Which ensemble method trains models sequentially, where each new model attempts to correct the errors made by previous ones?
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Boosting
Boosting algorithms, such as AdaBoost and Gradient Boosting, convert weak learners into strong learners by focusing on training samples that were misclassified in previous iterations. This is distinct from Bagging, which trains models in parallel.
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- Which ensemble technique converts weak learners into strong learners by training them in a sequence to fix prior errors?