Generalization error for decision problems

Abstract

In this entry we review the generalization error for classification and single-stage decision problems. We distinguish three alternative definitions of the generalization error which have, at times, been conflated in the statistics literature and show that these definitions need not be equivalent even asymptotically. Because the generalization error is a non-smooth functional of the underlying generative model, standard asymptotic approximations, e.g., the bootstrap or normal approximations, cannot guarantee correct frequentist operating characteristics without modification. We provide simple data-adaptive procedures that can be used to construct asymptotically valid confidence sets for the generalization error. We conclude the entry with a discussion of extensions and related problems.