GBDT (Gradient Boosted Decision Trees) is a machine learning technique that produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function. GBDTs are commonly used for regression and classification tasks, often achieving high accuracy.
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