Gradient Boosting is a machine learning technique that is used for both regression and classification tasks. It is a type of ensemble method that combines several weak learners into a strong learner.
Gradient Boosting algorithms build an additive model in a forward stagewise manner. Each new model is trained to minimize the loss function of the whole model, with respect to the residual errors made by the previous models. The algorithm takes an iterative approach, where each new model tries to correct the errors of the previous models.
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