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arXiv · 0908.2904

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

Ryan J. Tibshirani·Robert Tibshirani
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Abstract

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. 2009-08-20. A bias correction for the minimum error rate in cross-validation. https://doi.org/10.1214/08-aoas224

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