Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Регуляризована лінійна регресія× | Elastic Net× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1970–2005 | 2005 |
| Автор методу≠ | Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005) | Zou, H. & Hastie, T. |
| Тип≠ | Penalized linear model | Regularized linear regression (L1 + L2 penalty) |
| Основоположне джерело≠ | Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320. DOI ↗ |
| Інші назви | Ridge regression, Lasso regression, Elastic Net regression, penalized regression | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression |
| Пов'язані | 4 | 4 |
| Підсумок≠ | Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated. | Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors. |
| ScholarGateНабір даних ↗ |
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