# F*: An Interpretable Transformation of the F-measure

arXiv: https://arxiv.org/abs/2008.00103 · HTML: https://mlsanity.com/paper/2008.00103/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [Selecting a classification performance measure: matching the measure to the problem](https://mlsanity.com/paper/2409.12391/) (2024)
- [Privacy-preserving Deep Learning based Record Linkage](https://mlsanity.com/paper/2211.02161/) (2022)
- [Fβ-plot -- a visual tool for evaluating imbalanced data classifiers](https://mlsanity.com/paper/2404.08709/) (2024)
- [A Pragmatic Method for Comparing Clusterings with Overlaps and Outliers](https://mlsanity.com/paper/2602.14855/) (2026)
- [Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization](https://mlsanity.com/paper/2511.22169/) (2025)
- [WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning](https://mlsanity.com/paper/2602.12852/) (2026)
- [Rank-Constrained Deep Matrix Completion for Group Recommendation](https://mlsanity.com/paper/2606.01948/) (2026)

## Abstract (arXiv)

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.