AutoML does not support hyperparameter optimization, as it relies on default hyperparameters for all models.

AutoML supports hyperparameter optimization by manually selecting a fixed set of hyperparameters for each model based on expert knowledge.

AutoML supports hyperparameter optimization by using techniques such as grid search, random search, and Bayesian optimization to explore the hyperparameter space and find the optimal hyperparameter values for the model.

AutoML supports hyperparameter optimization by automatically selecting the best hyperparameters based on the model's performance on a validation set, without exploring the hyperparameter space.