Linganisha mbinu

	Regressioni ya Elastic Net	Regression ya Kiasi (Quantile Regression)
Nyanja	Takwimu	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2005	1978
Mwanzilishi	Hui Zou and Trevor Hastie	Koenker & Bassett
Aina	Penalized linear regression	Conditional quantile 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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Majina mbadala	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	conditional quantile regression, regression quantiles, Kantil Regresyon
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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.