השוואת שיטות

	הסברתיות של חיזוק גרדיאנט (Explainable Gradient Boosting)	XGBoost
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2017–2020	2016
הוגה השיטה	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)	Chen, T. & Guestrin, C.
סוג	Ensemble + explainability layer	Ensemble (gradient-boosted decision trees)
מקור מכונן	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
כינויים	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting	XGBoost, extreme gradient boosting, scalable tree boosting
קשורות	6	5
תקציר	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.