Comparer des méthodes

	Gradient Boosting Explicable	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017–2020	2001
Auteur d'origine	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)	Breiman, L.
Type	Ensemble + explainability layer	Ensemble (bagging of decision trees)
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. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
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.	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.