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In the context of using Hyperopt for hyperparameter tuning, explain how Bayesian inference can be leveraged to optimize the search for the best hyperparameters in a distributed model. Discuss the advantages and potential drawbacks of this approach.
A
Bayesian inference allows for a more systematic exploration of the hyperparameter space by leveraging previous evaluations to guide future searches, but it may require more computational resources.
B
Bayesian inference is only applicable to single-node models and cannot be used in distributed settings.
C
Bayesian inference does not improve the search for hyperparameters and should not be used in any model tuning scenarios.
D
Bayesian inference is primarily used for reducing the number of trials needed, but it does not affect the accuracy of the model.
