Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Elastic Net× | Regressione Lasso× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2005 | 1996 |
| Ideatore≠ | Zou, H. & Hastie, T. | Tibshirani, R. |
| Tipo≠ | Regularized linear regression (L1 + L2 penalty) | Regularized linear regression (L1 penalty) |
| Fonte seminale≠ | Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 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 ↗ |
| Alias | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Correlati | 4 | 4 |
| Sintesi≠ | Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors. | 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. |
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