So sánh phương pháp

	Hồi quy từng bước	Elastic Net
Lĩnh vực	Thống kê	Học máy
Họ	Regression model	Machine learning
Năm ra đời	1960	2005
Người khởi xướng	M. A. Efroymson	Zou, H. & Hastie, T.
Loại	Automated variable selection	Regularized linear regression (L1 + L2 penalty)
Công trình gốc	Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical Methods for Digital Computers (pp. 191–203). Wiley.	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.
Tên gọi khác	stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression
Liên quan	5	4
Tóm tắt	Stepwise regression is an automated variable selection procedure for multiple linear regression that adds or removes predictor variables one at a time according to a statistical criterion, typically the F-statistic or a p-value threshold. The forward-selection algorithm was formally described by Efroymson (1960) and the bidirectional variant was popularised by Draper and Smith in their landmark 1966 text Applied Regression Analysis. Despite widespread historical use, the method is now widely critiqued, making its documentation essential in any canonical methods library.	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.