Linganisha mbinu

	Regressioni ya Elastic Net	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)
Nyanja	Takwimu	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2005	2019
Mwanzilishi	Hui Zou and Trevor Hastie	Wooldridge (textbook treatment); classical least squares
Aina	Penalized linear regression	Linear regression
Chanzo asilia	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Majina mbadala	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Zinazohusiana	6	5
Muhtasari	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).