विधियों की तुलना करें

	सपोर्ट वेक्टर रिग्रेशन	के-निकटतम पड़ोसी (K-Nearest Neighbors)	लासो रिग्रेशन	रिज रिग्रेशन
क्षेत्र	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	2004	1967	1996	1970
प्रवर्तक	Smola, A.J. & Schölkopf, B.	Cover, T.M. & Hart, P.E.	Tibshirani, R.	Hoerl, A.E. & Kennard, R.W.
प्रकार	Kernel-based supervised model (epsilon-insensitive regression)	Instance-based (non-parametric) learning	Regularized linear regression (L1 penalty)	L2-regularized linear regression
मौलिक स्रोत	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.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
उपनाम	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	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
संबंधित	4	5	4	4
सारांश	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.