F*: An Interpretable Transformation of the F-measure

TL;DR

The paper introduces F*, a transformed version of the F-measure that offers a more intuitive interpretation for evaluating classification performance.

Contribution

It proposes a simple transformation of the F-measure, called F*, that provides clearer practical interpretability compared to the traditional F1-score.

Findings

F* has an immediate practical interpretation

F* addresses interpretability issues of the F-measure

The transformation improves understanding of classification performance

Abstract

The F-measure, also known as the F1-score, is widely used to assess the performance of classification algorithms. However, some researchers find it lacking in intuitive interpretation, questioning the appropriateness of combining two aspects of performance as conceptually distinct as precision and recall, and also questioning whether the harmonic mean is the best way to combine them. To ease this concern, we describe a simple transformation of the F-measure, which we call F* (F-star), which has an immediate practical interpretation.