เปรียบเทียบวิธี

	Lasso Regression	การถดถอยโลจิสติก
สาขาวิชา	การเรียนรู้ของเครื่อง	สถิติการวิจัย
ตระกูล	Machine learning	Process / pipeline
ปีกำเนิด	1996	1958
ผู้ริเริ่ม	Tibshirani, R.	David Roxbee Cox
ประเภท	Regularized linear regression (L1 penalty)	Method
แหล่งต้นตำรับ	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
ชื่อเรียกอื่น	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	logit model, binomial logistic regression, LR
ที่เกี่ยวข้อง	4	3
สรุป	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.	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.