The VOROS: Lifting ROC curves to 3D

arXiv:2402.18689v1 Announce Type: new
Abstract: The area under the ROC curve is a common measure that is often used to rank the relative performance of different binary classifiers. However, as has been also previously noted, it can be a measure that ill-captures the benefits of different classifiers when either the true class values or misclassification costs are highly unbalanced between the two classes. We introduce a third dimension to capture these costs, and lift the ROC curve to a ROC …

abstract arxiv benefits binary class classifiers cs.lg math.mg math.st performance roc stat.me stat.th true type values