Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Polynomická regrese× | Regrese Lasso× | |
|---|---|---|
| Obor≠ | Statistika | Strojové učení |
| Rodina≠ | Regression model | Machine learning |
| Rok vzniku≠ | 2012 | 1996 |
| Tvůrce≠ | Montgomery, Peck & Vining (textbook treatment); classical least squares | Tibshirani, R. |
| Typ≠ | Linear regression in transformed predictors | Regularized linear regression (L1 penalty) |
| Původní zdroj≠ | Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811 | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Další názvy≠ | polynomial least squares, curvilinear regression, Polinom Regresyonu | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Příbuzné | 4 | 4 |
| Shrnutí≠ | Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends. | 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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