OUT-OF-BAG ESTIMATION 论文

摘要

In bagging, predictors are constructed using bootstrap samples from the training set and then aggregated to form a bagged predictor. Each bootstrap sample leaves out about 37 % of the examples. These left-out examples can be used to form accurate estimates of important quantities. For instance, they can be used to give much improved estimates of node probabilities and node error rates in decision trees. Using estimated outputs instead of the observed outputs improves accuracy in regression trees. They can also be used to give nearly optimal estimates of generalization errors for bagged predictors. * Partially supported by NSF Grant 1-444063-21445 Introduction: We assume that there is a training set T = {(yn,xn), n=1,...,N} and a method for constructing a predictor Q(x,T) using the given training set. The output variable y can either be