Explain the role of Bayesian optimization in hyperparameter tuning and how it differs from traditional methods like grid search and random search. Provide a detailed explanation of the Bayesian optimization process, including the use of a surrogate model and an acquisition function.

Simulated

Bayesian optimization is identical to grid search but with a different name.

0.0%

Bayesian optimization uses a surrogate model to approximate the performance of the model with different hyperparameters and an acquisition function to decide which hyperparameters to evaluate next, making it more efficient than grid search and random search.

91.2%

Bayesian optimization is more complex than random search and grid search but offers no significant advantages.

0.0%

Bayesian optimization is only used for tuning hyperparameters of deep learning models, not other types of models.

8.8%

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Explain the role of Bayesian optimization in hyperparameter tuning and how it differs from traditional methods like grid search and random search. Provide a detailed explanation of the Bayesian optimization process, including the use of a surrogate model and an acquisition function.