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| SHAP (SHapley Additive exPlanations)× | XGBoost× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2017 | 2016 |
| Twórca≠ | Lundberg, S.M. & Lee, S.-I. | Chen, T. & Guestrin, C. |
| Typ≠ | Model-explanation method (Shapley-value attribution) | Ensemble (gradient-boosted decision trees) |
| Źródło pierwotne≠ | Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Inne nazwy≠ | SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability | XGBoost, extreme gradient boosting, scalable tree boosting |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | SHAP is a model-explanation method, introduced by Scott Lundberg and Su-In Lee in 2017, that uses Shapley values from cooperative game theory to measure how much each feature contributes to an individual prediction, making the output of black-box machine-learning models interpretable. It supports both global explanations (overall feature importance) and local explanations (why one specific prediction came out the way it did). | 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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