Comparer des méthodes

	Gradient Boosting Explicable	Arbre de décision explicable
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017–2020	1984 (CART); XAI framing formalized 2010s–2020s
Auteur d'origine	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.
Type	Ensemble + explainability layer	Interpretable supervised learning model
Source fondatrice	Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2, 56–67.	Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8
Alias	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree
Apparentées	6	4
Résumé	Explainable Gradient Boosting combines the predictive power of gradient boosting ensembles with structured interpretability tools — principally SHAP (SHapley Additive exPlanations) — to produce models that are both highly accurate and transparently auditable. Practitioners obtain global feature rankings and individual-level explanations alongside standard performance metrics.	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.