Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Balsošanas ansamblis× | Bagging (Bootstrap Aggregating)× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1990s–2004 | 1996 |
| Autors≠ | Lam & Suen; Kuncheva, L. I. (systematic treatment) | Breiman, L. |
| Tips≠ | Ensemble (combination of multiple classifiers by vote) | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) |
| Pirmavots≠ | Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8 | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ |
| Citi nosaukumi≠ | majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | 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. |
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