Abstract

We review accuracy estimation methods and compare the two most common methods: crossvalidation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensiveleaveone -out cross-validation. We report on a largescale experiment---over half a million runs of C4.5 and a Naive-Bayes algorithm---to estimate the effects of different parameters on these algorithms on real-world datasets. For crossvalidation, wevary the number of folds and whether the folds are stratified or not# for bootstrap, wevary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold stratified cross validation, even if computation power allows using more folds.