+Gradient Boosting is a powerful [Machine Learning](/wiki/machine_learning) technique for building predictive models. It sequentially combines many simple, weak prediction models, typically decision trees, where each new model corrects the errors of its predecessors by following a [Gradient](/wiki/gradient) in the loss function. This iterative refinement makes it highly effective for both classification and regression tasks.
+## See also
+- [Decision Tree](/wiki/decision_tree)
+- [Ensemble Learning](/wiki/ensemble_learning)
+- [Boosting](/wiki/boosting)
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