Krahasoni metodat

	Mbledhja Robuste (Robust Bagging)	Ensemble votimi
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	1996–2000s	1990s–2004
Krijuesi	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Lloji	Ensemble (robust bootstrap aggregating)	Ensemble (combination of multiple classifiers by vote)
Burimi themelues	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Emërtime të tjera	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Të lidhura	6	5
Përmbledhja	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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.