Sammenlign metoder

	Beslutningstræ	Gaussisk Blandingsmodel	Random Forest
Fagområde	Maskinlæring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning
Oprindelsesår	1984	1977	2001
Ophavsperson	Breiman, Friedman, Olshen & Stone	Dempster, Laird & Rubin (EM algorithm)	Breiman, L.
Type	Recursive partitioning (if-then rules)	Probabilistic (soft) clustering — mixture model	Ensemble (bagging of decision trees)
Oprindelig kilde	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Aliasser	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relaterede	5	4	4
Resumé	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.