Explain 'Hyperparameter Tuning'.

Approach: Describe the process of finding the best configuration for the model.

The problem of choosing a set of optimal hyperparameters for a learning algorithm. It is typically done using search methods and cross-validation to maximize model performance.

Follow-up: What is the search space?

Key concepts: grid search, random search, bayesian optimization.