The Generalization Paradox of Ensembles

Abstract

Ensemble models—built by methods such as bagging, boosting, and Bayesian model averaging—appear dauntingly complex, yet tend to strongly outperform their component models on new data. Doesn’t this violate “Occam’s razor”—the widespread belief that “the simpler of competing alternatives is preferred”? We argue no: if complexity is measured by function rather… (More)

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