# Notes on the H-measure of classifier performance

arXiv: https://arxiv.org/abs/2106.11888 · HTML: https://mlsanity.com/paper/2106.11888/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 24 references that are in the map.
- First posted on arXiv 22 Jun 2021.

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

- [Evaluating Probabilistic Classifiers: The Triptych](https://mlsanity.com/paper/2301.10803/) (2023)
- [t-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparators](https://mlsanity.com/paper/2309.12237/) (2023)
- [Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs](https://mlsanity.com/paper/2506.14540/) (2025)
- [Selecting a classification performance measure: matching the measure to the problem](https://mlsanity.com/paper/2409.12391/) (2024)
- [Absolute Evaluation Measures for Machine Learning: A Survey](https://mlsanity.com/paper/2507.03392/) (2025)
- [A Convex Loss Function for Set Prediction with Optimal Trade-offs Between Size and Conditional Coverage](https://mlsanity.com/paper/2512.19142/) (2025)
- [Instruction Alignment for Binary Code Representation Learning](https://mlsanity.com/paper/2608.11766/) (2026)

## Abstract (arXiv)

The H-measure is a classifier performance measure which takes into account the context of application without requiring a rigid value of relative misclassification costs to be set. Since its introduction in 2009 it has become widely adopted. This paper answers various queries which users have raised since its introduction, including questions about its interpretation, the choice of a weighting function, whether it is strictly proper, and its coherence, and relates the measure to other work.