Comparer des méthodes

	Ensemble de vote explicable	Gradient Boosting Explicable
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2016–2020	2017–2020
Auteur d'origine	Composite: voting ensemble (Dietterich, 2000) + XAI frameworks (Ribeiro et al., 2016; Lundberg & Lee, 2017)	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)
Type	Ensemble with post-hoc or ante-hoc interpretability	Ensemble + explainability layer
Source fondatrice	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	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.
Alias	XAI voting ensemble, interpretable voting classifier, transparent voting ensemble, explainable majority vote model	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting
Apparentées	6	6
Résumé	An Explainable Voting Ensemble combines predictions from multiple diverse base models through majority vote (hard voting) or averaged probabilities (soft voting), then applies post-hoc or ante-hoc XAI techniques — such as SHAP values, LIME, or permutation importance — to produce feature-level explanations for the combined model's decisions. The goal is to retain the accuracy gains of ensemble aggregation while meeting interpretability requirements in high-stakes or regulated applications.	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.