Bayesian Optimization is a strategy for the optimization of expensive and noisy functions. This method is particularly useful when the function evaluations are costly, as it allows for efficient exploration and exploitation of the search space. It leverages probabilistic models to predict the performance of different parameter configurations and make informed decisions on where to sample next.
