A data scientist trains an XGBoost model in SageMaker to predict customer churn. During evaluation, the model achieves 0.97 AUC on the training set but only 0.71 AUC on the validation set. The scientist wants to reduce this gap by adjusting hyperparameters in the next tuning job. Which combination of hyperparameter changes is MOST likely to reduce the overfitting?