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| Robuster Zufallswald× | Gradient Boosting× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2000s–2010s | 2001 |
| Urheber≠ | Various (extensions of Breiman 2001 Random Forest) | Friedman, J. H. |
| Typ≠ | Robust Ensemble (noise-tolerant bagging of decision trees) | Ensemble (sequential boosting 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 ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Aliasnamen | RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Verwandt≠ | 6 | 5 |
| 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. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. |
| ScholarGateDatensatz ↗ |
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