Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Bagging (Bootstrap Aggregating)× | Boosting× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1996 | 1990–1997 |
| Autor original≠ | Breiman, L. | Schapire, R. E.; Freund, Y. |
| Tipo≠ | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) | Sequential ensemble (iterative reweighting) |
| Fonte 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 ↗ |
| Outros nomes≠ | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Relacionados≠ | 5 | 6 |
| Resumo≠ | 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. | 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. |
| ScholarGateConjunto de dados ↗ |
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