What does the term 'hyperparameter tuning' refer to?

The term 'hyperparameter tuning' specifically refers to the process of optimizing hyperparameters, which are the settings or configurations external to the model that can significantly influence its performance. These hyperparameters include settings such as learning rate, batch size, and the number of layers in a neural network. Unlike model parameters, which are learned directly from the training data during the training process, hyperparameters are set prior to the training and can affect how well the model learns and generalizes to unseen data.

By optimizing model complexity before training, practitioners aim to find the best combination of hyperparameters that will lead to a model that not only fits the training data well but also performs well on validation or test data. This careful tuning can prevent issues such as overfitting or underfitting, ultimately leading to a more robust model.

Contextually, adjusting model parameters during training is focused on the learning process itself and does not relate directly to hyperparameters. Modifying the training dataset pertains to the data preparation phase rather than hyperparameter adjustments. Increasing computational speed involves operational aspects but does not directly address the concept of tuning hyperparameters in terms of optimizing the model's architecture and learning process.