방법 비교

	설명 가능한 결정 트리	랜덤 포레스트	XGBoost
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	1984 (CART); XAI framing formalized 2010s–2020s	2001	2016
창시자	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.	Breiman, L.	Chen, T. & Guestrin, C.
유형	Interpretable supervised learning model	Ensemble (bagging of decision trees)	Ensemble (gradient-boosted decision trees)
원전	Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8	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	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
관련	4	4	5
요약	An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes.	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.