Methoden vergleichen
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| Voting Ensemble× | Extra Trees× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1990s–2004 | 2006 |
| Urheber≠ | Lam & Suen; Kuncheva, L. I. (systematic treatment) | Geurts, P.; Ernst, D.; Wehenkel, L. |
| Typ≠ | Ensemble (combination of multiple classifiers by vote) | Ensemble (extremely randomized decision trees) |
| Wegweisende Quelle≠ | Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8 | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ |
| Aliasnamen | majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET |
| 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. | Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time. |
| ScholarGateDatensatz ↗ |
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