This paper is a study of two-group classification models for binary variables. Eight classification procedures for binary variables are discussed and evaluated at each of 118 configurations of the sampling experiments. The results obtained ranked the procedures as follows: Optimal, Linear discriminant, Maximum likelihood, Predictive, Dillon Goldstein, Full multinomial, Likelihood and Nearest neighbour. Also the result of the study show that increase in the number of variables improve the accuracy of the models.
Keywords: Binary Variables, Classification Models, Dillon Goldstein, Linear Discriminant, Misclassification, Optimal, Predictive, and Multinomial, maximum likelihood
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