Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Ensemble Gradient Boosting× | XGBoost× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2001 | 2016 |
| Autor original≠ | Friedman, J. H. | Chen, T. & Guestrin, C. |
| Tipo≠ | Ensemble (sequential boosting of decision trees) | Ensemble (gradient-boosted decision trees) |
| Fonte seminal≠ | Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Outros nomes≠ | Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting | XGBoost, extreme gradient boosting, scalable tree boosting |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateConjunto de dados ↗ |
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