On Explaining Degree of Error Reduction due to CombiningMultiple Decision

In multiple models research, classiications or evidence is combined from several classiication models in order to form overall classiications with higher accuracy than any of the individual models. However, most previous work has concentrated on demonstrating that use of multiple models leads to statistically signiicant error reduction as compared to the error rate of the single model learned on the same data. In this paper we present a model of the degree of error reduction due to the use of multiple decision trees. In particular, we show that the degree of error reduction can be linearly mod-eled by the \degree to which decision trees make uncorrelated errors." These conclusions are based on results from 21 domains. under the supervision of Michael Pazzani. We acknowledge the support of NSF grant IRI 93-10413.