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Approximating prediction uncertainty for random forest regression models

Formally Refereed
Authors: John W. Coulston, Christine E. Blinn, Valerie A. Thomas, Randolph H. Wynne
Year: 2016
Type: Scientific Journal
Station: Southern Research Station
Source: Photogrammetric Engineering & Remote Sensing


Machine 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.


Coulston, 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