When should I use Bayesian optimization?

What is Bayesian optimization and when should I use it?

Bayesian optimization is a strategy for optimizing objective functions that are expensive to evaluate. You should use it when you have a limited number of evaluations and want to find the maximum or minimum of a function.

Can you give me an example of a scenario where it would be beneficial?

Sure! It's particularly useful in hyperparameter tuning for machine learning models, where each evaluation can be costly in terms of time and resources.

Are there any limitations to using Bayesian optimization?

Yes, it can struggle with high-dimensional spaces and may require a good prior to be effective. It's also computationally intensive.

How does it compare to other optimization methods?

Compared to methods like grid search or random search, Bayesian optimization is more efficient as it uses past evaluations to inform future ones, leading to faster convergence.