Example Concept: Hyperparameter optimization is the process of finding the best set of hyperparameters for a machine learning model. This is typically done by defining a search space for each hyperparameter and using methods like grid search, random search, or Bayesian optimization to explore this space. The goal is to identify the hyperparameter values that result in the best model performance on a validation dataset.