In this chapter, we have learned about different classical classification algorithms, such as LR, SVM, and NB. Using these algorithms, we predicted whether a customer is likely to cancel their telecommunications subscription or not. We've also discussed what types of data are required to build a successful churn predictive model.
Tree-based and tree ensemble classifiers are really useful and robust, and are widely used for solving both classification and regression tasks. In the next chapter, we will look into developing such classifiers and regressors using tree-based and ensemble techniques such as DT, RF, and GBT, for both classification and regression.