Databricks Certified Machine Learning - Associate
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In the context of using Hyperopt for hyperparameter tuning, what is the role of the objective function's return value, and how does it influence the optimization process?
A
The objective function's return value is not relevant to the optimization process, as it does not influence the selection of hyperparameters.
B
The objective function's return value is used to evaluate the performance of the model for a given set of hyperparameters, and it directly influences the selection of hyperparameters in the optimization process.
C
The objective function's return value is only relevant for distributed models and does not play a role in the optimization process for single-node models.
D
The objective function's return value is a fixed value that does not change during the optimization process and does not affect the selection of hyperparameters.