Editing Random forest (section)

== Variants ==
Instead of decision trees, linear models have been proposed and evaluated as base estimators in random forests, in particular [[multinomial logistic regression]] and [[naive Bayes classifier]]s.<ref name=":0">{{Cite journal |last1=Piryonesi |first1=S. Madeh |last2=El-Diraby |first2=Tamer E. |date=2021-02-01 |title=Using Machine Learning to Examine Impact of Type of Performance Indicator on Flexible Pavement Deterioration Modeling |url=http://ascelibrary.org/doi/10.1061/%28ASCE%29IS.1943-555X.0000602 |journal=Journal of Infrastructure Systems |language=en |volume=27 |issue=2 |page=04021005 |doi=10.1061/(ASCE)IS.1943-555X.0000602 |issn=1076-0342 |s2cid=233550030}}</ref><ref>{{cite journal |author=Prinzie, A. |author2=Van den Poel, D. |year=2008 |title=Random Forests for multiclass classification: Random MultiNomial Logit |journal=Expert Systems with Applications |volume=34 |issue=3 |pages=1721–1732 |doi=10.1016/j.eswa.2007.01.029}}</ref><ref>{{Cite conference | doi = 10.1007/978-3-540-74469-6_35 | contribution=Random Multiclass Classification: Generalizing Random Forests to Random MNL and Random NB|title=Database and Expert Systems Applications: 18th International Conference, DEXA 2007, Regensburg, Germany, September 3-7, 2007, Proceedings |editor1=Roland Wagner |editor2=Norman Revell |editor3=Günther Pernul| year=2007 | series=Lecture Notes in Computer Science | volume=4653 | pages=349–358 | last1 = Prinzie | first1 = Anita| isbn=978-3-540-74467-2 }}</ref> In cases that the relationship between the predictors and the target variable is linear, the base learners may have an equally high accuracy as the ensemble learner.<ref name=":1">{{Cite journal|last1=Smith|first1=Paul F.|last2=Ganesh|first2=Siva|last3=Liu|first3=Ping|date=2013-10-01|title=A comparison of random forest regression and multiple linear regression for prediction in neuroscience|url=https://linkinghub.elsevier.com/retrieve/pii/S0165027013003026|journal=Journal of Neuroscience Methods|language=en|volume=220|issue=1|pages=85–91|doi=10.1016/j.jneumeth.2013.08.024|pmid=24012917|s2cid=13195700}}</ref><ref name=":0" />