Model Training¶

Now with the best model identified, you will re-train one more time to obtain the fully trained model and output model explainability metrics.

Again, this step uses SageMaker configurations from pipeline.yaml to run SageMaker’s XGBoost container image. This time around, training is kicking off with optimized hyperparameters and SageMaker Debugger settings. Running the training job with Debugger, allows for explainability metrics to be output to S3 in addition to a fully trained model.

Explainability metrics show how each feature affects customer churn. Incorporating techniques like SHAP, enables the ability to explain the model as a whole and, more importantly, the ability to look at how scores are determined on an individual customer basis.