Databricks Machine Learning Associate Question 244
Select 3You are training a machine learning model in Databricks and want to optimize hyperparameters for a Random Forest model. You decide to use grid search with cross-validation to find the best combination of hyperparameters. Which of the following statements correctly describe this process?
- A
Grid search iterates over a predefined set of hyperparameter values to evaluate model performance.
- B
Cross-validation ensures that the model is evaluated on different subsets of the data to reduce overfitting.
- C
Grid search is computationally faster than random search because it evaluates fewer combinations of hyperparameters.
- D
Cross-validation divides the data into training and validation sets multiple times to measure model generalization performance.
- E
Grid search automatically selects the best hyperparameter combination without requiring validation.
Show answer and explanation
Correct answers: A, B, D
Explanation
Grid search and cross-validation are commonly used together to optimize machine learning models. Grid search systematically explores a grid of predefined hyperparameter values, while cross-validation ensures robust evaluation by splitting the data into training and validation sets multiple times. Together, they help identify the best hyperparameter combination that leads to a model with good generalization performance. However, grid search is computationally expensive, and it does not inherently select the best hyperparameters without a user-defined performance metric.
- A. Correct.
This is correct. Grid search systematically evaluates all combinations of the predefined hyperparameter grid.
- B. Correct.
This is correct. Cross-validation splits the data into multiple folds, ensuring the model is tested on various subsets to reduce overfitting and improve generalization.
- C. Incorrect.
This is incorrect. Grid search is computationally expensive because it evaluates all possible combinations of hyperparameters, unlike random search, which samples a subset of combinations.
- D. Correct.
This is correct. Cross-validation involves repeatedly splitting the data into training and validation sets to provide a robust estimate of the model's generalization performance.
- E. Incorrect.
This is incorrect. Grid search does not inherently select the best hyperparameter combination; it evaluates performance for each combination, and the user typically specifies a performance metric to identify the best result.