Databricks Machine Learning Associate Question 205
Select 3You are tasked with tuning the hyperparameters of a machine learning model in Databricks. Instead of using grid search or random search, you decide to use Bayesian optimization. Which of the following statements correctly describe the Bayesian optimization approach for hyperparameter tuning?
- A
Bayesian optimization uses a probabilistic model to predict the performance of hyperparameters.
- B
Bayesian optimization is computationally expensive because it evaluates every possible combination of hyperparameters exhaustively.
- C
Bayesian optimization updates its model iteratively based on the results of prior evaluations.
- D
Bayesian optimization requires a search space to be defined for the hyperparameters.
- E
Bayesian optimization guarantees finding the global optimum of hyperparameters.
Show answer and explanation
Correct answers: A, C, D
Explanation
Bayesian optimization is a powerful approach for hyperparameter tuning that uses a probabilistic model to balance exploration and exploitation. It requires a defined search space and iteratively updates its model based on prior evaluations. Unlike grid search or exhaustive methods, it is efficient and does not evaluate every combination of hyperparameters. While effective, it does not guarantee finding the global optimum.
- A. Correct.
Correct: Bayesian optimization uses a probabilistic model, such as Gaussian Processes, to estimate the performance of hyperparameters and guide the search process.
- B. Incorrect.
Incorrect: Bayesian optimization does not evaluate every possible combination of hyperparameters. Instead, it uses a probabilistic model to intelligently select the next set of hyperparameters to evaluate, making it more efficient than exhaustive methods.
- C. Correct.
Correct: Bayesian optimization iteratively updates its model based on the performance of previously evaluated hyperparameters, improving its predictions over time.
- D. Correct.
Correct: Bayesian optimization requires the user to define a search space (ranges and types) for the hyperparameters, as this space determines where the optimization will occur.
- E. Incorrect.
Incorrect: Bayesian optimization does not guarantee finding the global optimum. However, it is often more effective than random or grid search at finding near-optimal solutions in fewer evaluations.