Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Boosting× | Naive Bayes× | Random Forest× | |
|---|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning | Machine learning |
| Ano de origem≠ | 1990–1997 | 1997 | 2001 |
| Autor original≠ | Schapire, R. E.; Freund, Y. | Mitchell, T. M. (textbook treatment) | Breiman, L. |
| Tipo≠ | Sequential ensemble (iterative reweighting) | Probabilistic classifier (Bayes' theorem with conditional independence) | 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 ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Outros nomes≠ | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 6 | 4 | 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. | Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate. | 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. |
| ScholarGateConjunto de dados ↗ |
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