Question

A data scientist is training an XGBoost model in SageMaker to predict loan defaults. They set num_round to 2000 but notice that the validation AUC peaks around round 350 and then slowly degrades while training AUC keeps improving. They want to automatically stop training when the model stops improving on the validation set and keep the best-performing model, without manually rerunning experiments to find the ideal number of rounds. Which approach best achieves this?

Accepted Answer

Set the early_stopping_rounds hyperparameter and provide a validation channel, so training halts if the evaluation metric does not improve for the specified number of consecutive rounds.

Answer

Reduce num_round to 350 based on the observed peak and retrain the model with only a training channel.

Answer

Enable SageMaker automatic model tuning with num_round as the only tunable hyperparameter across 2000 discrete values.

Answer

Increase the learning rate (eta) so the model converges before round 350 and stops overfitting.