Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Potenciación× | Árbol de decisión regularizado× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1990–1997 | 1984 |
| Autor original≠ | Schapire, R. E.; Freund, Y. | Breiman, L., Friedman, J., Olshen, R., & Stone, C. |
| Tipo≠ | Sequential ensemble (iterative reweighting) | Supervised learning (regularized tree) |
| 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 ↗ | Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8 |
| Alias | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART |
| Relacionados | 6 | 6 |
| 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. | A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees. |
| ScholarGateConjunto de datos ↗ |
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