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Answer: Parallelize the hyperparameter tuning process using Hyperopt's Trials, but limit the number of concurrent trials to match the available computational resources to avoid exceeding the budget.
In this scenario, parallelizing the hyperparameter tuning process using Hyperopt's Trials is essential to make the most of the available computational resources. However, it is crucial to balance the number of concurrent trials with the limited budget to avoid exceeding it. By carefully managing the parallelization process, it is possible to explore a reasonable portion of the hyperparameter space and potentially improve the model's accuracy within the given constraints.
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Consider a scenario where you are tasked with optimizing a Spark ML model using Hyperopt. You have a dataset with 1 million records and a limited computational budget. How would you approach the parallelization of the hyperparameter tuning process to maximize the model's accuracy within the given constraints?
A
Use a single-threaded approach and sequentially run the hyperparameter tuning trials, as parallelization is not necessary for Spark ML models.
B
Parallelize the hyperparameter tuning process using Hyperopt's Trials, but limit the number of concurrent trials to match the available computational resources to avoid exceeding the budget.
C
Increase the number of trials exponentially to maximize the exploration of the hyperparameter space, regardless of the computational budget.
D
Focus solely on the initial choice of hyperparameters and avoid any hyperparameter tuning, as the computational budget is too limited for an effective optimization process.
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