What does it mean to tune hyperparameters in machine learning?

Tuning hyperparameters in machine learning involves adjusting the parameters that dictate how the learning algorithm behaves during the training process. These parameters influence various aspects such as the learning rate, the number of layers in a neural network, the number of trees in a random forest, and many other settings that determine how the model learns from the training data.

The main goal of hyperparameter tuning is to enhance the model's performance on unseen data, which is typically evaluated through techniques like cross-validation. By finding the optimal values for these hyperparameters, practitioners can significantly improve the accuracy and generalizability of the model.

This process is essential because it allows for experimentation and fine-tuning that can lead to a better fit for the data. For example, a learning rate that is too high might cause the model to converge too quickly to a suboptimal solution, whereas a learning rate that is too low could result in a prolonged training time without achieving significant improvements.

Choosing fixed values for parameters would not allow for this vital adaptability, halting the training process does not relate to tuning, and analyzing the model without modification sidesteps the critical aspect of adjustment needed to optimize performance.