Example Concept: Hyperparameter optimization can be achieved through techniques such as grid search, random search, or more advanced methods like Bayesian optimization. These methods involve defining a search space for each hyperparameter and evaluating the model's performance using a validation set. Grid search exhaustively tries all combinations, while random search samples random combinations. Bayesian optimization uses probabilistic models to predict the best hyperparameters, often requiring fewer evaluations.