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| Bayesian Bagging× | Röstningsensemble× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2001 | 1990s–2004 |
| Upphovsperson≠ | Clyde, M. & Lee, H. (building on Rubin's Bayesian bootstrap, 1981) | Lam & Suen; Kuncheva, L. I. (systematic treatment) |
| Typ≠ | Ensemble (Bayesian bootstrap aggregation) | Ensemble (combination of multiple classifiers by vote) |
| Ursprungskälla≠ | Clyde, M. & Lee, H. (2001). Bagging and the Bayesian bootstrap. In T. Richardson & T. Jaakkola (Eds.), Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001). link ↗ | Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8 |
| Alias | Bayesian bootstrap aggregation, BB-ensemble, Bayesian model averaging via bootstrap, Bayesian bagged ensemble | majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | Bayesian Bagging replaces the classical bootstrap with the Bayesian bootstrap — drawing Dirichlet-distributed weights over training observations rather than sampling with replacement — and trains an ensemble of base learners under those weights. The result is a principled ensemble that approximates a Bayesian posterior over predictions, yielding calibrated uncertainty estimates alongside strong predictive accuracy. | 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. |
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