What are the differences between Bayesian Optimization and Grid Search in Python ML?

What are the differences between Bayesian Optimization and Grid Search in Python ML?

Bayesian Optimization is a probabilistic model that optimizes hyperparameters by learning from past evaluations, while Grid Search exhaustively searches through a specified parameter grid. Bayesian is often more efficient.

Which one is better for hyperparameter tuning?

It depends on the problem. Bayesian Optimization is generally more efficient for complex models, while Grid Search is simpler and can be effective for smaller parameter spaces.

Can you give an example of when to use each?

Use Grid Search for simpler models or when you have a small number of hyperparameters. Use Bayesian Optimization for complex models with many hyperparameters or when evaluations are expensive.

What libraries can I use for these methods in Python?

For Grid Search, you can use Scikit-learn's GridSearchCV. For Bayesian Optimization, libraries like BayesianOptimization or Optuna are popular.