Automated hyperparameter tuning refers to the process of systematically searching for the most optimal set of hyperparameters for a given model. This is typically done using algorithms that can efficiently explore the hyperparameter space, such as Bayesian optimization, random search, or grid search. In the context of machine learning models applied to nanotechnology, these hyperparameters can include learning rates, regularization parameters, and kernel functions, among others.
