Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Bagging (Bootstrap Aggregating)× | Gradient Boosting× | Online Boosting× | |
|---|---|---|---|
| Vakgebied | Machine learning | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 1996 | 2001 | 2001 |
| Grondlegger≠ | Breiman, L. | Friedman, J. H. | Oza, N. C. & Russell, S. |
| Type≠ | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) | Ensemble (sequential boosting of decision trees) | Online ensemble (incremental boosting) |
| Oorspronkelijke bron≠ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann. link ↗ |
| Aliassen≠ | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting |
| Verwant≠ | 5 | 5 | 6 |
| Samenvatting≠ | 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. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. | Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments. |
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