Comparer des méthodes

	Arbre de décision régularisé	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1984	2001
Auteur d'origine	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Breiman, L.
Type	Supervised learning (regularized tree)	Ensemble (bagging of decision trees)
Source fondatrice	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	6	4
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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.