Objectives

Logloss

CrossEntropy

metrics

Classification metrics

Precision -
Recall -
F -
F1 -
BalancedAccuracy -
BalancedErrorRate -
MCC -
Accuracy -
CtrFactor -
AUC -
QueryAUC -
NormalizedGini -
BrierScore -
HingeLoss -
HammingLoss -
ZeroOneLoss -
Kappa -
WKappa -
LogLikelihoodOfPrediction -

Classification metrics

See the Classification metrics section of the user guide for further details.

metrics.accuracy_score(y_true, y_pred, *[, …]) Accuracy classification score.
metrics.auc(x, y) Compute Area Under the Curve (AUC) using the trapezoidal rule.
metrics.average_precision_score(y_true, …) Compute average precision (AP) from prediction scores.
metrics.balanced_accuracy_score(y_true, …) Compute the balanced accuracy.
metrics.brier_score_loss(y_true, y_prob, *) Compute the Brier score loss.
metrics.classification_report(y_true, y_pred, *) Build a text report showing the main classification metrics.
metrics.cohen_kappa_score(y1, y2, *[, …]) Cohen’s kappa: a statistic that measures inter-annotator agreement.
metrics.confusion_matrix(y_true, y_pred, *) Compute confusion matrix to evaluate the accuracy of a classification.
metrics.dcg_score(y_true, y_score, *[, k, …]) Compute Discounted Cumulative Gain.
metrics.det_curve(y_true, y_score[, …]) Compute error rates for different probability thresholds.
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.hamming_loss(y_true, y_pred, *[, …]) Compute the average Hamming loss.
metrics.hinge_loss(y_true, pred_decision, *) Average hinge loss (non-regularized).
metrics.jaccard_score(y_true, y_pred, *[, …]) Jaccard similarity coefficient score.
metrics.log_loss(y_true, y_pred, *[, eps, …]) Log loss, aka logistic loss or cross-entropy loss.
metrics.matthews_corrcoef(y_true, y_pred, *) Compute the Matthews correlation coefficient (MCC).
metrics.multilabel_confusion_matrix(y_true, …) Compute a confusion matrix for each class or sample.
metrics.ndcg_score(y_true, y_score, *[, k, …]) Compute Normalized Discounted Cumulative Gain.
metrics.precision_recall_curve(y_true, …) Compute precision-recall pairs for different probability thresholds.
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).
metrics.top_k_accuracy_score(y_true, y_score, *) Top-k Accuracy classification score.
metrics.zero_one_loss(y_true, y_pred, *[, …]) Zero-one classification loss.

https://catboost.ai/en/docs/concepts/loss-functions-classification#used-for-optimization