Methoden vergleichen
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| Voting Ensemble× | Stacking× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1990s–2004 | 1992 |
| Urheber≠ | Lam & Suen; Kuncheva, L. I. (systematic treatment) | Wolpert, D.H. |
| Typ≠ | Ensemble (combination of multiple classifiers by vote) | Ensemble (heterogeneous meta-learning) |
| Wegweisende Quelle≠ | Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8 | Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259. DOI ↗ |
| Aliasnamen≠ | majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble | Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions. |
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