Σύγκριση μεθόδων

	Παλινδρόμηση Lasso	Παλινδρόμηση Poisson και Αρνητική Διωνυμική
Πεδίο	Μηχανική Μάθηση	Οικονομετρία
Οικογένεια	Machine learning	Regression model
Έτος προέλευσης	1996	1998
Δημιουργός	Tibshirani, R.	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Τύπος	Regularized linear regression (L1 penalty)	Generalized linear model for count data
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Συναφείς	4	4
Σύνοψη	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.