 Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrapJi-Hyun Kim Computational Statistics & Data Analysis, 2009, vol. 53, issue 11, 3735-3745 Abstract: We consider the accuracy estimation of a classifier constructed on a given training sample. The naive resubstitution estimate is known to have a downward bias problem. The traditional approach to tackling this bias problem is cross-validation. The bootstrap is another way to bring down the high variability of cross-validation. But a direct comparison of the two estimators, cross-validation and bootstrap, is not fair because the latter estimator requires much heavier computation. We performed an empirical study to compare the .632+ bootstrap estimator with the repeated 10-fold cross-validation and the repeated one-third holdout estimator. All the estimators were set to require about the same amount of computation. In the simulation study, the repeated 10-fold cross-validation estimator was found to have better performance than the .632+ bootstrap estimator when the classifier is highly adaptive to the training sample. We have also found that the .632+ bootstrap estimator suffers from a bias problem for large samples as well as for small samples. Date: 2009 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:11:p:3735-3745 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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