Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Регрессия Лассо× | Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | Гребневая регрессия× | |
|---|---|---|---|
| Область≠ | Машинное обучение | Эконометрика | Машинное обучение |
| Семейство≠ | Machine learning | Regression model | Machine learning |
| Год появления≠ | 1996 | 2019 | 1970 |
| Автор метода≠ | Tibshirani, R. | Wooldridge (textbook treatment); classical least squares | Hoerl, A.E. & Kennard, R.W. |
| Тип≠ | Regularized linear regression (L1 penalty) | Linear regression | L2-regularized linear regression |
| Основополагающий источник≠ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Другие названия | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Связанные≠ | 4 | 5 | 4 |
| Сводка≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | 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. |
| ScholarGateНабор данных ↗ |
|
|
|