Comparer des méthodes

	Boosting	Arbre de décision
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1990–1997	1984
Auteur d'origine	Schapire, R. E.; Freund, Y.	Breiman, Friedman, Olshen & Stone
Type	Sequential ensemble (iterative reweighting)	Recursive partitioning (if-then rules)
Source fondatrice	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.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Alias	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Apparentées	6	5
Résumé	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 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.