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Answer: Parallelizing with Trials can be done by setting up a Spark cluster and distributing the trials across nodes, but this may not necessarily improve accuracy.
The Trials object in Hyperopt can be used to parallelize the hyperparameter tuning process by distributing trials across a Spark cluster. This setup can significantly speed up the tuning process. However, the relationship between the number of trials and model accuracy is not linear; too many trials can lead to overfitting or unnecessary resource consumption.
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Consider a scenario where you are tuning hyperparameters for a Spark ML model using Hyperopt. Describe how you would set up the Trials object to parallelize the tuning process and what considerations you need to make regarding the number of trials versus the model's accuracy.
A
The Trials object should be configured to run sequentially without parallelization to ensure stability.
B
Parallelizing with Trials can be done by setting up a Spark cluster and distributing the trials across nodes, but this may not necessarily improve accuracy.
C
Increasing the number of trials always leads to higher accuracy, so more trials should always be preferred.
D
Parallelization is not possible with Hyperopt when using Spark ML models.
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