Consider a situation where you are tuning hyperparameters for a machine learning model that is sensitive to overfitting. Discuss how you would use Hyperopt to address this issue, including the selection of appropriate search algorithms, the definition of the search space, and the use of validation metrics.

Simulated

Use a random search algorithm with a broad search space and focus on training metrics to avoid overfitting.

8.3%

Select a Bayesian optimization algorithm, define a narrow search space to limit exploration, and use validation metrics to monitor overfitting.

70.8%

Avoid using Hyperopt for models sensitive to overfitting as it cannot prevent overfitting.

8.3%

Increase the number of trials to ensure comprehensive coverage of the hyperparameter space, regardless of overfitting risks.

12.5%

Databricks Certified Machine Learning - Associate