Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| CatBoost× | Random Forest× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2018 | 2001 |
| Autor original≠ | Prokhorenkova, L. et al. (Yandex) | Breiman, L. |
| Tipo≠ | Gradient boosting on decision trees | Ensemble (bagging of decision trees) |
| Fonte seminal≠ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Outros nomes | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 5 | 4 |
| Resumo≠ | CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data. | 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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