Databricks Machine Learning Associate Question 206
Select 3You are tasked with tuning the hyperparameters of a machine learning model on Databricks. You want to use Bayesian optimization to efficiently search the hyperparameter space. Which of the following statements correctly describe the advantages of Bayesian methods for hyperparameter tuning?
- A
Bayesian optimization builds a probabilistic model of the objective function to guide the search for optimal hyperparameters.
- B
Bayesian optimization requires a large number of trials to achieve better results compared to random search.
- C
Bayesian optimization can incorporate prior knowledge about the hyperparameter space to make more informed decisions.
- D
Bayesian optimization uses acquisition functions to balance exploration and exploitation during the search process.
- E
Bayesian optimization cannot be used for hyperparameter tuning in Databricks.
Show answer and explanation
Correct answers: A, C, D
Explanation
Bayesian optimization is a powerful method for hyperparameter tuning that builds a probabilistic model of the objective function to efficiently search the hyperparameter space. It is particularly useful because it is sample-efficient, can incorporate prior knowledge, and uses acquisition functions to intelligently balance exploration and exploitation. These features make it a strong choice for optimizing machine learning models in Databricks.
- A. Correct.
Correct. Bayesian optimization builds a probabilistic model, such as a Gaussian process, to approximate the objective function and guide the search process.
- B. Incorrect.
Incorrect. Bayesian optimization is designed to be sample-efficient, meaning it can achieve better results with fewer trials compared to random search or grid search.
- C. Correct.
Correct. Bayesian optimization can incorporate prior knowledge, such as bounds or distributions of hyperparameters, to improve the search process.
- D. Correct.
Correct. Bayesian optimization uses acquisition functions like Expected Improvement (EI) or Upper Confidence Bound (UCB) to balance the trade-off between exploring new regions of the hyperparameter space and exploiting known good regions.
- E. Incorrect.
Incorrect. Bayesian optimization can be implemented in Databricks using libraries such as Hyperopt and MLflow.