Comparar métodos

	SHAP (SHapley Additive exPlanations)	XGBoost
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2017	2016
Autor original	Lundberg, S.M. & Lee, S.-I.	Chen, T. & Guestrin, C.
Tipo	Model-explanation method (Shapley-value attribution)	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, 4766–4777.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Outros nomes	SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionados	5	5
Resumo	SHAP is a model-explanation method, introduced by Scott Lundberg and Su-In Lee in 2017, that uses Shapley values from cooperative game theory to measure how much each feature contributes to an individual prediction, making the output of black-box machine-learning models interpretable. It supports both global explanations (overall feature importance) and local explanations (why one specific prediction came out the way it did).	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.