Comparer des méthodes

	Arbre de décision régularisé	Arbre de décision
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1984	1984
Auteur d'origine	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Breiman, Friedman, Olshen & Stone
Type	Supervised learning (regularized tree)	Recursive partitioning (if-then rules)
Source fondatrice	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Alias	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Apparentées	6	5
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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.