The ROC Curve and the Area under It as Performance Measures

TLDR

The ROC curve is a two‑dimensional measure of classification performance, while the area under the ROC curve (AUC) provides a scalar gauge of one facet of that performance. The article uses five idealized models to relate ROC curve shape and AUC to features of the underlying forecast distribution. Five idealized models are employed to link ROC curve shape and AUC to distributional characteristics of forecasts. The analysis shows that ROC asymmetry reflects unequal distribution widths, and that AUC distinguishes good from bad models but not among good ones, providing a clear pedagogical illustration of these relationships.

Abstract

Abstract The receiver operating characteristic (ROC) curve is a two-dimensional measure of classification performance. The area under the ROC curve (AUC) is a scalar measure gauging one facet of performance. In this short article, five idealized models are utilized to relate the shape of the ROC curve, and the area under it, to features of the underlying distribution of forecasts. This allows for an interpretation of the former in terms of the latter. The analysis is pedagogical in that many of the findings are already known in more general (and more realistic) settings; however, the simplicity of the models considered here allows for a clear exposition of the relation. For example, although in general there are many reasons for an asymmetric ROC curve, the models considered here clearly illustrate that an asymmetry in the ROC curve can be attributed to unequal widths of the distributions. Furthermore, it is shown that AUC discriminates well between “good” and “bad” models, but not between good models.