방법 비교

	설명 가능한 스태킹 앙상블	랜덤 포레스트	XGBoost
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	1992 (stacking); 2010s–2020s (explainable extensions)	2001	2016
창시자	Wolpert, D. H. (stacking); XAI integration developed across the community	Breiman, L.	Chen, T. & Guestrin, C.
유형	Ensemble meta-learning with post-hoc or intrinsic interpretability	Ensemble (bagging of decision trees)	Ensemble (gradient-boosted decision trees)
원전	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
별칭	XAI-Stacking, interpretable stacking, transparent stacking ensemble, explainable stacked generalisation	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
관련	4	4	5
요약	Explainable Stacking Ensemble combines the predictive power of stacked generalisation — training a meta-learner on the outputs of multiple diverse base models — with interpretability tools such as SHAP or LIME that reveal how each base model and each input feature contributed to the final prediction. It bridges the accuracy–transparency trade-off that makes pure stacking opaque in high-stakes settings.	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.	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.