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| Lépésenkénti Regresszió× | Elastic Net× | |
|---|---|---|
| Tudományterület≠ | Statisztika | Gépi tanulás |
| Módszercsalád≠ | Regression model | Machine learning |
| Keletkezés éve≠ | 1960 | 2005 |
| Megalkotó≠ | M. A. Efroymson | Zou, H. & Hastie, T. |
| Típus≠ | Automated variable selection | Regularized linear regression (L1 + L2 penalty) |
| Alapmű≠ | Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical Methods for Digital Computers (pp. 191–203). Wiley. link ↗ | 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 ↗ |
| Alternatív nevek≠ | stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression |
| Kapcsolódó≠ | 5 | 4 |
| Összefoglaló≠ | Stepwise regression is an automated variable selection procedure for multiple linear regression that adds or removes predictor variables one at a time according to a statistical criterion, typically the F-statistic or a p-value threshold. The forward-selection algorithm was formally described by Efroymson (1960) and the bidirectional variant was popularised by Draper and Smith in their landmark 1966 text Applied Regression Analysis. Despite widespread historical use, the method is now widely critiqued, making its documentation essential in any canonical methods library. | 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. |
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