Fig. 8
From: A novel cost-sensitive framework for customer churn predictive modeling
Comparison of the random forest, logistic regression, Bayes minimum risk and cost-sensitive logistic regression algorithms. Overall, the CSDT is the method that produces significant higher savings. Moreover, it is observed that the cost-sensitive methods CSLR and CSDT gave the best results when estimated using the full training dataset, thats because these models does not appear to be impacted by the class imbalance, and themselves deal with the cost-sensitivity of each customer