Optimizing Two-Way Partial AUC With an End-to-End Framework
Optimizing Two-Way Partial AUC With an End-to-End Framework
The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive Rates (FPRs). Based on the knowledge that a skillful classifier should simultaneously embrace a high TPR and a low FPR, we …