Comparer des méthodes

	Gradient Boosting Explicable	Forêt Aléatoire Explicable
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017–2020	2001–2017
Auteur d'origine	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)
Type	Ensemble + explainability layer	Interpretable ensemble (bagging + post-hoc attribution)
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.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Alias	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting	XRF, interpretable random forest, transparent random forest, random forest with explainability
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.	Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike.