Methoden vergleichen
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| Voting Ensemble× | Random Forest× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1990s–2004 | 2001 |
| Urheber≠ | Lam & Suen; Kuncheva, L. I. (systematic treatment) | Breiman, L. |
| Typ≠ | Ensemble (combination of multiple classifiers by vote) | Ensemble (bagging of decision trees) |
| Wegweisende Quelle≠ | Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8 | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Aliasnamen | majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Verwandt≠ | 5 | 4 |
| 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateDatensatz ↗ |
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