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	Gradient Boosting	Albero decisionale regolarizzato
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	2001	1984
Ideatore	Friedman, J. H.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Tipo	Ensemble (sequential boosting of decision trees)	Supervised learning (regularized tree)
Fonte seminale	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
Alias	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Correlati	5	6
Sintesi	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees.