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| Wyjaśnialne drzewo decyzyjne× | XGBoost× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1984 (CART); XAI framing formalized 2010s–2020s | 2016 |
| Twórca≠ | Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J. | Chen, T. & Guestrin, C. |
| Typ≠ | Interpretable supervised learning model | Ensemble (gradient-boosted decision trees) |
| Źródło pierwotne≠ | Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8 | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Inne nazwy≠ | XDT, interpretable decision tree, rule-based decision tree, transparent decision tree | XGBoost, extreme gradient boosting, scalable tree boosting |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes. | 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. |
| ScholarGateZbiór danych ↗ |
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