# Selecting a classification performance measure: matching the measure to the problem

arXiv: https://arxiv.org/abs/2409.12391 · HTML: https://mlsanity.com/paper/2409.12391/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 76 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Classifier Calibration: A survey on how to assess and improve predicted class probabilities](https://mlsanity.com/paper/2112.10327/) (2021)
- [Extending F1 metric, probabilistic approach](https://mlsanity.com/paper/2210.11997/) (2022)
- [Good Classification Measures and How to Find Them](https://mlsanity.com/paper/2201.09044/) (2022)
- [Evaluating Probabilistic Classifiers: The Triptych](https://mlsanity.com/paper/2301.10803/) (2023)
- [F*: An Interpretable Transformation of the F-measure](https://mlsanity.com/paper/2008.00103/) (2020)
- [Notes on the H-measure of classifier performance](https://mlsanity.com/paper/2106.11888/) (2021)
- [Does the evaluation stand up to evaluation? A first-principle approach to the evaluation of classifiers](https://mlsanity.com/paper/2302.12006/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Consensus Privacy Metrics Framework for Synthetic Data](https://mlsanity.com/paper/2503.04980/) (2025)
- [A weighted-likelihood framework for class imbalance in Bayesian prediction models](https://mlsanity.com/paper/2504.17013/) (2025)

## Abstract (arXiv)

The problem of identifying to which of a given set of classes objects belong is ubiquitous, occurring in many research domains and application areas, including medical diagnosis, financial decision making, online commerce, and national security. But such assignments are rarely completely perfect, and classification errors occur. This means it is necessary to compare classification methods and algorithms to decide which is ``best'' for any particular problem. However, just as there are many different classification methods, so there are many different ways of measuring their performance. It is thus vital to choose a measure of performance which matches the aims of the research or application. This paper is a contribution to the growing literature on the relative merits of different performance measures. Its particular focus is the critical importance of matching the properties of the measure to the aims for which the classification is being made.