ROC curves for LDA classifiers

TL;DR

This paper derives an analytical formula for ROC curves of LDA classifiers, explores their properties, and demonstrates their application on a real dataset, enhancing understanding of classifier performance metrics.

Contribution

It introduces a novel analytic formula for ROC curves of LDA classifiers and analyzes their properties, which was not previously available.

Findings

Derived explicit ROC curve formula for LDA classifiers

Established properties like monotonicity and concavity of ROC curves

Applied results to real breast cancer dataset

Abstract

In the paper, we derive an analytic formula for the ROC curves of the LDA classifiers. We establish elementary properties of these curves (monotonicity and concavity), provide formula for the area under curve (AUC) and compute the Youden J-index. Finally, we illustrate the performance of our results on a real--life dataset of Wisconsin breast cancer patients.