Estimating generalization error using out-of-bag estimates
Tom Bylander, Dennis Hanzlik · National Conference on Artificial Intelligence · 1999
We provide a method for estimating the generalization error of a bag using out-of-bag estimates. In bagging, each predictor (single hypothesis) is learned from a bootstrap sample of the training examples; the output of a bag (a set of predictors) on an example is determined by voting. The out-of-bag estimate is based on recording the votes of each predictor on those training examples omitted from its bootstrap sample. Because no additional predictors are generated, the out-of-bag estimate requires considerably less time than 10- fold cross-validation. We address the question of how to use the out-of-bag estimate to estimate generalization error. Our experiments on several datasets show that the out-of-bag estimate and 10-fold cross-validation have very inaccurate (much too optimistic) confidence levels. We can improve the out-of-bag estimate by incorporating a correction.