Databricks Machine Learning Associate Question 202
Select 3You are training a machine learning model using Databricks ML and want to tune its hyperparameters. Instead of exhaustively searching all possible combinations, you decide to perform random search on the hyperparameter space. Which of the following steps are necessary to correctly implement random search in Databricks ML?
- A
Define the hyperparameter space using a dictionary where each key is a hyperparameter and the value is a distribution or set of possible values.
- B
Use a grid search algorithm to systematically evaluate all possible combinations of hyperparameter values.
- C
Specify the number of random samples to draw from the hyperparameter space.
- D
Use Databricks' built-in support for random search by configuring an MLflow experiment and running a hyperparameter tuning job.
- E
Analyze the results of random search using MLflow to identify the best hyperparameter combination.
Show answer and explanation
Correct answers: A, C, E
Explanation
Random search is a hyperparameter tuning method that evaluates a specified number of random combinations from the hyperparameter space instead of testing all possibilities (as grid search does). In Databricks, you need to define the hyperparameter space, specify the number of random samples, and analyze the results using MLflow to identify the optimal configuration. While Databricks supports MLflow integration for experiment tracking, it does not have a dedicated built-in random search API, so you must implement the random sampling logic yourself.
- A. Correct.
Correct. Defining the hyperparameter space is the first step in implementing random search. The space must include the hyperparameters you want to tune and their possible values or distributions.
- B. Incorrect.
Incorrect. Grid search evaluates all possible combinations of hyperparameter values, which is not the random search method.
- C. Correct.
Correct. In random search, you must specify the number of random samples to evaluate from the hyperparameter space, as it does not test all combinations.
- D. Incorrect.
Incorrect. Databricks does not have a specific built-in random search API but supports general hyperparameter tuning workflows through MLflow and custom code.
- E. Correct.
Correct. MLflow is a key tool in Databricks for tracking experiments and analyzing the results of random search to determine the best hyperparameter configuration.