Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Регресія Еластичної Мережі× | Гребенева регресія× | |
|---|---|---|
| Галузь≠ | Статистика | Машинне навчання |
| Родина≠ | Regression model | Machine learning |
| Рік появи≠ | 2005 | 1970 |
| Автор методу≠ | Hui Zou and Trevor Hastie | Hoerl, A.E. & Kennard, R.W. |
| Тип≠ | Penalized linear regression | L2-regularized linear regression |
| Основоположне джерело≠ | Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Інші назви | elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Пов'язані≠ | 6 | 4 |
| Підсумок≠ | Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone. | 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. |
| ScholarGateНабір даних ↗ |
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