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Given a Spark ML model and a dataset, how would you use Hyperopt to perform Bayesian hyperparameter inference for distributed models? Provide a code snippet that demonstrates the integration of Hyperopt with Spark MLlib for this purpose.
A
Use Hyperopt's fmin function to define the search space and objective function, and then pass the Spark ML model as the objective function to perform Bayesian hyperparameter inference.
B
Integrate Hyperopt with Spark MLlib by creating a custom Spark ML estimator and using Hyperopt's fmin function to optimize its hyperparameters.
C
Hyperopt cannot be used for Bayesian hyperparameter inference in distributed models, as it is designed for single-node models only.
D
Use Spark MLlib's built-in hyperparameter tuning methods instead of Hyperopt, as they are specifically designed for distributed models.
