Azure Data Scientists Associate Practice Exam

Which method should be used to tune hyperparameters while balancing exploration and exploitation?

1. Random Search

2. Grid Search

3. Bayesian Sampling

4. Exhaustive Search

The correct answer is: Bayesian Sampling

The method that is most effective for tuning hyperparameters while balancing exploration and exploitation is Bayesian Sampling. This approach utilizes probabilistic modeling to make informed decisions about which hyperparameters to explore next based on the data it has already seen. By estimating the distribution of the function that maps hyperparameters to model performance, Bayesian Sampling can effectively identify areas of the hyperparameter space that are likely to yield better results. This method contrasts with other approaches such as random search and grid search, where there may be less strategic evaluation of hyperparameters. While random search explores the hyperparameter space randomly and grid search evaluates every combination within a predefined grid, they do not adaptively focus on areas that show promise based on previous evaluations. Exhaustive search examines all possible combinations, which can be computationally expensive and impractical for large hyperparameter spaces. Bayesian Sampling’s balance of exploration (searching new areas of the space) and exploitation (refining known promising areas) makes it particularly suitable for hyperparameter tuning in scenarios where computational resources are limited or when it is critical to find optimal settings efficiently.