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Logit models are popular tools for analyzing discrete choice and ranking data.The models assume that judges rate each item with a measurable utility,and the ordering of a judges utilities determines the outcome.Logit models have been proven to be powerful tools,but they become difficult to interpret if the models contain nonlinear and interaction terms.We extended the logit models by adding a decision tree structure to overcome this difficulty.