Compară metode

	Bagging (Agregare Bootstrap)	Pădurea Aleatoare (Random Forest)	Împachetare Robustă (Robust Bagging)
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	1996	2001	1996–2000s
Autorul original	Breiman, L.	Breiman, L.	Breiman, L. (bagging); robust variants developed by various authors in 2000s
Tip	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (bagging of decision trees)	Ensemble (robust bootstrap aggregating)
Sursa seminală	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
Denumiri alternative	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing
Înrudite	5	4	6
Rezumat	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.	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.