# The Effect of Class Imbalance on Precision-Recall Curves

arXiv: https://arxiv.org/abs/2007.01905 · HTML: https://mlsanity.com/paper/2007.01905/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 5 of 5)

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- [Imbalanced Classification in Medical Imaging via Regrouping](https://mlsanity.com/paper/2210.12234/) (2022)
- [Exact Reformulation and Optimization for Direct Metric Optimization in Binary Imbalanced Classification](https://mlsanity.com/paper/2507.15240/) (2025)
- [Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness](https://mlsanity.com/paper/2507.09687/) (2025)
- [Federated Computation of ROC and PR Curves](https://mlsanity.com/paper/2510.04979/) (2025)

## Abstract (arXiv)

In this note I study how the precision of a classifier depends on the ratio r of positive to negative cases in the test set, as well as the classifier's true and false positive rates. This relationship allows prediction of how the precision-recall curve will change with r, which seems not to be well known. It also allows prediction of how Fβ and the Precision Gain and Recall Gain measures of Flach and Kull (2015) vary with r.