Sammenlign metoder

	Random Forest	Beslutningstræ	Logistisk regression
Fagområde	Maskinlæring	Maskinlæring	Forskningsstatistik
Familie	Machine learning	Machine learning	Process / pipeline
Oprindelsesår	2001	1984	1958
Ophavsperson	Breiman, L.	Breiman, Friedman, Olshen & Stone	David Roxbee Cox
Type	Ensemble (bagging of decision trees)	Recursive partitioning (if-then rules)	Method
Oprindelig kilde	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Aliasser	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	logit model, binomial logistic regression, LR
Relaterede	4	5	3
Resumé	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.	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.	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.