Large-scale Optimization of Partial AUC in a Range of False Positive Rates. (arXiv:2203.01505v2 [cs.LG] UPDATED)

The area under the ROC curve (AUC) is one of the most widely used performance
measures for classification models in machine learning. However, it summarizes
the true positive rates (TPRs) over all false positive rates (FPRs) in the ROC
space, which may include the FPRs with no practical relevance in some
applications. The partial AUC, as a generalization of the AUC, summarizes only
the TPRs over a specific range of the FPRs and is thus a more suitable
performance measure …

arxiv auc false optimization positive scale