Comparer des méthodes

	Extra Trees Explicables	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006 (Extra Trees); 2017 (SHAP integration)	2001
Auteur d'origine	Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer)	Breiman, L.
Type	Ensemble (randomized trees) with post-hoc explainability	Ensemble (bagging of decision trees)
Source fondatrice	Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	XAI-ET, Explainable ET, Interpretable Extra Trees, Extra Trees with SHAP	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	5	4
Résumé	Explainable Extra Trees combines the Extremely Randomized Trees (Extra Trees) ensemble algorithm with post-hoc explainability methods — most commonly SHAP values — to deliver both strong predictive performance and transparent, feature-level explanations. It extends the classic Extra Trees classifier or regressor so that every prediction can be decomposed into individual feature contributions, satisfying demands for accountability in applied and regulated domains.	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.