{"ID":2890245,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.20048","arxiv_id":"2507.20048","title":"Irredundant $k$-Fold Cross-Validation","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2507.20048","url_pdf":"https://arxiv.org/pdf/2507.20048v2","authors":"[\"Jesus S. Aguilar-Ruiz\"]","published":"2025-07-26T19:59:37Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}