Stability Regularized Cross-Validation

• URL: http://arxiv.org/abs/2505.06927v1
• Date: Sun, 11 May 2025 10:06:59 GMT
• Title: Stability Regularized Cross-Validation
• Authors: Ryan Cory-Wright, Andrés Gómez,
• Abstract summary: We revisit the problem of ensuring strong test-set performance via cross-validation.<n>Motivated by the generalization theory literature, we propose a nested k-fold cross-validation scheme.<n>We benchmark our procedure on a suite of 13 real-world UCI datasets.
• Score: 5.156484100374059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We revisit the problem of ensuring strong test-set performance via cross-validation. Motivated by the generalization theory literature, we propose a nested k-fold cross-validation scheme that selects hyperparameters by minimizing a weighted sum of the usual cross-validation metric and an empirical model-stability measure. The weight on the stability term is itself chosen via a nested cross-validation procedure. This reduces the risk of strong validation set performance and poor test set performance due to instability. We benchmark our procedure on a suite of 13 real-world UCI datasets, and find that, compared to k-fold cross-validation over the same hyperparameters, it improves the out-of-sample MSE for sparse ridge regression and CART by 4% on average, but has no impact on XGBoost. This suggests that for interpretable and unstable models, such as sparse regression and CART, our approach is a viable and computationally affordable method for improving test-set performance.

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