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dc.contributor.authorCoulston, John W.en
dc.contributor.authorBlinn, Christine E.en
dc.contributor.authorThomas, Valerie A.en
dc.contributor.authorWynne, Randolph H.en
dc.date.accessioned2020-04-24T18:49:49Z
dc.date.available2020-04-24T18:49:49Z
dc.date.issued2016-03en
dc.identifier.issn0099-1112en
dc.identifier.urihttp://hdl.handle.net/10919/97899
dc.description.abstractMachine 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.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.titleApproximating Prediction Uncertainty for Random Forest Regression Modelsen
dc.typeArticle - Refereeden
dc.contributor.departmentForest Resources and Environmental Conservationen_US
dc.title.serialPhotogrammetric Engineering and Remote Sensingen
dc.identifier.doihttps://doi.org/10.14358/PERS.82.3.189en
dc.identifier.volume82en
dc.identifier.issue3en
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen
dc.description.adminPublic domain – authored by a U.S. government employeeen
dc.identifier.eissn2374-8079en


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