arXiv:2605.25859v1 Announce Type: cross Abstract: We study the mean-squared error of $k$-fold cross-validation as a risk estimator, with particular emphasis on how its accuracy depends on the number of folds $k$. Despite the widespread use of cross-validation, principled guidance for choosing $k$ is largely absent, mainly due to the complex dependence between fold-wise error estimates. To obtain sharp and interpretable results, we focus on the majority algorithm in binary classification, a minimal yet nontrivial empirical risk minimization procedure. We provide a fine-grained analysis of its c
The paper represents ongoing academic efforts to refine fundamental AI/ML techniques, crucial for improving the reliability and efficiency of increasingly complex models.
Improved theoretical understanding of cross-validation directly impacts the robustness, predictability, and interpretability of AI systems, addressing a key challenge in AI development.
Guidance for choosing the 'k' in k-fold cross-validation becomes more principled, leading to more accurate and less variable risk estimates for models, especially in critical applications.
- · AI researchers
- · ML practitioners
- · academic institutions
- · software developers
- · developers of unreliable AI
- · inefficient model validation pipelines
More accurate model validation leads to more trustworthy AI systems.
Increased trust accelerates AI adoption in regulated industries and sensitive applications.
The development of AI agents and automated decision-making systems gains a stronger foundational integrity, reducing unexpected failures.
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