Methoden vergleichen

	Erklärbares K-Nächstes-Nachbarn-Verfahren	Entscheidungsbaum	LIME: Local Interpretable Model-agnostic Explanations	Random Forest
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning	Machine learning	Machine learning
Entstehungsjahr	1967 (KNN); 2010s (explainability extensions)	1984	2016	2001
Urheber	Cover, T. & Hart, P. (KNN); XAI extensions by various authors	Breiman, Friedman, Olshen & Stone	Marco Ribeiro, Sameer Singh & Carlos Guestrin	Breiman, L.
Typ	Instance-based learning with explainability layer	Recursive partitioning (if-then rules)	post-hoc local explanation	Ensemble (bagging of decision trees)
Wegweisende Quelle	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.	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.
Aliasnamen	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors	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
Verwandt	4	5	2	4
Zusammenfassung	Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers.	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.