# Enumerating the k-fold configurations in multi-class classification problems

arXiv: https://arxiv.org/abs/2401.13843 · HTML: https://mlsanity.com/paper/2401.13843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [A new baseline for retinal vessel segmentation: Numerical identification and correction of methodological inconsistencies affecting 100+ papers](https://mlsanity.com/paper/2111.03853/) (2021)
- [Deep Learning based HEp-2 Image Classification: A Comprehensive Review](https://mlsanity.com/paper/1911.08916/) (2019)
- [Testing the Consistency of Performance Scores Reported for Binary Classification Problems](https://mlsanity.com/paper/2310.12527/) (2023)
- [mlscorecheck: Testing the consistency of reported performance scores and experiments in machine learning](https://mlsanity.com/paper/2311.07541/) (2023)

## Abstract (arXiv)

K-fold cross-validation is a widely used tool for assessing classifier performance. The reproducibility crisis faced by artificial intelligence partly results from the irreproducibility of reported k-fold cross-validation-based performance scores. Recently, we introduced numerical techniques to test the consistency of claimed performance scores and experimental setups. In a crucial use case, the method relies on the combinatorial enumeration of all k-fold configurations, for which we proposed an algorithm in the binary classification case.