Сравнение на методи

	Обясним метод на K-най-близки съседи	Дърво на решенията
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	1967 (KNN); 2010s (explainability extensions)	1984
Създател	Cover, T. & Hart, P. (KNN); XAI extensions by various authors	Breiman, Friedman, Olshen & Stone
Тип	Instance-based learning with explainability layer	Recursive partitioning (if-then rules)
Основополагащ източник	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.
Други названия	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Свързани	4	5
Резюме	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.