Vertaile menetelmiä
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| Selitettävä Extra Trees× | XGBoost× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2006 (Extra Trees); 2017 (SHAP integration) | 2016 |
| Kehittäjä≠ | Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer) | Chen, T. & Guestrin, C. |
| Tyyppi≠ | Ensemble (randomized trees) with post-hoc explainability | Ensemble (gradient-boosted decision trees) |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet≠ | XAI-ET, Explainable ET, Interpretable Extra Trees, Extra Trees with SHAP | XGBoost, extreme gradient boosting, scalable tree boosting |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
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