Comparer des méthodes

	Gradient Boosting Régularisé	Boosting	Arbre de décision régularisé
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)	1990–1997	1984
Auteur d'origine	Chen, T. & Guestrin, C. (building on Friedman, J. H.)	Schapire, R. E.; Freund, Y.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Type	Regularized ensemble (additive tree model)	Sequential ensemble (iterative reweighting)	Supervised learning (regularized tree)
Source fondatrice	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.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	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	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Apparentées	6	6	6
Résumé	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.