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.
If you use this workflow, please cite:
F. Villaroel Ordenes & R. Silipo, “Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications”, Journal of Business Research 137(1):393-410, DOI: 10.1016/j.jbusres.2021.08.036.
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.
If you use this workflow, please cite:
F. Villaroel Ordenes & R. Silipo, “Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications”, Journal of Business Research 137(1):393-410, DOI: 10.1016/j.jbusres.2021.08.036.