Comparer des méthodes

	LightGBM Explicable	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017	2001
Auteur d'origine	Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models)	Breiman, L.
Type	Gradient boosting with post-hoc explainability (SHAP)	Ensemble (bagging of decision trees)
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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	XAI-LightGBM, LightGBM with SHAP, Interpretable LightGBM, LightGBM explainability	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	6	4
Résumé	Explainable LightGBM combines Microsoft's LightGBM gradient boosting framework with SHAP (SHapley Additive exPlanations) to deliver both high predictive performance and rigorous, theoretically grounded feature-level explanations. It is widely adopted in applied research where predictive accuracy and interpretability are simultaneously required.	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.