Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Extra Trees× | Gradient Boosting× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2006 | 2001 |
| Autor original≠ | Geurts, P.; Ernst, D.; Wehenkel, L. | Friedman, J. H. |
| Tipo≠ | Ensemble (extremely randomized decision trees) | Ensemble (sequential boosting of decision trees) |
| Fuente seminal≠ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Alias | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Relacionados | 5 | 5 |
| Resumen≠ | Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time. | 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. |
| ScholarGateConjunto de datos ↗ |
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