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分布偏差妥协-一个离开-一个离开-交叉验证.
George I Austin1,2, Itsik Pe'er2,3, Tal Korem2,4
1Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
Science advances
|November 28, 2025
概括
交叉验证可以引入"分布偏差",对机器学习模型评估产生负面影响. 一种新的再平衡交叉验证方法纠正了这种偏差,改善了在各种机器学习任务中的性能评估.
科学领域:
- 机器学习 机器学习
- 统计建模 统计建模
- 数据科学数据科学数据科学
背景情况:
- 交叉验证是评估机器学习模型概括的标准技术.
- 在低数据场景中,经常采用离开一次的交叉验证 (LOOCV).
- 在LOOCV折叠中汇总预测是性能指标的常见做法.
研究的目的:
- 在聚合交叉验证中识别和数学证明"分布偏差"的存在.
- 为了证明分布偏差对模型评估和超参数调整的负面影响.
- 开发和验证一种新型的交叉验证方法,对分布偏差具有稳定性.
主要方法:
- 理论证明,确定训练折叠平均值和测试实例标签之间的负相关性.
- 在各种机器学习任务,模型和评估指标中进行实证验证.
- 开发和模拟重新平衡的交叉验证技术,以缓解偏差.
主要成果:
- 分布偏差被证明是聚合的LOOCV固有的工件,对性能产生负面影响.
- 这种偏见在各种机器学习应用中被观察到,并且可以不公平地惩罚强有力的规范化.
- 拟议的再平衡交叉验证方法在模拟和基准中表现出更好的准确性和稳定性.
结论:
- 聚合的"离开一个"交叉验证引入了系统的分布偏差,损害了评估可靠性.
- 一个新的再平衡的交叉验证策略有效地减轻了分类和回归中的这种偏差.
- 这种方法为机器学习模型评估提供了更准确,更可靠的方法,特别是在数据稀缺的环境中.
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