পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য গ্রেডিয়েন্ট বুস্টিং	গ্রেডিয়েন্ট বুস্টিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017–2020	2001
প্রবর্তক	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)	Friedman, J. H.
ধরন	Ensemble + explainability layer	Ensemble (sequential boosting of decision trees)
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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
অপর নাম	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
সম্পর্কিত	6	5
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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.