title: A comparison of random forest regression and multiple linear regression for prediction in neuroscience.
abstract: BACKGROUND
  Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention.
  
  
  METHODS
  In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)).
  
  
  RESULTS
  The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases.
  
  
  METHODS
  In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error.
  
  
  CONCLUSIONS
  In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases.
authors.affiliation: Department of Pharmacology and Toxicology, The Brain Health Research Centre, University of Otago, Dunedin, New Zealand. Electronic address: paul.smith@stonebow.otago.ac.nz.
authors.forename: Paul F
authors.initials: PF
authors.lastname: Smith
authors.forename: Siva
authors.initials: S
authors.lastname: Ganesh
authors.forename: Ping
authors.initials: P
authors.lastname: Liu
mesh_terms: D000818:Animals; D001923:Brain Chemistry; D002531:Cerebellum / Q000737:chemistry*; D016014:Linear Models; D008297:Male; D009488:Neurosciences / Q000379:methods*; D051381:Rats; D017207:Rats, Sprague-Dawley; D012044:Regression Analysis
keywords: Cerebellum; Linear regression; Random forest regression; Regression; Regression trees; Vestibular nucleus; l-Arginine metabolism