What is Bayesian Optimization in Machine Learning?

What is Bayesian Optimization in Machine Learning?

Bayesian Optimization is a strategy for optimizing objective functions that are expensive to evaluate. It uses a probabilistic model to predict the performance of different configurations.

How does it differ from other optimization methods?

Unlike methods like grid search or random search, Bayesian Optimization builds a model of the objective function and uses it to make informed decisions about where to sample next.

Can you give me an example of how to implement it in Python?

Sure! You can use libraries like scikit-optimize or GPyOpt. Here's a simple example using scikit-optimize to optimize a function.

What kind of problems is it best suited for?

It's particularly useful for hyperparameter tuning in machine learning models, where evaluations are costly and time-consuming.