A bias correction for the minimum error rate in cross-validation
Ryan J. Tibshirani, Robert Tibshirani
The Annals of Applied Statistics · 2009 · Volume 3 · Issue 2
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.
Keywords
cross-validation, optimism estimation, prediction error estimation
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