Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Lasso× | Regressão de Poisson e Binomial Negativa× | |
|---|---|---|
| Área≠ | Aprendizado de máquina | Econometria |
| Família≠ | Machine learning | Regression model |
| Ano de origem≠ | 1996 | 1998 |
| Autor original≠ | Tibshirani, R. | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Tipo≠ | Regularized linear regression (L1 penalty) | Generalized linear model for count data |
| Fonte seminal≠ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Outros nomes | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Relacionados | 4 | 4 |
| Resumo≠ | 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. |
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