# Irredundant k-Fold Cross-Validation

arXiv: https://arxiv.org/abs/2507.20048 · HTML: https://mlsanity.com/paper/2507.20048/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 26 Jul 2025.

## Builds on (most cited first, 2 of 2)

- [On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima](https://mlsanity.com/paper/1609.04836/) (2016)
- [Cross-validation: what does it estimate and how well does it do it?](https://mlsanity.com/paper/2104.00673/) (2021)

## Abstract (arXiv)

In traditional k-fold cross-validation, each instance is used (k-1) times for training and once for testing, leading to redundancy that lets many instances disproportionately influence the learning phase. We introduce Irredundant k-fold cross-validation, a novel method that guarantees each instance is used exactly once for training and once for testing across the entire validation procedure. This approach ensures a more balanced utilization of the dataset, mitigates overfitting due to instance repetition, and enables sharper distinctions in comparative model analysis. The method preserves stratification and remains model-agnostic, i.e., compatible with any classifier. Experimental results demonstrate that it delivers consistent performance estimates across diverse datasets -- comparable to k-fold cross-validation -- while providing less optimistic variance estimates because training partitions are non-overlapping, and significantly reducing the overall computational cost.