Bayesian Optimization

Bayesian Optimization is a probabilistic model-based optimization technique primarily used for optimizing functions that are expensive to evaluate. It employs a surrogate model, often a Gaussian process, to predict the performance of various configurations and guides the search for optimal solutions by balancing exploration and exploitation. This method is particularly useful in scenarios where evaluations are time-consuming, such as hyperparameter tuning in machine learning models or optimizing engineering designs. By systematically selecting the most promising points to evaluate based on previous results, Bayesian Optimization efficiently converges to a near-optimal solution with fewer evaluations compared to traditional optimization methods.