A BIAS CORRECTION FOR THE MINIMUM ERROR RATE IN CROSS-VALIDATION

被引：69

作者：

Tibshirani, Ryan J. ^{[1
]}

Tibshirani, Robert ^{[1
]}

机构：

[1] Stanford Univ, Dept Stat, Stanford, CA 94305 USA

来源：

ANNALS OF APPLIED STATISTICS | 2009年 / 3卷 / 02期

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

Cross-validation; prediction error estimation; optimism estimation;

D O I：

10.1214/08-AOAS224

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Tuning parameters in supervised learning problems are often estimated by cross-validation. The minimum value of the cross-validation error can be biased downward as an estimate of the test error at that same value of the tuning parameter. We propose a simple method for the estimation of this bias that uses information from the cross-validation process. As a result, it requires essentially no additional computation. We apply our bias estimate to a number of popular classifiers in various settings, and examine its performance.

引用

页码：822 / 829

页数：8

共 7 条

[1]

[Anonymous], 1994, An introduction to the bootstrap: CRC press

[2]

Breiman L., 1984, BIOMETRICS, V40, P874, DOI 10.1201/9781315139470

[3] 1977 RIETZ LECTURE - BOOTSTRAP METHODS - ANOTHER LOOK AT THE JACKKNIFE [J].