手法を比較

	説明可能な決定木	決定木	XGBoost
分野	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	1984 (CART); XAI framing formalized 2010s–2020s	1984	2016
提唱者	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.	Breiman, Friedman, Olshen & Stone	Chen, T. & Guestrin, C.
種類	Interpretable supervised learning model	Recursive partitioning (if-then rules)	Ensemble (gradient-boosted decision trees)
原典	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., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
別名	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	XGBoost, extreme gradient boosting, scalable tree boosting
関連	4	5	5
概要	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.