The error rate of decision-tree and other classification learners can often be much reduced by bagging: learning multiple models from bootstrap samples of the database, and combining them by uniform voting. In this paper we empirically test two alternative explanations for this, both based on Bayesian learning theory: (1) bagging works because it is an approximation to the optimal procedure of Bayesian model averaging, with an appropriate implicit prior; (2) bagging works because it effectively shifts the prior to a more appropriate region of model space. All the experimental evidence contradicts the first hypothesis, and confirms the second.
['Pedro M. Domingos']
Why does bagging work? a Bayesian account and its implications