What is the role of a hyperparameter in machine learning?

A hyperparameter plays a crucial role in controlling the training process of a machine learning model. These parameters are set before the learning begins and can significantly influence how the model learns from the data. Unlike model parameters, which are learned during training, hyperparameters impact various aspects such as the learning rate, the number of layers in a neural network, or the depth of a decision tree. By adjusting hyperparameters, practitioners can enhance model performance, prevent overfitting, and ensure that the model can generalize well to unseen data.

For instance, the learning rate determines how quickly a model updates its weights during training—a higher learning rate might converge faster but could also risk overshooting the optimal solution, while a lower rate might lead to more precise updates but could take a long time to converge. This control enables practitioners to experiment and find the most effective settings for their specific task or dataset, ultimately leading to better model performance.