Сравнение методов

	Метод K ближайших соседей	Дерево решений
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1967	1984
Автор метода	Cover, T.M. & Hart, P.E.	Breiman, Friedman, Olshen & Stone
Тип	Instance-based (non-parametric) learning	Recursive partitioning (if-then rules)
Основополагающий источник	Cover, T.M. & Hart, P.E. (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.
Другие названия	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Связанные	5	5
Сводка	K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values.	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.