Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Полиномиальная регрессия× | Регрессия Лассо× | |
|---|---|---|
| Область≠ | Статистика | Машинное обучение |
| Семейство≠ | Regression model | Machine learning |
| Год появления≠ | 2012 | 1996 |
| Автор метода≠ | Montgomery, Peck & Vining (textbook treatment); classical least squares | Tibshirani, R. |
| Тип≠ | Linear regression in transformed predictors | Regularized linear regression (L1 penalty) |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | polynomial least squares, curvilinear regression, Polinom Regresyonu | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Связанные | 4 | 4 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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