true, y_pred, *[, ...])	Compute the F1 score, also known as balanced F-score or F-measure
metrics.fbeta_score(y_true, y_pred, *, beta)	Compute the F-beta score
metrics.log_loss(y_true, y_pred, *[, eps, ...])	Log loss, aka logistic loss or cross-entropy loss.
metrics.multilabel_confusion_matrix(y_true, ...)	Compute a confusion matrix for each class or sample.
metrics.precision_recall_fscore_support(...)	Compute precision, recall, F-measure and support for each class
metrics.precision_score(y_true, y_pred, *[, ...])	Compute the precision
metrics.recall_score(y_true, y_pred, *[, ...])	Compute the recall
metrics.roc_auc_score(y_true, y_score, *[, ...])	Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
metrics.roc_curve(y_true, y_score[, ...])	Compute Receiver operating characteristic (ROC)

Regression Metrics

metrics.r2_score(y_true, y_pred, *[, ...])

\(R^2\) (coefficient of determination) regression score function.

Pairwise metrics

metrics.pairwise.cosine_distances(X[, Y])	Compute cosine distance between samples in X and Y.
metrics.pairwise.cosine_similarity(X[, Y, ...])	Compute cosine similarity between samples in X and Y.
metrics.pairwise.euclidean_distances(X[, Y, ...])	Considering the rows of X (and Y=X) as vectors, compute the distance matrix between each pair of vectors.
metrics.pairwise.haversine_distances(X[, Y])	Compute the Haversine distance between samples in X and Y
metrics.pairwise.manhattan_distances(X[, Y])	Compute the L1 distances between the vectors in X and Y.
metrics.pairwise.rbf_kernel(X[, Y, gamma])	Compute the rbf (gaussian) kernel between X and Y.
metrics.pairwise_distances(X[, Y, metric])