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 than form-for example, according to generalized degrees of freedom (GDF)-the razor's role is restored. On a two-dimensional decision tree problem, bagging several trees is shown to actually have less GDF complexity than a single component tree, removing the generalization paradox of ensembles.
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