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Answer: Boost `num_evals` to 50
Correct answer is **E. Boost `num_evals` to 50**. Explanation: In hyperparameter tuning, the `num_evals` parameter determines how many different sets of hyperparameters the tuning process will evaluate. Increasing `num_evals` from 5 to 50 significantly expands the exploration of possible hyperparameter configurations, raising the probability of discovering a set that yields a more precise model. This is because the algorithm has a wider range of options to evaluate, potentially identifying a more optimal solution. Other options either do not inherently improve the search for a more precise model or would reduce the efficiency and scalability of the hyperparameter search process.
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A data scientist is using a code segment to refine hyperparameters for a machine learning model with the following parameters: num_evals = 5, trials = SparkTrials(), and inside the objective function, they use space-search space, algo-tpe.suggest, max_evals=num_evals, trials = trials. Which modification can they apply to the aforementioned code to increase the chances of obtaining a more precise model? Choose only ONE best answer.
A
Substitute tpe.suggest with random.suggest
B
Omit the algo-tpe.suggest argument
C
Replace faint() with fmax()
D
Switch SparkTrials() to Trials()
E
Boost num_evals to 50