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| Erklärbares XGBoost× | XGBoost× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2016–2020 | 2016 |
| Urheber≠ | Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees) | Chen, T. & Guestrin, C. |
| Typ≠ | Interpretable ensemble (gradient-boosted trees + SHAP) | Ensemble (gradient-boosted decision trees) |
| Wegweisende Quelle≠ | Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Aliasnamen≠ | XGBoost + SHAP, interpretable XGBoost, XAI-XGBoost, transparent gradient boosting | XGBoost, extreme gradient boosting, scalable tree boosting |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Explainable XGBoost pairs the high predictive accuracy of XGBoost gradient-boosted trees with SHAP (SHapley Additive exPlanations) values to make each prediction fully auditable. The result is a model that matches or surpasses neural networks on tabular data while offering theoretically grounded, per-prediction feature attributions that satisfy both scientific transparency and regulatory demands. | 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. |
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