विधियों की तुलना करें

	लासो रिग्रेशन	पॉइसन और ऋणात्मक द्विपद प्रतिगमन (Poisson and Negative Binomial Regression)
क्षेत्र	मशीन अधिगम	अर्थमिति
परिवार	Machine learning	Regression model
उद्भव वर्ष	1996	1998
प्रवर्तक	Tibshirani, R.	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
प्रकार	Regularized linear regression (L1 penalty)	Generalized linear model for count data
मौलिक स्रोत	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.
उपनाम	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
संबंधित	4	4
सारांश	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.