Bandingkan metode

	Peningkatkan Gradien yang Dapat Dijelaskan	Peningkatan Gradien
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2017–2020	2001
Pencetus	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)	Friedman, J. H.
Tipe	Ensemble + explainability layer	Ensemble (sequential boosting of decision trees)
Sumber perintis	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.
Alias	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Terkait	6	5
Ringkasan	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.