English(EN) The Relative Instability of Model Comparison with Cross-validation

新论文质疑交叉验证在模型比较中的稳定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-05 04:00

一篇新发表在arXiv上的论文表明，交叉验证（一种常用的机器学习模型比较统计技术）可能产生不稳定且无效的推断。研究特别指出，尽管Lasso和软阈值方法本身是稳定的，但它们可能导致不可靠的比较。这种不稳定性引发了对在未事先验证相对稳定性情况下的常规交叉验证模型比较用法的质疑。 AI

影响强调了标准模型评估技术潜在的缺陷，敦促在解释比较结果时要谨慎。

排序理由该集群包含一篇学术论文，详细介绍了机器学习统计方法的新研究发现。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Alexandre Bayle, Lucas Janson, Lester Mackey · 2026-06-05 04:00

交叉验证下模型比较的相对不稳定性

arXiv:2508.04409v3 Announce Type: replace Abstract: Cross-validation (CV) is known to provide asymptotically exact tests and confidence intervals for model improvement but only when the model comparison is relatively stable. Surprisingly, we prove that even simple, individually s…