Databricks Machine Learning Associate Question 248
Select 3You are building a machine learning pipeline in Databricks to predict customer churn. To optimize your model's hyperparameters, you decide to use grid search with cross-validation. Which of the following statements correctly describes how grid search with cross-validation works and its implications?
- A
Grid search evaluates all combinations of specified hyperparameter values to identify the best-performing set.
- B
Cross-validation ensures that the model's performance is validated across multiple folds of the data, reducing the risk of overfitting.
- C
Grid search automatically selects the hyperparameters that minimize overfitting without requiring additional validation.
- D
Cross-validation splits the training data into a single training and testing set to validate the model's performance.
- E
Grid search can be computationally expensive, especially with a large number of hyperparameter combinations.
Show answer and explanation
Correct answers: A, B, E
Explanation
Grid search with cross-validation optimizes hyperparameters by systematically evaluating all combinations of specified values while ensuring robust performance validation via cross-validation. However, it is computationally intensive due to the exhaustive search process. Understanding these concepts is essential for applying grid search and cross-validation effectively in Databricks and other machine learning workflows.
- A. Correct.
Correct. Grid search systematically evaluates all combinations of hyperparameter values to find the optimal set based on performance metrics.
- B. Correct.
Correct. Cross-validation divides the data into multiple folds, training on some and validating on others, reducing the risk of overfitting by testing the model's generalization ability.
- C. Incorrect.
Incorrect. Grid search does not inherently minimize overfitting; it requires cross-validation or other validation methods to evaluate the hyperparameters' performance.
- D. Incorrect.
Incorrect. Cross-validation involves splitting the training data into multiple folds, not just a single training and testing set. This ensures a more robust evaluation of the model.
- E. Correct.
Correct. Grid search can be computationally expensive, especially when the hyperparameter space is large, as it evaluates all combinations systematically.