Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Vysvětlitelné extrémní náhodné stromy× | XGBoost× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2006 (Extra Trees); 2017 (SHAP integration) | 2016 |
| Tvůrce≠ | Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer) | Chen, T. & Guestrin, C. |
| Typ≠ | Ensemble (randomized trees) with post-hoc explainability | Ensemble (gradient-boosted decision trees) |
| Původní zdroj≠ | Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Další názvy≠ | XAI-ET, Explainable ET, Interpretable Extra Trees, Extra Trees with SHAP | XGBoost, extreme gradient boosting, scalable tree boosting |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | Explainable Extra Trees combines the Extremely Randomized Trees (Extra Trees) ensemble algorithm with post-hoc explainability methods — most commonly SHAP values — to deliver both strong predictive performance and transparent, feature-level explanations. It extends the classic Extra Trees classifier or regressor so that every prediction can be decomposed into individual feature contributions, satisfying demands for accountability in applied and regulated domains. | 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. |
| ScholarGateDatová sada ↗ |
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