A bias correction for the minimum error rate in cross-validation
Applications
2009-08-21 v1
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.
Cite
@article{arxiv.0908.2904,
title = {A bias correction for the minimum error rate in cross-validation},
author = {Ryan J. Tibshirani and Robert Tibshirani},
journal= {arXiv preprint arXiv:0908.2904},
year = {2009}
}
Comments
Published in at http://dx.doi.org/10.1214/08-AOAS224 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)