Comparar métodos
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| Agregación por Bootstrap (Bagging)× | Empaquetado Robusto× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1996 | 1996–2000s |
| Autor original≠ | Breiman, L. | Breiman, L. (bagging); robust variants developed by various authors in 2000s |
| Tipo≠ | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) | Ensemble (robust bootstrap aggregating) |
| Fuente seminal | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ | Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. DOI ↗ |
| Alias≠ | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor | robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing |
| Relacionados≠ | 5 | 6 |
| 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. | Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions. |
| ScholarGateConjunto de datos ↗ |
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