Methoden vergelijken

	Explainable Gradient Boosting	Explainable XGBoost
Vakgebied	Machine learning	Machine learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2017–2020	2016–2020
Grondlegger	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)	Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees)
Type	Ensemble + explainability layer	Interpretable ensemble (gradient-boosted trees + SHAP)
Oorspronkelijke bron	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, 56–67.	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.
Aliassen	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting	XGBoost + SHAP, interpretable XGBoost, XAI-XGBoost, transparent gradient boosting
Verwant	6	6
Samenvatting	Explainable Gradient Boosting combines the predictive power of gradient boosting ensembles with structured interpretability tools — principally SHAP (SHapley Additive exPlanations) — to produce models that are both highly accurate and transparently auditable. Practitioners obtain global feature rankings and individual-level explanations alongside standard performance metrics.	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.