Open Access
June 2009 A bias correction for the minimum error rate in cross-validation
Ryan J. Tibshirani, Robert Tibshirani
Ann. Appl. Stat. 3(2): 822-829 (June 2009). DOI: 10.1214/08-AOAS224


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.


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Ryan J. Tibshirani. Robert Tibshirani. "A bias correction for the minimum error rate in cross-validation." Ann. Appl. Stat. 3 (2) 822 - 829, June 2009.


Published: June 2009
First available in Project Euclid: 22 June 2009

zbMATH: 1166.62311
MathSciNet: MR2750683
Digital Object Identifier: 10.1214/08-AOAS224

Keywords: cross-validation , optimism estimation , prediction error estimation

Rights: Copyright © 2009 Institute of Mathematical Statistics

Vol.3 • No. 2 • June 2009
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