Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Árvore de Decisão em Ensemble× | Random Forest× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1996–2000 | 2001 |
| Autor original≠ | Breiman, L.; Dietterich, T. G. | Breiman, L. |
| Tipo≠ | Ensemble (multiple decision trees combined) | Ensemble (bagging of decision trees) |
| Fonte seminal≠ | Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Outros nomes | decision tree ensemble, ensemble of decision trees, combined decision trees, multiple classifier system (decision trees) | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification and regression tasks. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
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