Hyperparameter tuning is not necessary in AutoML, as the algorithm automatically selects the best hyperparameters for the model.

Hyperparameter tuning is important in AutoML, but it is performed manually by the user by trying different combinations of hyperparameters and selecting the best combination.

Hyperparameter tuning is important in AutoML, and it can be automated using techniques such as grid search, random search, and Bayesian optimization to find the optimal hyperparameters for the model.

Hyperparameter tuning is not relevant for classification models in AutoML, as the algorithm automatically selects the best model architecture and hyperparameters.