手法を比較

	説明可能なXGBoost	説明可能な勾配ブースティング
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2016–2020	2017–2020
提唱者	Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees)	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)
種類	Interpretable ensemble (gradient-boosted trees + SHAP)	Ensemble + explainability layer
原典	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., 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.
別名	XGBoost + SHAP, interpretable XGBoost, XAI-XGBoost, transparent gradient boosting	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting
関連	6	6
概要	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 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.