What does the term "hyperparameter tuning" refer to in machine learning?

Hyperparameter tuning refers to the process of optimizing parameters that control the learning process of a machine learning model before training. These hyperparameters, which influence the behavior of the training algorithm, can include aspects such as the learning rate, batch size, number of hidden layers, number of neurons in each layer, and more.

In this context, optimizing hyperparameters is crucial because they can significantly affect the model's accuracy, efficiency, and overall performance. For example, selecting the right learning rate can mean the difference between a model that converges effectively and one that fails to learn.

The other options describe different aspects of the machine learning process but do not capture the essence of hyperparameter tuning specifically. Modifying model architecture could be an important aspect of model design, but it doesn't fall under hyperparameter tuning. Changing the training dataset is related to data preparation and does not involve tuning parameters. Lastly, modifying loss functions pertains to how the model evaluates its predictions, rather than tuning the hyperparameters that guide the training process.