ml-specialty video for a data science team at a fintech company has developed an XGBoost model to predict credit risk based on historical loan repayment data.
A data science team at a fintech company has developed an XGBoost model to predict credit risk based on historical loan repayment data. While the model demonstrates high accuracy when tested against the training dataset, its performance significantly drops when evaluated using a separate validation dataset, indicating a potential overfitting issue. To address this problem and enhance the model's generalization to new, unseen data, which hyperparameter adjustment should the team consider as the most effective solution? (Choose TWO)