Irredundant k-Fold Cross-Validation

• URL: http://arxiv.org/abs/2507.20048v1
• Date: Sat, 26 Jul 2025 19:59:37 GMT
• Title: Irredundant k-Fold Cross-Validation
• Authors: Jesus S. Aguilar-Ruiz,
• Abstract summary: In traditional k-fold cross-validation, each instance is used ($k!-!1$) times for training and once for testing, leading to redundancy.<n>We introduce Irredundant $k$--fold cross-validation, a novel method that guarantees each instance is used exactly once for training and once for testing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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