bayesian-optimization

Bayesian optimization is used to optimize objective functions that are expensive to evaluate. It employs a probabilistic model to predict the performance of points in the search space and uses this information to choose the most promising candidates for evaluation. The method is particularly effective in scenarios where evaluations are costly, such as hyperparameter tuning in machine learning models. A common misconception is that it only works for simple functions; however, it can handle complex, noisy, and high-dimensional spaces.