পদ্ধতির তুলনা করুন

	সাপোর্ট ভেক্টর রিগ্রেশন	ল্যাসো রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2004	1996
প্রবর্তক	Smola, A.J. & Schölkopf, B.	Tibshirani, R.
ধরন	Kernel-based supervised model (epsilon-insensitive regression)	Regularized linear regression (L1 penalty)
মৌলিক উৎস	Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
অপর নাম	Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
সম্পর্কিত	4	4
সারসংক্ষেপ	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.	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.