Which method is appropriate for Bayesian Hyperparameter inference for distributed models?

Bayesian Hyperparameter inference is particularly well-suited for optimizing hyperparameters in machine learning models. Among the methods listed, Hyperopt is designed explicitly for this purpose, allowing for efficient exploration of hyperparameter spaces.

Hyperopt employs a probabilistic model to iteratively suggest hyperparameters based on the performance of previous trials, which can be especially beneficial when dealing with distributed models. This approach allows Hyperopt to intelligently focus the search in areas of the hyperparameter space that are likely to yield better results, leading to more efficient optimization than methods like grid search or random search.

The other methods available, such as GridSearchCV and RandomizedSearchCV, do not utilize Bayesian optimization principles and are generally more exhaustive or less focused. GridSearchCV systematically evaluates every possible combination of parameters, which can be computationally expensive, especially in high-dimensional spaces. RandomizedSearchCV samples from a parameter distribution but does not optimize based on past results as Hyperopt does. Optuna, while also a Bayesian optimization framework, is not directly mentioned to be the focus here, making Hyperopt the more relevant choice for distributed models when it comes to Bayesian hyperparameter inference.