Сравнение методов

	Регрессия на основе опорных векторов	Метод K ближайших соседей	Регрессия Лассо
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	2004	1967	1996
Автор метода	Smola, A.J. & Schölkopf, B.	Cover, T.M. & Hart, P.E.	Tibshirani, R.
Тип	Kernel-based supervised model (epsilon-insensitive regression)	Instance-based (non-parametric) learning	Regularized linear regression (L1 penalty)
Основополагающий источник	Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222.	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Другие названия	Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Связанные	4	5	4
Сводка	Support Vector Regression (SVR), described in Smola and Schölkopf's 2004 tutorial, predicts a continuous outcome by fitting a function that stays within an epsilon-wide tube around the data while incurring as little error as possible. It extends the support vector machine idea from classification to regression, using a kernel to capture nonlinear relationships.	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.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.