So sánh phương pháp

	Hồi quy Lasso	Elastic Net	Hồi quy Logistic
Lĩnh vực	Học máy	Học máy	Thống kê nghiên cứu
Họ	Machine learning	Machine learning	Process / pipeline
Năm ra đời	1996	2005	1958
Người khởi xướng	Tibshirani, R.	Zou, H. & Hastie, T.	David Roxbee Cox
Loại	Regularized linear regression (L1 penalty)	Regularized linear regression (L1 + L2 penalty)	Method
Công trình gốc	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Tên gọi khác	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	logit model, binomial logistic regression, LR
Liên quan	4	4	3
Tóm tắt	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.	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.