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Gradient Boosted Decision Trees (GBDT) are a powerful machine learning technique used primarily for regression and classification tasks. They combine the predictions of multiple weak learners, usually decision trees, to create a strong predictive model. The core idea is to build models sequentially, each new model attempting to correct the errors made by the previous ones.
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