Comparer des méthodes

	Machine à Vecteurs de Support Explicable	Forêt Aléatoire Explicable
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2016–2017 (XAI layer)	2001–2017
Auteur d'origine	Cortes & Vapnik (SVM); explainability layer via Lundberg & Lee (SHAP, 2017) and Ribeiro et al. (LIME, 2016)	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)
Type	Post-hoc explainability applied to SVM	Interpretable ensemble (bagging + post-hoc attribution)
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., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Alias	Explainable SVM, Interpretable SVM, XAI-SVM, Transparent Support Vector Machine	XRF, interpretable random forest, transparent random forest, random forest with explainability
Apparentées	4	4
Résumé	Explainable SVM combines a trained Support Vector Machine with a post-hoc interpretability layer — typically SHAP or LIME — to produce feature-level explanations for individual predictions and global importance rankings. It retains the discriminative power of SVM while meeting transparency requirements in high-stakes domains such as medicine, finance, and law.	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.