In the context of hyperparameter tuning using Hyperopt, what is the role of the objective function, and how does it relate to the model's performance?

Simulated

The objective function is used to evaluate the performance of the model for a given set of hyperparameters and is not directly related to the model's accuracy.

21.4%

The objective function is a measure of the model's accuracy and is used to guide the hyperparameter search process towards better performing hyperparameter combinations.

78.6%

The objective function is only relevant for single-node models and does not play a role in the hyperparameter tuning of distributed models.

0.0%

The objective function is a fixed value that does not change during the hyperparameter tuning process and does not affect the model's performance.

0.0%

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In the context of hyperparameter tuning using Hyperopt, what is the role of the objective function, and how does it relate to the model's performance?