What are the differences between Bayesian Optimization and Grid Search?

What are the differences between Bayesian Optimization and Grid Search?

Bayesian Optimization is a probabilistic model that finds the minimum of a function efficiently, while Grid Search exhaustively searches through a specified subset of hyperparameters.

Which one is more efficient for hyperparameter tuning?

Bayesian Optimization is generally more efficient, especially for expensive functions, as it uses past evaluations to inform future ones.

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

Use Grid Search for simpler models with fewer hyperparameters, and Bayesian Optimization for complex models where evaluations are costly.

Are there any tools or libraries that implement these methods?

Yes, popular libraries include Scikit-learn for Grid Search and Optuna or GPyOpt for Bayesian Optimization.