เปรียบเทียบวิธี

	Lasso Regression	การถดถอยพัวซงและทวินามเชิงลบ
สาขาวิชา	การเรียนรู้ของเครื่อง	เศรษฐมิติ
ตระกูล	Machine learning	Regression model
ปีกำเนิด	1996	1998
ผู้ริเริ่ม	Tibshirani, R.	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
ประเภท	Regularized linear regression (L1 penalty)	Generalized linear model for count data
แหล่งต้นตำรับ	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
ชื่อเรียกอื่น	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
ที่เกี่ยวข้อง	4	4
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.	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.