新的交叉拟合方法改进了机器学习模型的统计推断

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

一种名为交叉拟合的新统计方法已被开发出来，以解决模型推断中的非正则性问题。该方法仍然遵循中心极限定理，但需要调整其渐近方差以考虑交叉折叠相关性。所提出的置信区间旨在估计这种相关性，目标是实现渐近名义覆盖率，并在随机森林和神经网络的模拟中显示出有希望的结果。 AI

影响增强了机器学习模型的统计严谨性，有可能提高随机森林和神经网络等应用的推断可靠性。

排序理由该集群包含一篇详细介绍机器学习推断新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Bruno Fava · 2026-10-05 04:00

非规则性下的交叉拟合：通过局部性实现正态性和推断

arXiv:2610.02944v1 Announce Type: cross Abstract: Cross-fitting is routine in much of applied research. While conventional confidence intervals that ignore cross-fold dependence are asymptotically valid in several settings, they undercover in many applications that share a common…