What role do hyperparameters play in model tuning?

Hyperparameters are crucial in determining how a machine learning model learns from the training data and how well it performs in terms of accuracy. They are settings that are not learned during the training process but rather set before the training begins, affecting the overall behavior and efficiency of the model.

When tuning a model, hyperparameters such as the learning rate, batch size, number of epochs, and regularization parameters directly control the learning process. For instance, the learning rate determines how quickly a model updates its weights during training, impacting how effectively it converges to a minimum loss. A well-tuned learning rate can significantly enhance model performance, while an incorrectly set rate may lead to underfitting or overfitting.

Additionally, hyperparameters influence various aspects of the training process, including handling overfitting through regularization techniques, adjusting the complexity of the model, and calibrating the model's sensitivity to the training data. By fine-tuning these hyperparameters, one can improve the model's accuracy and generalization to unseen data, making them a central focus in the optimization of machine learning algorithms.