What are hyperparameters in machine learning, and how are they tuned?

Hyperparameters are parameters set before training a machine learning model, such as learning rate, regularization strength, and the number of estimators in ensemble models. Hyperparameters can be tuned using techniques like grid search, random search, or Bayesian optimization to find the best values that lead to optimal model performance.