What are the differences between grid search and random search for hyperparameter tuning?

What are the differences between grid search and random search for hyperparameter tuning?

Grid search systematically explores all possible combinations of hyperparameters, while random search samples a subset of combinations randomly.

Which method is generally more efficient?

Random search is often more efficient, especially when the hyperparameter space is large, as it can find good configurations faster.

Are there any situations where grid search is preferred?

Yes, grid search can be preferred when the hyperparameter space is small and you want to ensure that all combinations are evaluated.

Can you give an example of when to use each method?

Use grid search for small models with few hyperparameters, and random search for complex models with many hyperparameters.