Question

A data scientist trains an XGBoost model in a SageMaker training job to predict customer churn. After training, the model achieves 98% accuracy on the training set but only 71% accuracy on the validation set. The validation loss begins increasing after several boosting rounds while training loss keeps decreasing. Which action is MOST likely to improve the model's generalization?

Accepted Answer

Apply regularization by increasing the lambda and alpha hyperparameters and reduce max_depth, while using early stopping on the validation metric.

Answer

Increase the max_depth hyperparameter and add more boosting rounds to help the model learn the training data more completely.

Answer

Switch the objective function from binary:logistic to reg:squarederror to better fit the target distribution.

Answer

Collect additional features and increase the number of columns used per split with a higher colsample_bytree value.