Which of the following best describes the role of hyper-parameters in modeling?

Hyper-parameters play a vital role in the modeling process, as they are essential configurations defined by the user before the training phase begins. These parameters set the groundwork for the behavior of the learning algorithm and can significantly influence the model's performance. For instance, hyper-parameters determine aspects such as the learning rate, the number of layers in a neural network, or the number of epochs during training.

By tuning hyper-parameters, practitioners can optimize their models for better accuracy, efficiency, and generalization to unseen data. This stands in contrast to parameters that are learned by the model itself during the training process, which adjust dynamically based on the data. The ability to set and adjust hyper-parameters allows for more control over the model's training and performance outcomes.