Comparar métodos

	Floresta Aleatória Explicável	Gradient Boosting
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2001–2017	2001
Autor original	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)	Friedman, J. H.
Tipo	Interpretable ensemble (bagging + post-hoc attribution)	Ensemble (sequential boosting of 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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Outros nomes	XRF, interpretable random forest, transparent random forest, random forest with explainability	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Relacionados	4	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.