Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Elastic Net× | Regresión Lasso× | Random Forest× | |
|---|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning | Machine learning |
| Año de origen≠ | 2005 | 1996 | 2001 |
| Autor original≠ | Zou, H. & Hastie, T. | Tibshirani, R. | Breiman, L. |
| Tipo≠ | Regularized linear regression (L1 + L2 penalty) | Regularized linear regression (L1 penalty) | Ensemble (bagging of decision trees) |
| Fuente seminal≠ | 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 ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados | 4 | 4 | 4 |
| Resumen≠ | 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. | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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