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Updated: Jan 8, 2026

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预测模型内部验证方法的实证模拟:比较k-fold交叉验证与基于引导的乐观度纠正

Chao Zhang1, Ruohua Yan1, Xiaohang Liu1

  • 1Center for Clinical Epidemiology and Evidence-based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.

Journal of clinical epidemiology
|December 14, 2025
PubMed
概括

十倍交叉验证为统计和机器学习模型提供了强大的内部验证,优于引导式方法,特别是在复杂的模型中. 这种方法因其稳定性和易于在预测建模中实现而被推.

关键词:
基于Bootstrap的乐观度修正方法临床预测 临床预测内部验证内部验证通过K折交叉验证.机器学习模型机器学习模型过度装配 过度装配 是一个问题.

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