Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Elastic Net× | Regressão Lasso× | |
|---|---|---|
| Área≠ | Estatística | Aprendizado de máquina |
| Família≠ | Regression model | Machine learning |
| Ano de origem≠ | 2005 | 1996 |
| Autor original≠ | Hui Zou and Trevor Hastie | Tibshirani, R. |
| Tipo≠ | Penalized linear regression | Regularized linear regression (L1 penalty) |
| Fonte seminal≠ | Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Outros nomes | elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone. | 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. |
| ScholarGateConjunto de dados ↗ |
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