Abstract

Optimal classification rule and maximum likelihood rules have the largest possible posterior probability of correct allocation with respect to the prior. They have a ‘nice’ optimal property and appropriate for the development of linear classification models. In this paper we consider the problem of choosing between the two methods and set some guidelines for proper choice. The comparison between the methods is based on several measures of predictive accuracy. The performance of the methods is studied by simulations.

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