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| Regressione per passi× | Regressione Ridge× | |
|---|---|---|
| Campo≠ | Statistica | Apprendimento automatico |
| Famiglia≠ | Regression model | Machine learning |
| Anno di origine≠ | 1960 | 1970 |
| Ideatore≠ | M. A. Efroymson | Hoerl, A.E. & Kennard, R.W. |
| Tipo≠ | Automated variable selection | L2-regularized linear regression |
| Fonte seminale≠ | Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical Methods for Digital Computers (pp. 191–203). Wiley. link ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Alias≠ | stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | 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. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
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