قارن الطرق

	شجرة القرار (Decision Tree)	نموذج الخليط الغاوسي (Gaussian Mixture Model)
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	1984	1977
صاحب الطريقة	Breiman, Friedman, Olshen & Stone	Dempster, Laird & Rubin (EM algorithm)
النوع	Recursive partitioning (if-then rules)	Probabilistic (soft) clustering — mixture model
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.
الأسماء البديلة	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians
ذات صلة	5	4
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.