MLA-C01 Question 153
Select 3You are training a machine learning model for a customer churn prediction use case. The model is underperforming on the validation set, and its predictions indicate a high bias. You are using Amazon SageMaker's built-in XGBoost algorithm. Which of the following actions can help improve the model's performance?
- A
Increase the maximum depth of the trees in the XGBoost model.
- B
Reduce the learning rate of the XGBoost model.
- C
Increase the size of the training dataset by augmenting it with synthetic samples.
- D
Enable early stopping during training to avoid overfitting.
- E
Increase the number of estimators (trees) in the XGBoost model.
Show answer and explanation
Correct answers: A, B, C
Explanation
The high bias in the model indicates it is underfitting and failing to capture the underlying patterns in the data. Actions like increasing tree depth, reducing the learning rate, and expanding the training dataset can help address this issue by making the model more expressive and improving generalization. Early stopping and increasing the number of trees are useful techniques, but they address overfitting and variance rather than bias.
- A. Correct.
Increasing the maximum depth of the trees can help the model capture more complex patterns in the data, which can reduce bias. However, this should be done carefully to avoid overfitting.
- B. Correct.
Reducing the learning rate allows the model to make smaller updates during training, which can improve model accuracy over time and reduce bias.
- C. Correct.
Increasing the size of the training dataset, either by collecting more data or generating synthetic samples, can help the model generalize better and improve performance.
- D. Incorrect.
Enabling early stopping helps prevent overfitting but does not directly address the issue of high bias in the model.
- E. Incorrect.
Increasing the number of trees might improve performance in some cases, but it primarily helps reduce variance rather than bias.