Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Bagging (Bootstrap Aggregating)× | Random Forest× | Stacking× | |
|---|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning | Machine learning |
| Ano de origem≠ | 1996 | 2001 | 1992 |
| Autor original≠ | Breiman, L. | Breiman, L. | Wolpert, D.H. |
| Tipo≠ | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) | Ensemble (bagging of decision trees) | Ensemble (heterogeneous meta-learning) |
| Fonte seminal≠ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ | Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259. DOI ↗ |
| Outros nomes≠ | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner |
| Relacionados≠ | 5 | 4 | 5 |
| Resumo≠ | Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner. | 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. | Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions. |
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