Comparer des méthodes

	Arbre de décision explicable	XGBoost
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1984 (CART); XAI framing formalized 2010s–2020s	2016
Auteur d'origine	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.	Chen, T. & Guestrin, C.
Type	Interpretable supervised learning model	Ensemble (gradient-boosted decision trees)
Source fondatrice	Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree	XGBoost, extreme gradient boosting, scalable tree boosting
Apparentées	4	5
Résumé	An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes.	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.