Comparar métodos

	XGBoost Explicable	Random Forest Explicable
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2016–2020	2001–2017
Autor original	Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees)	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)
Tipo	Interpretable ensemble (gradient-boosted trees + SHAP)	Interpretable ensemble (bagging + post-hoc attribution)
Fuente seminal	Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Alias	XGBoost + SHAP, interpretable XGBoost, XAI-XGBoost, transparent gradient boosting	XRF, interpretable random forest, transparent random forest, random forest with explainability
Relacionados	6	4
Resumen	Explainable XGBoost pairs the high predictive accuracy of XGBoost gradient-boosted trees with SHAP (SHapley Additive exPlanations) values to make each prediction fully auditable. The result is a model that matches or surpasses neural networks on tabular data while offering theoretically grounded, per-prediction feature attributions that satisfy both scientific transparency and regulatory demands.	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.