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	एक्सप्लेनेबल एक्सजीबूस्ट (Explainable XGBoost)	रैंडम फ़ॉरेस्ट
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2016–2020	2001
प्रवर्तक	Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees)	Breiman, L.
प्रकार	Interpretable ensemble (gradient-boosted trees + SHAP)	Ensemble (bagging of decision trees)
मौलिक स्रोत	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
उपनाम	XGBoost + SHAP, interpretable XGBoost, XAI-XGBoost, transparent gradient boosting	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
संबंधित	6	4
सारांश	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.