What is hyperparameter tuning?

Hyperparameter tuning refers to the process of optimizing the hyperparameters of a machine learning model to improve its performance. Hyperparameters are the configurations that are set before the learning process begins and cannot be learned directly from the training data. They influence how the model is trained and can include settings like learning rate, batch size, number of hidden layers, and regularization parameters.

By fine-tuning these hyperparameters, practitioners aim to find the optimal settings that enhance the model's ability to generalize well to unseen data, which ultimately leads to better predictive performance. The tuning process often involves techniques such as grid search, random search, or more advanced methods like Bayesian optimization, assessing the model's performance using validation datasets to ensure the chosen parameters lead to lower errors or improved accuracy.

The other options focus on different aspects of model training or data handling, such as minimizing loss, increasing data volume, or making interface adjustments, none of which encapsulate the specific process of hyperparameter tuning.