# The MCC-F1 curve: a performance evaluation technique for binary classification

arXiv: https://arxiv.org/abs/2006.11278 · HTML: https://mlsanity.com/paper/2006.11278/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 6 of 6)

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- [Exploring Genetic-histologic Relationships in Breast Cancer](https://mlsanity.com/paper/2103.08082/) (2021)
- [Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation](https://mlsanity.com/paper/2412.17066/) (2024)
- [MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation](https://mlsanity.com/paper/2507.10591/) (2025)

## Abstract (arXiv)

Many fields use the ROC curve and the PR curve as standard evaluations of binary classification methods. Analysis of ROC and PR, however, often gives misleading and inflated performance evaluations, especially with an imbalanced ground truth. Here, we demonstrate the problems with ROC and PR analysis through simulations, and propose the MCC-F1 curve to address these drawbacks. The MCC-F1 curve combines two informative single-threshold metrics, MCC and the F1 score. The MCC-F1 curve more clearly differentiates good and bad classifiers, even with imbalanced ground truths. We also introduce the MCC-F1 metric, which provides a single value that integrates many aspects of classifier performance across the whole range of classification thresholds. Finally, we provide an R package that plots MCC-F1 curves and calculates related metrics.