Comparer des méthodes

	LIME : Explications Locales Interprétables Agnostiques du Modèle	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2016	2001
Auteur d'origine	Marco Ribeiro, Sameer Singh & Carlos Guestrin	Breiman, L.
Type	post-hoc local explanation	Ensemble (bagging of decision trees)
Source fondatrice	Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Local Surrogate Explanations, Model-Agnostic Local Explanations, Locally Faithful Approximations, Yerel Yorumlanabilir Model-Bağımsız Açıklamalar	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	2	4
Résumé	LIME, introduced by Ribeiro, Singh, and Guestrin in 2016, explains the predictions of any black-box classifier or regressor by building a simple, locally faithful surrogate model around a single prediction of interest. Rather than explaining the global model, LIME focuses on why a specific instance was classified the way it was, making complex models such as deep neural networks and ensemble methods interpretable to end-users, domain experts, and auditors.	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.