F*: An Interpretable Transformation of the F-measure
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jul 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1; 2026: 3.
7 papers.
- 2024Selecting a classification performance measure: matching the measure to the problemcited by 2
- 2022Privacy-preserving Deep Learning based Record Linkagecited by 1
- 2024Fβ-plot -- a visual tool for evaluating imbalanced data classifierscited by 1
- 2026A Pragmatic Method for Comparing Clusterings with Overlaps and Outlierscited by 1
- 2025Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimizationcited by 0
- 2026WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruningcited by 0
- 2026Rank-Constrained Deep Matrix Completion for Group Recommendationcited by 0
- 2026Rank-Constrained Deep Matrix Completion for Group Recommendation1 Jun 2026
- 2026A Pragmatic Method for Comparing Clusterings with Overlaps and Outliers16 Feb 2026
- 2026WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning13 Feb 2026
- 2025Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization27 Nov 2025
- 2024Selecting a classification performance measure: matching the measure to the problem19 Sep 2024
- 2024Fβ-plot -- a visual tool for evaluating imbalanced data classifiers11 Apr 2024
- 2022Privacy-preserving Deep Learning based Record Linkage3 Nov 2022