Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Boosting× | Árvore de Decisão× | Random Forest× | |
|---|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning | Machine learning |
| Ano de origem≠ | 1990–1997 | 1984 | 2001 |
| Autor original≠ | Schapire, R. E.; Freund, Y. | Breiman, Friedman, Olshen & Stone | Breiman, L. |
| Tipo≠ | Sequential ensemble (iterative reweighting) | Recursive partitioning (if-then rules) | Ensemble (bagging 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 ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Outros nomes≠ | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 6 | 5 | 4 |
| Resumo≠ | 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. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. | 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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