Comparer des méthodes

	Arbre de décision régularisé	Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1984	1990–1997
Auteur d'origine	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Schapire, R. E.; Freund, Y.
Type	Supervised learning (regularized tree)	Sequential ensemble (iterative reweighting)
Source fondatrice	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8	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.
Alias	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Apparentées	6	6
Résumé	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.	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.