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	Gradient Boosting Regolarizzato	Albero decisionale regolarizzato
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)	1984
Ideatore	Chen, T. & Guestrin, C. (building on Friedman, J. H.)	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Tipo	Regularized ensemble (additive tree model)	Supervised learning (regularized tree)
Fonte seminale	Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
Alias	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Correlati	6	6
Sintesi	Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.	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.