Comparar métodos

	Árvore de Decisão Explicável	Árvore de Decisão	XGBoost
Área	Aprendizado de máquina	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning	Machine learning
Ano de origem	1984 (CART); XAI framing formalized 2010s–2020s	1984	2016
Autor original	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.	Breiman, Friedman, Olshen & Stone	Chen, T. & Guestrin, C.
Tipo	Interpretable supervised learning model	Recursive partitioning (if-then rules)	Ensemble (gradient-boosted decision trees)
Fonte seminal	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.
Outros nomes	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
Relacionados	4	5	5
Resumo	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.