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	Random Forest	Logistisk regression	Support Vector Machine (Klassificering)
Ämnesområde	Maskininlärning	Forskningsstatistik	Maskininlärning
Familj	Machine learning	Process / pipeline	Machine learning
Ursprungsår	2001	1958	1995
Upphovsperson	Breiman, L.	David Roxbee Cox	Cortes, C. & Vapnik, V.
Typ	Ensemble (bagging of decision trees)	Method	Maximum-margin classifier (kernel method)
Ursprungskälla	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Alias	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	logit model, binomial logistic regression, LR	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Närliggande	4	3	5
Sammanfattning	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.	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.