Comparar métodos

	Floresta Aleatória 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	2001–2017	1984	2016
Autor original	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)	Breiman, Friedman, Olshen & Stone	Chen, T. & Guestrin, C.
Tipo	Interpretable ensemble (bagging + post-hoc attribution)	Recursive partitioning (if-then rules)	Ensemble (gradient-boosted decision trees)
Fonte seminal	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	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	XRF, interpretable random forest, transparent random forest, random forest with explainability	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionados	4	5	5
Resumo	Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike.	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.