Comparer des méthodes

	Ensemble d'empilement explicable	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1992 (stacking); 2010s–2020s (explainable extensions)	2001
Auteur d'origine	Wolpert, D. H. (stacking); XAI integration developed across the community	Breiman, L.
Type	Ensemble meta-learning with post-hoc or intrinsic interpretability	Ensemble (bagging of decision trees)
Source fondatrice	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	XAI-Stacking, interpretable stacking, transparent stacking ensemble, explainable stacked generalisation	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	4	4
Résumé	Explainable Stacking Ensemble combines the predictive power of stacked generalisation — training a meta-learner on the outputs of multiple diverse base models — with interpretability tools such as SHAP or LIME that reveal how each base model and each input feature contributed to the final prediction. It bridges the accuracy–transparency trade-off that makes pure stacking opaque in high-stakes settings.	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.