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Explain the basic principles of Bayesian methods for hyperparameter tuning. How do these methods differ from random search and grid search? Provide a detailed explanation of how Bayesian optimization works, including the role of the surrogate model and the acquisition function in guiding the search process.
Explain the basic principles of Bayesian methods for hyperparameter tuning. How do these methods differ from random search and grid search? Provide a detailed explanation of how Bayesian optimization works, including the role of the surrogate model and the acquisition function in guiding the search process.
Simulated
A
Bayesian methods are similar to grid search but use a probabilistic model to guide the search.
11.3%
B
Bayesian methods are more complex than random search and grid search but offer no significant advantages.
1.4%
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