Compara mètodes

	XGBoost explicable	LightGBM Explicable
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2016–2020	2017
Autor original	Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees)	Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models)
Tipus	Interpretable ensemble (gradient-boosted trees + SHAP)	Gradient boosting with post-hoc explainability (SHAP)
Font 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.
Àlies	XGBoost + SHAP, interpretable XGBoost, XAI-XGBoost, transparent gradient boosting	XAI-LightGBM, LightGBM with SHAP, Interpretable LightGBM, LightGBM explainability
Relacionats	6	6
Resum	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 LightGBM combines Microsoft's LightGBM gradient boosting framework with SHAP (SHapley Additive exPlanations) to deliver both high predictive performance and rigorous, theoretically grounded feature-level explanations. It is widely adopted in applied research where predictive accuracy and interpretability are simultaneously required.