What is the function of hyperparameter tuning during model training?

Hyperparameter tuning is a crucial step in the model training process as it focuses on optimizing the model's configuration. Hyperparameters are settings that are not learned from the data but rather set prior to the training phase. Examples include learning rates, the number of trees in a forest, and the depth of decision trees. By carefully adjusting these hyperparameters, practitioners can significantly improve a model's performance, resulting in better accuracy, reduced overfitting, and enhanced generalization capabilities.

This optimization process involves experimenting with different hyperparameter values to determine the combination that yields the best performance on a validation dataset. The goal is to find a balance that allows the model to learn effectively from the training data while still being able to adapt to new, unseen data.

The other options do not accurately reflect the purpose of hyperparameter tuning. Reducing model accuracy or making models slower are generally not beneficial, and limiting the number of features relates more to feature selection rather than the hyperparameter tuning process. Therefore, emphasizing the optimization of a model's configuration aptly captures the importance of hyperparameter tuning in predictive analytics.