Hyperparameter Tuning

Hyperparameter tuning refers to the process of finding the best values for the hyperparameters of a machine learning model. It involves setting boundaries on the search space to avoid an endless search. Various strategies, such as grid search, Bayesian optimization, gradient-based optimization, and random search, can be employed to efficiently explore the hyperparameter space.