autointent.metrics.decision.decision_roc_auc

autointent.metrics.decision.decision_roc_auc(y_true, y_pred)

Calculate ROC AUC for multiclass and multilabel classification.

The ROC AUC measures the ability of a model to distinguish between classes. It is calculated as the area under the curve of the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.

Parameters:

• y_true (autointent.metrics.custom_types.LABELS_VALUE_TYPE) – True values of labels

• y_pred (autointent.metrics.custom_types.LABELS_VALUE_TYPE) – Predicted values of labels

Returns:

Score of the decision ROC AUC

Return type:

float