مقایسهٔ روش‌ها

	تشریح تقویت گرادیان (Explainable Gradient Boosting)	ایکس‌جی‌بوستِ قابل‌تفسیر
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2017–2020	2016–2020
پدیدآور	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)	Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees)
نوع	Ensemble + explainability layer	Interpretable ensemble (gradient-boosted trees + SHAP)
منبع بنیادین	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.
نام‌های دیگر	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting	XGBoost + SHAP, interpretable XGBoost, XAI-XGBoost, transparent gradient boosting
مرتبط	6	6
خلاصه	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.