قارن الطرق

	شجرة القرار (Decision Tree)	نموذج الخليط الغاوسي (Gaussian Mixture Model)	الانحدار اللوجستي
المجال	تعلم الآلة	تعلم الآلة	إحصاء البحث
العائلة	Machine learning	Machine learning	Process / pipeline
سنة النشأة	1984	1977	1958
صاحب الطريقة	Breiman, Friedman, Olshen & Stone	Dempster, Laird & Rubin (EM algorithm)	David Roxbee Cox
النوع	Recursive partitioning (if-then rules)	Probabilistic (soft) clustering — mixture model	Method
المصدر التأسيسي	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	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.
الأسماء البديلة	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians	logit model, binomial logistic regression, LR
ذات صلة	5	4	3
الملخص	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.	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.