Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| AdaBoost× | Gradient Boosting× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1997 | 2001 |
| Autor original≠ | Freund, Y. & Schapire, R.E. | Friedman, J. H. |
| Tipo≠ | Ensemble (sequential boosting of weak learners) | Ensemble (sequential boosting of decision trees) |
| Fonte 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 ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Outros nomes≠ | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. |
| ScholarGateConjunto de dados ↗ |
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