AutoML does not support ensemble learning, as it relies on individual models for each task.

AutoML supports ensemble learning by manually selecting a group of models and combining their predictions using a voting or averaging mechanism.

AutoML supports ensemble learning by using techniques such as bagging, boosting, and stacking to combine the predictions of multiple models and improve the performance of the final预测model.

AutoML supports ensemble learning by automatically selecting the best individual model and using it as the final prediction model, without combining the predictions of multiple models.