Databricks Machine Learning Associate Question 199
Select 3You are using Databricks to train a machine learning model and want to tune hyperparameters efficiently. You decide to use random search for hyperparameter tuning. Which of the following correctly describes how random search operates and its key benefits?
- A
Random search samples hyperparameters from a predefined distribution, rather than testing all possible combinations.
- B
Random search guarantees finding the optimal hyperparameter configuration within a fixed number of trials.
- C
Random search is more efficient than grid search when only a few hyperparameters significantly impact model performance.
- D
Random search cannot be used with Databricks MLflow for hyperparameter tuning.
- E
Random search is particularly useful when you have a limited computational budget.
Show answer and explanation
Correct answers: A, C, E
Explanation
Random search is a hyperparameter tuning technique that samples configurations from a predefined distribution, which is more efficient than grid search in many scenarios. It is especially useful when computational resources are limited or when only a few hyperparameters significantly impact model performance. However, it does not guarantee finding the optimal configuration and is supported in Databricks MLflow.
- A. Correct.
Correct: Random search samples hyperparameters from a predefined distribution, making it more flexible and efficient compared to grid search, which tests all possible combinations.
- B. Incorrect.
Incorrect: Random search does not guarantee finding the optimal hyperparameter configuration, as it relies on random sampling. It increases the likelihood of finding good configurations, but optimality is not assured.
- C. Correct.
Correct: Random search is efficient when only a subset of hyperparameters significantly impacts performance, as it explores a broader range of configurations without testing all combinations.
- D. Incorrect.
Incorrect: Databricks MLflow supports random search for hyperparameter tuning, making it a valid approach within the Databricks ecosystem.
- E. Correct.
Correct: Random search is particularly useful when computational resources are limited, as it can explore a diverse set of configurations within a fixed number of trials.