Example Concept: Hyperparameter optimization is the process of finding the best set of hyperparameters for a machine learning model. It involves evaluating the model's performance on a validation set for different combinations of hyperparameters. Common methods include grid search, which exhaustively searches through a specified subset of hyperparameters, and random search, which randomly samples from the hyperparameter space. More advanced techniques, like Bayesian optimization, use probabilistic models to predict the performance of hyperparameter sets and focus on the most promising areas of the search space.