Comparar métodos

	Gradient Boosting Explicable	Random Forest Explicable
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2017–2020	2001–2017
Autor original	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)
Tipo	Ensemble + explainability layer	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, 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	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting	XRF, interpretable random forest, transparent random forest, random forest with explainability
Relacionados	6	4
Resumen	Explainable Gradient Boosting combines the predictive power of gradient boosting ensembles with structured interpretability tools — principally SHAP (SHapley Additive exPlanations) — to produce models that are both highly accurate and transparently auditable. Practitioners obtain global feature rankings and individual-level explanations alongside standard performance metrics.	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.