مقایسهٔ روش‌ها

	SHAP (SHapley Additive exPlanations)	مدل آمیخته گوسی	رگرسیون لجستیک
حوزه	یادگیری ماشین	یادگیری ماشین	آمار پژوهش
خانواده	Machine learning	Machine learning	Process / pipeline
سال پیدایش	2017	1977	1958
پدیدآور	Lundberg, S.M. & Lee, S.-I.	Dempster, Laird & Rubin (EM algorithm)	David Roxbee Cox
نوع	Model-explanation method (Shapley-value attribution)	Probabilistic (soft) clustering — mixture model	Method
منبع بنیادین	Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777.	Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
نام‌های دیگر	SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians	logit model, binomial logistic regression, LR
مرتبط	5	4	3
خلاصه	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 Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.