Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Potenciación× | Extra Trees× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1990–1997 | 2006 |
| Autor original≠ | Schapire, R. E.; Freund, Y. | Geurts, P.; Ernst, D.; Wehenkel, L. |
| Tipo≠ | Sequential ensemble (iterative reweighting) | Ensemble (extremely randomized 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 ↗ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ |
| Alias | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET |
| Relacionados≠ | 6 | 5 |
| 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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