Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Elastic Net× | Random Forest× | Ridge Regression× | |
|---|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning | Machine learning |
| Ursprungsår≠ | 2005 | 2001 | 1970 |
| Upphovsperson≠ | Zou, H. & Hastie, T. | Breiman, L. | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Regularized linear regression (L1 + L2 penalty) | Ensemble (bagging of decision trees) | L2-regularized linear regression |
| Ursprungskälla≠ | 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Alias | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Närliggande | 4 | 4 | 4 |
| Sammanfattning≠ | 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. | 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. |
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