This paper presents results of assessing machine learning algorithms-neural networks (nnet package), random forest (ranger), gradient boosting (gbm), K-nearest neighborhood (class) and Cubist-for operational mapping of PNV. PNV is useful for raising public awareness about land degradation and for estimating land potential. Potential natural vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities. Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential. Cite this article Hengl T, Walsh MG, Sanderman J, Wheeler I, Harrison SP, Prentice IC. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. 6 Department of Life Sciences and Grantham Institute-Climate Change and the Environment, Imperial College London, London, UK DOI 10.7717/peerj.5457 Published Accepted Received Academic Editor Le Yu Subject Areas Biogeography, Computational Biology, Plant Science, Data Mining and Machine Learning, Spatial and Geographic Information Science Keywords Natural vegetation, Random forest, Global, Biome, Machine learning, Forest species, FAPAR, Land potential Copyright © 2018 Hengl et al.

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