English(EN) A Theory of Cross-Validation Variance

新理论解释交叉验证方差，K折交叉验证更优

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

一篇新发表在arXiv上的论文引入了一个理论框架，用于理解预测性能交叉验证估计中的方差。该研究为该方差建立了精确的有限样本恒等式，表明它分解为抽样方差和由于随机折叠划分引起的方差。研究结果表明，K折交叉验证在一阶上是方差最优的，而蒙特卡洛和自助法则不是。论文还建议，对于均方误差和AUC等特定指标，留一法或留二法等特定方案能提供最小的方差。 AI

影响为选择最优交叉验证设计提供了理论基础，可能提高模型评估的稳健性。

排序理由关于机器学习统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Nathaniel S. O'Connell, Jaime L. Speiser · 2026-11-11 05:00

交叉验证方差理论

arXiv:2611.14463v1 Announce Type: cross Abstract: Cross-validation estimates predictive performance by repeatedly splitting the data into training and validation sets, fitting the model on the first and evaluating it on the second. Every observation influences the estimate in two…

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交叉验证方差理论

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