Methoden vergelijken

	Lasso-regressie	Gewone Kleinste Kwadraten (GKK) Regressie	Poisson- en negatief-binomiale regressie
Vakgebied	Machine learning	Econometrie	Econometrie
Familie	Machine learning	Regression model	Regression model
Jaar van ontstaan	1996	2019	1998
Grondlegger	Tibshirani, R.	Wooldridge (textbook treatment); classical least squares	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Regularized linear regression (L1 penalty)	Linear regression	Generalized linear model for count data
Oorspronkelijke bron	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Aliassen	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Verwant	4	5	4
Samenvatting	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.