What is Bayesian Optimization in Python?

What is Bayesian Optimization in Python?

Bayesian Optimization is a strategy for optimizing objective functions that are expensive to evaluate. It uses a probabilistic model to find the minimum or maximum of a function.

How does it differ from other optimization methods?

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

Can you show me a simple example using Python?

Sure! You can use libraries like scikit-optimize to implement Bayesian Optimization. Here's a simple example:

from skopt import gp_minimize

# Define the objective function
def objective(x):
    return (x - 2) ** 2

# Perform Bayesian Optimization
result = gp_minimize(objective, [(-5.0, 5.0)])
print(result)

What are some applications of Bayesian Optimization?

It's widely used in hyperparameter tuning for machine learning models, optimizing engineering designs, and even in drug discovery.