Usporedite metode
Pregledajte odabrane metode jednu uz drugu; retci koji se razlikuju su istaknuti.
| Korak po korak regresija× | Elastic Net× | |
|---|---|---|
| Područje≠ | Statistika | Strojno učenje |
| Obitelj≠ | Regression model | Machine learning |
| Godina nastanka≠ | 1960 | 2005 |
| Tvorac≠ | M. A. Efroymson | Zou, H. & Hastie, T. |
| Vrsta≠ | Automated variable selection | Regularized linear regression (L1 + L2 penalty) |
| Temeljni izvor≠ | 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 ↗ |
| Drugi nazivi≠ | stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression |
| Srodne≠ | 5 | 4 |
| Sažetak≠ | 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. |
| ScholarGateSkup podataka ↗ |
|
|