Порівняння методів

	Метод опорних векторів (класифікація)	К-найближчі сусіди	Випадковий ліс
Галузь	Машинне навчання	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning	Machine learning
Рік появи	1995	1967	2001
Автор методу	Cortes, C. & Vapnik, V.	Cover, T.M. & Hart, P.E.	Breiman, L.
Тип	Maximum-margin classifier (kernel method)	Instance-based (non-parametric) learning	Ensemble (bagging of decision trees)
Основоположне джерело	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Інші назви	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Пов'язані	5	5	4
Підсумок	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.	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.	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.