Hyperparameter Tuning

Gabriel works for a big tech company and is trying to build a machine-learning model that can accurately classify emails as spam or not spam.

He knows that choosing the right hyperparameters can greatly improve the model's performance, but he doesn't have much experience with hyperparameter tuning.

Which of the following statements summarizes the core goal of hyperparameter tuning?

Hyperparameter tuning is about choosing the set of optimal samples from the data to train a model.

Hyperparameter tuning is about choosing the set of optimal features from the data to train a model.

Hyperparameter tuning is about choosing the set of hypotheses that better fit the goal of the model.

Hyperparameter tuning is about choosing the optimal parameters for a learning algorithm to train a model.