Explaining Bagging with Monte Carlo Theory

In this paper we propose the use of the framework of Monte Carlo stochastic algorithms to analyze ensemble learning, specifically, bagging. In particular, this framework allows one to explain baggingrsquos behavior and also why increasing the margin improves performances. Experimental results support the theoretical analysis.