Porównaj metody

	Random Forest	Robust Bagging
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2001	1996–2000s
Twórca	Breiman, L.	Breiman, L. (bagging); robust variants developed by various authors in 2000s
Typ	Ensemble (bagging of decision trees)	Ensemble (robust bootstrap aggregating)
Źródło pierwotne	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
Inne nazwy	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing
Pokrewne	4	6
Podsumowanie	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.	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.