The Annals of Applied Statistics

A bias correction for the minimum error rate in cross-validation

Ryan J. Tibshirani and Robert Tibshirani

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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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Ann. Appl. Stat., Volume 3, Number 2 (2009), 822-829.

First available in Project Euclid: 22 June 2009

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Cross-validation prediction error estimation optimism estimation


Tibshirani, Ryan J.; Tibshirani, Robert. A bias correction for the minimum error rate in cross-validation. Ann. Appl. Stat. 3 (2009), no. 2, 822--829. doi:10.1214/08-AOAS224.

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