Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Agregación por Bootstrap (Bagging)× | AdaBoost× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1996 | 1997 |
| Autor original≠ | Breiman, L. | Freund, Y. & Schapire, R.E. |
| Tipo≠ | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) | Ensemble (sequential boosting of weak learners) |
| Fuente seminal≠ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ | 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 ↗ |
| Alias≠ | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma |
| Relacionados | 5 | 5 |
| Resumen≠ | Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner. | AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification. |
| ScholarGateConjunto de datos ↗ |
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