Skip to Main Content
Approximating prediction uncertainty for random forest regression modelsAuthor(s): John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne
Source: Photogrammetric Engineering & Remote Sensing
Publication Series: Scientific Journal (JRNL)
Station: Southern Research Station
Download Publication (5.0 MB)
DescriptionMachine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as inputs to other modeling applications such as fire modeling. Here we use a Monte Carlo approach to quantify prediction uncertainty for random forest regression models. We test the approach by simulating maps of dependent and independent variables with known characteristics and comparing actual errors with prediction errors. Our approach produced conservative prediction intervals across most of the range of predicted values. However, because the Monte Carlo approach was data driven, prediction intervals were either too wide or too narrow in sparse parts of the prediction distribution. Overall, our approach provides reasonable estimates of prediction uncertainty for random forest regression models.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationCoulston, John W.; Blinn, Christine E.; Thomas, Valerie A.; Wynne, Randolph H. 2016. Approximating prediction uncertainty for random forest regression models. Photogrammetric Engineering & Remote Sensing, Vol. 82(3): 189-197. 9 p. 10.14358/PERS.82.3.189
- Probabilistic accounting of uncertainty in forecasts of species distributions under climate change
- Statistical uncertainty of eddy flux-based estimates of gross ecosystem carbon exchange at Howland Forest, Maine
- A framework for simulating map error in ecosystem models
XML: View XML