Explanation:
The 'SparkTrials' class is specifically designed to parallelize computations for single-machine ML models, like those from sci-kit-learn. However, for models built with distributed ML algorithms such as MLlib or Horovod, the model-building process is already parallelized across the cluster. In these cases, it's recommended to use the default Hyperopt class 'Trials' instead of 'SparkTrials'.
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When is it advisable to use the default Hyperopt class 'Trials' over 'SparkTrials' in the context of hyperparameter tuning with Hyperopt?
A
When integrating with external experiments.
B
When dealing with large datasets.
C
When using distributed ML algorithms such as MLlib or Horovod.
D
When parallelizing computations for single-machine ML models.