Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Пошаговая регрессия× | Регрессия Лассо× | |
|---|---|---|
| Область≠ | Статистика | Машинное обучение |
| Семейство≠ | Regression model | Machine learning |
| Год появления≠ | 1960 | 1996 |
| Автор метода≠ | M. A. Efroymson | Tibshirani, R. |
| Тип≠ | Automated variable selection | Regularized linear regression (L1 penalty) |
| Основополагающий источник≠ | Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical Methods for Digital Computers (pp. 191–203). Wiley. link ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Другие названия≠ | stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Связанные≠ | 5 | 4 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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