เปรียบเทียบวิธี

	การถดถอยแบบขั้นบันได	Elastic Net
สาขาวิชา	สถิติศาสตร์	การเรียนรู้ของเครื่อง
ตระกูล	Regression model	Machine learning
ปีกำเนิด	1960	2005
ผู้ริเริ่ม	M. A. Efroymson	Zou, H. & Hastie, T.
ประเภท	Automated variable selection	Regularized linear regression (L1 + L2 penalty)
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression
ที่เกี่ยวข้อง	5	4
สรุป	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.