Determination of Algorithms Making Balance Between Accuracy and Comprehensibility in Churn Prediction Setting

Abstract

Predictive modeling is a useful tool for identifying customers who are at risk of churn. An appropriate churn prediction model should be both accurate and comprehensible. However, reviewing the past researches in this context shows that much attention is paid to accuracy of churn prediction models than comprehensibility of them. This paper compares three different rule induction techniques from three categories of rule based classifiers in churn prediction context. Furthermore logistic regression (LR) and additive logistic regression (ALR) are used. After parameter setting, eight distinctive algorithms, namely C4.5, C4.5 CP, RIPPER, RIPPER CP, PART, PART CP, LR, and ALR, are obtained. These algorithms are applied on an original training set with the churn rate of 30% and another training set with the churn rate of 50%. Only the models built by applying these algorithms on a training set with the churn rate of 30% make balance between accuracy and comprehensibility. In addition, the results of this paper show that ALR can be an excellent alternative for LR, when models only from accuracy perspective are evaluated.

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