방법 비교

	SHAP (SHapley Additive exPlanations)	결정 트리
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2017	1984
창시자	Lundberg, S.M. & Lee, S.-I.	Breiman, Friedman, Olshen & Stone
유형	Model-explanation method (Shapley-value attribution)	Recursive partitioning (if-then rules)
원전	Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
별칭	SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
관련	5	5
요약	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).	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.