Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Potenciación× | Gradient Boosting× | Random Forest× | |
|---|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning | Machine learning |
| Año de origen≠ | 1990–1997 | 2001 | 2001 |
| Autor original≠ | Schapire, R. E.; Freund, Y. | Friedman, J. H. | Breiman, L. |
| Tipo≠ | Sequential ensemble (iterative reweighting) | Ensemble (sequential boosting of decision trees) | Ensemble (bagging of decision trees) |
| Fuente seminal≠ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 6 | 5 | 4 |
| Resumen≠ | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. | 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. |
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