BERT

Bayesian Optimization is an efficient method for tuning hyperparameters by constructing a probabilistic model, usually a Gaussian process, of the objective function. It intelligently explores the search space by balancing exploration of uncertain areas and exploitation of promising regions, leading to faster convergence on optimal solutions in machine learning tasks. This approach is particularly useful for expensive or complex model evaluations.