পদ্ধতির তুলনা করুন

	নিয়ন্ত্রিত রৈখিক নির্ভরণ (Regularized Linear Regression)	ইলাস্টিক নেট
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1970–2005	2005
প্রবর্তক	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)	Zou, H. & Hastie, T.
ধরন	Penalized linear model	Regularized linear regression (L1 + L2 penalty)
মৌলিক উৎস	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.
অপর নাম	Ridge regression, Lasso regression, Elastic Net regression, penalized regression	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression
সম্পর্কিত	4	4
সারসংক্ষেপ	Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.	Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors.