পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য গ্রেডিয়েন্ট বুস্টিং	ব্যাখ্যাযোগ্য এক্সজিবিওস্ট
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017–2020	2016–2020
প্রবর্তক	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)	Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees)
ধরন	Ensemble + explainability layer	Interpretable ensemble (gradient-boosted trees + SHAP)
মৌলিক উৎস	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.
অপর নাম	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting	XGBoost + SHAP, interpretable XGBoost, XAI-XGBoost, transparent gradient boosting
সম্পর্কিত	6	6
সারসংক্ষেপ	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.