Compară metode

	LIME: Explicații locale interpretabile, agnostice față de model	Explicații contrareale	Pădurea Aleatoare (Random Forest)
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	2016	2017	2001
Autorul original	Marco Ribeiro, Sameer Singh & Carlos Guestrin	Sandra Wachter, Brent Mittelstadt & Chris Russell	Breiman, L.
Tip	post-hoc local explanation	Post-hoc, model-agnostic explanation	Ensemble (bagging of decision trees)
Sursa seminală	Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144.	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.
Denumiri alternative	Local Surrogate Explanations, Model-Agnostic Local Explanations, Locally Faithful Approximations, Yerel Yorumlanabilir Model-Bağımsız Açıklamalar	Algorithmic Recourse, Contrastive Explanations, What-If Explanations, Karşıolgusal Açıklamalar	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Înrudite	2	2	4
Rezumat	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.	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.