Bayesian Optimization

Bayesian Optimization is a probabilistic approach to global optimization of black-box functions that are expensive to evaluate. It uses a probabilistic surrogate model to approximate the objective function and an acquisition function to decide the next point to evaluate. The surrogate model is updated with each new evaluation, and the acquisition function balances exploration and exploitation to find the global optimum with as few evaluations as possible. Bayesian Optimization has been successfully applied to hyperparameter tuning, neural architecture search, and other optimization problems in machine learning and data science.
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