The boosting process in the AutoML process achieves error reduction by observing the error records in a model and then oversampling misclassified records in the next model created.

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Boosting in AutoML focuses on sequentially improving the model by specifically targeting the errors of previous models. It increases the weight of misclassified records so that subsequent models pay more attention to them, aiming to reduce these errors over time.

The Boosting Process in the AutoML Process Achieves Error Reduction