مقایسهٔ روش‌ها

	رگرسیون الاستیک نت (Elastic Net Regression)	رگرسیون لسو
حوزه	آمار	یادگیری ماشین
خانواده	Regression model	Machine learning
سال پیدایش	2005	1996
پدیدآور	Hui Zou and Trevor Hastie	Tibshirani, R.
نوع	Penalized linear regression	Regularized linear regression (L1 penalty)
منبع بنیادین	Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
نام‌های دیگر	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
مرتبط	6	4
خلاصه	Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone.	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.