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	मजबूत बैगिंग	मतदान एनसेंबल
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1996–2000s	1990s–2004
प्रवर्तक	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Lam & Suen; Kuncheva, L. I. (systematic treatment)
प्रकार	Ensemble (robust bootstrap aggregating)	Ensemble (combination of multiple classifiers by vote)
मौलिक स्रोत	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
उपनाम	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
संबंधित	6	5
सारांश	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.