Describe the process of hyperparameter tuning using random search and discuss the advantages and disadvantages of this method compared to other tuning methods like grid search and Bayesian optimization. Provide examples of scenarios where random search might be the preferred choice.

Simulated

Random search is always inferior to grid search and Bayesian optimization and should not be used.

3.4%

Random search can be more efficient than grid search, especially when some hyperparameters are more important than others, but it is less efficient than Bayesian optimization.

96.6%

Random search is identical to grid search but with a different name.

0.0%

Random search is only used for tuning hyperparameters of deep learning models, not other types of models.

0.0%

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Describe the process of hyperparameter tuning using random search and discuss the advantages and disadvantages of this method compared to other tuning methods like grid search and Bayesian optimization. Provide examples of scenarios where random search might be the preferred choice.