A data scientist observes that a gradient boosted model in SAS Viya has very low training error but high validation error, while a linear model has moderate error on both. The team wants to improve the gradient boosting model without collecting more data. Which technique is specifically designed to address this situation?

B is correct because early stopping monitors validation error and halts training at the iteration where validation error is minimized, preventing additional boosting rounds from overfitting. This is a form of regularization specific to iterative ensemble methods. A worsens overfitting by adding more rounds. C increases model complexity further, compounding the problem. D explicitly removes safeguards against overfitting.