Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| AdaBoost× | Árvore de Decisão× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1997 | 1984 |
| Autor original≠ | Freund, Y. & Schapire, R.E. | Breiman, Friedman, Olshen & Stone |
| Tipo≠ | Ensemble (sequential boosting of weak learners) | Recursive partitioning (if-then rules) |
| 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 ↗ |
| Outros nomes≠ | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| 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. | 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. |
| ScholarGateConjunto de dados ↗ |
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