What are hyperparameters?

Hyperparameters are crucial in the context of machine learning as they play a significant role in shaping the training process of models. Specifically, hyperparameters are the parameters that are set before the training of a model begins. They govern aspects such as the model architecture, the learning rate, regularization techniques, and the number of training epochs, among others. By adjusting these hyperparameters, one can optimize the model's performance, making it an essential step in model tuning and selection.

The importance of hyperparameters lies in their ability to impact how the learning algorithm behaves during training. For example, a higher learning rate may lead to faster convergence but can also result in overshooting the optimal solution. Conversely, selecting too low of a learning rate might lead to longer training times without guaranteeing better performance.

By distinctly differentiating hyperparameters from other types of parameters, which are learned during the training phase (known as model parameters), it's essential to recognize that hyperparameters require careful selection and validation, often done through methods like grid search, random search, or more advanced techniques like Bayesian optimization.

This understanding helps in designing more effective machine learning workflows, ultimately leading to more robust models.