What is a hyperparameter in the context of machine learning?

A hyperparameter is indeed characterized as a parameter whose value is set before the learning process begins. In machine learning, hyperparameters are crucial because they influence the training process and the performance of the model. They include values such as the learning rate, the number of epochs, the batch size, or the architecture of the model itself.

Setting hyperparameters correctly can significantly affect how well the model learns from the data. Unlike parameters, which are learned from the data during training (like weights in a neural network), hyperparameters must be determined beforehand. This distinction is essential since the optimization of these hyperparameters often requires separate techniques, such as grid search or random search, to find the most effective configuration.

Understanding the role of hyperparameters helps in effectively managing model complexity, controlling overfitting, and improving generalization capabilities, which are key aspects in building robust machine learning systems.