Databricks Machine Learning Associate Question 208
Select 3You are using Bayesian optimization to tune the hyperparameters of a machine learning model within Databricks. Which of the following statements correctly describe how Bayesian optimization works and its advantages over random search?
- A
Bayesian optimization uses a probabilistic model to predict the performance of hyperparameter configurations.
- B
Bayesian optimization requires evaluating all possible hyperparameter configurations to find the optimal one.
- C
Bayesian optimization balances exploration and exploitation to efficiently search the hyperparameter space.
- D
Bayesian optimization is particularly useful when hyperparameter evaluation is computationally expensive.
- E
Bayesian optimization guarantees finding the global optimum for hyperparameters every time it is run.
Show answer and explanation
Correct answers: A, C, D
Explanation
Bayesian optimization is a strategy for hyperparameter tuning that relies on building a probabilistic model to predict the performance of different configurations. It is particularly useful when model training is computationally expensive, as it reduces the number of evaluations needed by balancing exploration and exploitation. However, it doesn't guarantee finding the global optimum in every run.
- A. Correct.
Correct. Bayesian optimization uses a surrogate model, such as Gaussian processes, to make probabilistic predictions about the performance of hyperparameters, enabling efficient tuning.
- B. Incorrect.
Incorrect. Bayesian optimization does not require evaluating all possible configurations. Instead, it uses a probabilistic model to strategically sample from the hyperparameter space.
- C. Correct.
Correct. Bayesian optimization effectively balances exploration (trying new hyperparameter configurations) and exploitation (refining configurations known to perform well) to optimize the search process.
- D. Correct.
Correct. Bayesian optimization is especially advantageous when evaluating a hyperparameter configuration is computationally expensive, as it aims to minimize the number of evaluations needed.
- E. Incorrect.
Incorrect. Bayesian optimization does not guarantee finding the global optimum but rather focuses on efficiently finding high-performing configurations within the search space.