A comparison of random forest regression and multiple linear regression for prediction in neuroscience

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. New method: 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 gamma-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. Comparison with existing methods: In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. Conclusion: 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. (C) 2013 Elsevier B.V. All rights reserved.