Vertaile menetelmiä

	Robust Bagging	Ääniennuste (Voting Ensemble)
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1996–2000s	1990s–2004
Kehittäjä	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Tyyppi	Ensemble (robust bootstrap aggregating)	Ensemble (combination of multiple classifiers by vote)
Alkuperäislähde	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
Rinnakkaisnimet	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Liittyvät	6	5
Tiivistelmä	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.