Comparer des méthodes

	Explications contrefactuelles	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017	2001
Auteur d'origine	Sandra Wachter, Brent Mittelstadt & Chris Russell	Breiman, L.
Type	Post-hoc, model-agnostic explanation	Ensemble (bagging of decision trees)
Source fondatrice	Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31, 841–887.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Algorithmic Recourse, Contrastive Explanations, What-If Explanations, Karşıolgusal Açıklamalar	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	2	4
Résumé	Counterfactual explanations, introduced by Wachter, Mittelstadt, and Russell in 2017, answer the question: 'What is the smallest change to the input that would have produced a different model output?' Rather than explaining why a model made a decision, they describe what would need to change for that decision to be reversed, making them particularly valuable for high-stakes applications such as credit scoring, medical diagnosis, and hiring decisions under frameworks like the EU GDPR.	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.