# Precision and Recall Reject Curves for Classification

arXiv: https://arxiv.org/abs/2308.08381 · HTML: https://mlsanity.com/paper/2308.08381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 16 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Machine Learning with a Reject Option: A survey](https://mlsanity.com/paper/2107.11277/) (2021)
- [Optimal strategies for reject option classifiers](https://mlsanity.com/paper/2101.12523/) (2021)
- [Performance measures for classification systems with rejection](https://mlsanity.com/paper/1504.02763/) (2015)
- [Explaining Reject Options of Learning Vector Quantization Classifiers](https://mlsanity.com/paper/2202.07244/) (2022)
- [Model Agnostic Local Explanations of Reject](https://mlsanity.com/paper/2205.07623/) (2022)
- [Explainable Human-Robot Training and Cooperation with Augmented Reality](https://mlsanity.com/paper/2302.01039/) (2023)

## Built on by (most cited first, 1 of 1)

- [Estimation of Confidence Bounds in Binary Classification using Wilson Score Kernel Density Estimation](https://mlsanity.com/paper/2602.20947/) (2026)

## Abstract (arXiv)

For some classification scenarios, it is desirable to use only those classification instances that a trained model associates with a high certainty. To obtain such high-certainty instances, previous work has proposed accuracy-reject curves. Reject curves allow to evaluate and compare the performance of different certainty measures over a range of thresholds for accepting or rejecting classifications. However, the accuracy may not be the most suited evaluation metric for all applications, and instead precision or recall may be preferable. This is the case, for example, for data with imbalanced class distributions. We therefore propose reject curves that evaluate precision and recall, the recall-reject curve and the precision-reject curve. Using prototype-based classifiers from learning vector quantization, we first validate the proposed curves on artificial benchmark data against the accuracy reject curve as a baseline. We then show on imbalanced benchmarks and medical, real-world data that for these scenarios, the proposed precision- and recall-curves yield more accurate insights into classifier performance than accuracy reject curves.