Hyperparameter Tuning¶

The Hyperparameter tuning step finds an optimal combination of hyperparmaters for your model. In this example, you use XGBoost to model churn as a binary outcome (will churn / will not churn). Specifically, you are going to try to achieve the highest accuracy possible through maximizing the Area Under the Curve, finding the best regularization terms, depth and tree splitting combinations in repeated parallel runs (defaulted at 2 total). This produces the most accurate model given the available data.

It’s worth noting that there is no script here. All configurations are passed as JSON directly to SageMaker in pipeline.yaml, using SageMaker’s XGBoost container. As before, defaults are hardcoded. However, like all parts of the pipeline, these are updatable as needed. For a deeper look on HyperParameter Tuning with Amazon SageMaker and the the type of inputs possible see here.

After this, a Lambda function is called to record the best performing model (and hyperparameter configurations) and then passes it on to the next step in the Step Functions workflow.