Databricks Certified Machine Learning - Associate
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In the context of optimizing a Spark ML model using Hyperopt, explain the significance of the number of trials in the hyperparameter tuning process and how it affects the model's accuracy. Provide a code snippet that demonstrates the use of Hyperopt's Trials to parallelize the tuning process.
A
The number of trials is irrelevant in the hyperparameter tuning process as the model's accuracy is solely determined by the initial choice of hyperparameters.
B
Increasing the number of trials in the hyperparameter tuning process can potentially improve the model's accuracy by exploring a wider range of hyperparameter combinations, but it also increases computational cost.
C
The number of trials is directly proportional to the model's accuracy, meaning that more trials always result in higher accuracy.
D
The relationship between the number of trials and model accuracy is not significant, as the model's accuracy is only affected by the quality of the initial hyperparameter guesses.