Comparer des méthodes

	Forêt Aléatoire	Arbre de décision	XGBoost
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2001	1984	2016
Auteur d'origine	Breiman, L.	Breiman, Friedman, Olshen & Stone	Chen, T. & Guestrin, C.
Type	Ensemble (bagging of decision trees)	Recursive partitioning (if-then rules)	Ensemble (gradient-boosted decision trees)
Source fondatrice	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	XGBoost, extreme gradient boosting, scalable tree boosting
Apparentées	4	5	5
Résumé	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.	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.