Building a Churn Predictor

Training a Churn Predictor

This workflow is an example of how to train a basic machine learning model for a churn prediction task. In this case we train a random forest after oversampling the minority class with the SMOTE algorithm.

Note that the Learner-Predictor construct is common to all supervised algorithms. Here we also use a cross-validation procedure for a more reliable estimation of the random forest performance.

External resources

• Churn Prediction