Módszerek összehasonlítása

	Robust Bagging	Bagging (Bootstrap Aggregating)	Robusztus Boosting
Tudományterület	Gépi tanulás	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning	Machine learning
Keletkezés éve	1996–2000s	1996	1999–2001
Megalkotó	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Breiman, L.	Freund, Y.; Mason, L. et al.
Típus	Ensemble (robust bootstrap aggregating)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (robust sequential boosting)
Alapmű	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.
Alternatív nevek	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting
Kapcsolódó	6	5	6
Összefoglaló	Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions.	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.	Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors.