Bayesian optimization
Bayesian optimization is a statistical method used for optimizing complex functions that are expensive to evaluate. It builds a probabilistic model of the function and uses this model to make informed decisions about where to sample next. This approach is particularly useful when the function evaluations are time-consuming or costly, such as in hyperparameter tuning for machine learning models.
The process involves two main components: a surrogate model, which approximates the function, and an acquisition function, which determines the next point to sample based on the surrogate model's predictions. By balancing exploration and exploitation, Bayesian optimization efficiently finds the optimal solution with fewer evaluations.