Cross-validation as the objective function for variable-selection techniques
Cross-validation as the objective function for variable-selection techniques
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DOI：
10.1016/s0165-9936(03)00607-1
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发表时间：
2003-06-01
影响因子：
13.1
通讯作者：
Baumann, K
中科院分区：
文献类型：
--
作者：
Baumann, K
研究了不同的交叉验证方法对指导变量选择算法产生高预测模型的适用性。结果表明，常用的留一交叉验证有很强的过拟合倾向，低估了真实的预测误差，在没有进一步约束或进一步验证的情况下不应使用。提出了留一交叉验证和其他验证方法的替代方法。(C) 2003年Elsevier Science B.V.出版
Different methods of cross-validation are studied for their suitability to guide variable-selection algorithms to yield highly predictive models. It is shown that the commonly applied leave-one-out cross-validation has a strong tendency to overfitting, underestimates the true prediction error, and should not be used without further constraints or further validation. Alternatives to leave-one-out cross-validation and other validation methods are presented. (C) 2003 Published by Elsevier Science B.V.