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| Erklärbarer Zufallswald× | XGBoost× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2001–2017 | 2016 |
| Urheber≠ | Breiman, L. (RF); Lundberg & Lee (SHAP attribution) | Chen, T. & Guestrin, C. |
| Typ≠ | Interpretable ensemble (bagging + post-hoc attribution) | Ensemble (gradient-boosted decision trees) |
| Wegweisende Quelle≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Aliasnamen≠ | XRF, interpretable random forest, transparent random forest, random forest with explainability | XGBoost, extreme gradient boosting, scalable tree boosting |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateDatensatz ↗ |
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