So sánh phương pháp

	Hồi quy Logistic Chính quy	Hồi quy tuyến tính chính quy hóa
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1996–2005	1970–2005
Người khởi xướng	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
Loại	Penalized classification model	Penalized linear model
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.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Tên gọi khác	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
Liên quan	5	4
Tóm tắt	Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.	Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.