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	Дрво одлучивања	LIME: Lokalno interpretabilna modelu-agnostička objašnjenja	Slučajna šuma
Oblast	Mašinsko učenje	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning	Machine learning
Godina nastanka	1984	2016	2001
Tvorac	Breiman, Friedman, Olshen & Stone	Marco Ribeiro, Sameer Singh & Carlos Guestrin	Breiman, L.
Tip	Recursive partitioning (if-then rules)	post-hoc local explanation	Ensemble (bagging of decision trees)
Temeljni izvor	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	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.
Drugi nazivi	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	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
Srodne	5	2	4
Sažetak	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.