Databricks Machine Learning Associate Question 542
Select 3You are developing a machine learning model in Databricks to predict customer churn for a subscription service. During model training, you notice that the model's performance on the training data is significantly better than its performance on the validation data. Which of the following actions could help address this issue?
- A
Increase the regularization parameter of the model.
- B
Add more features to the dataset to improve model complexity.
- C
Apply early stopping during model training.
- D
Reduce the size of the training set to balance performance.
- E
Perform hyperparameter tuning to optimize the model's settings.
Show answer and explanation
Correct answers: A, C, E
Explanation
The scenario indicates that the model is overfitting, as evidenced by its poor validation performance compared to training. Increasing regularization, applying early stopping, and performing hyperparameter tuning are effective strategies to address overfitting by either simplifying the model or optimizing its parameters. Adding unnecessary features or reducing the training set size would likely worsen the issue.
- A. Correct.
Increasing the regularization parameter helps reduce overfitting by penalizing overly complex models, which can improve performance on the validation set.
- B. Incorrect.
Adding more features increases model complexity and can exacerbate overfitting, especially if the additional features are not highly relevant.
- C. Correct.
Early stopping prevents the model from overfitting by halting training when the validation performance starts to degrade.
- D. Incorrect.
Reducing the size of the training set is generally not recommended as it reduces the amount of data available for learning, which can harm model performance.
- E. Correct.
Hyperparameter tuning allows you to optimize settings like learning rate, regularization strength, or tree depth, which can improve the model's generalizability and reduce overfitting.