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Abstract
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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
classifers in churn prediction context. Furthermore logistic regression (LR) and additive logistic regression
(ALR)areused.Afterparametersetting,eightdistinctivealgorithms,namelyC4.5,C4.5CP,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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