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| Robuster Zufallswald× | Random Forest× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2000s–2010s | 2001 |
| Urheber≠ | Various (extensions of Breiman 2001 Random Forest) | Breiman, L. |
| Typ≠ | Robust Ensemble (noise-tolerant bagging of decision trees) | Ensemble (bagging of decision trees) |
| Wegweisende Quelle≠ | Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18. link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Aliasnamen | RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Verwandt≠ | 6 | 4 |
| Zusammenfassung≠ | Robust Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect. | 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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