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. 2015 Oct 5;3:47. doi: 10.1186/s40168-015-0114-5

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© Ning and Beiko. 2015

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 4 — Classification accuracy with different sets of input features. The classification accuracy is shown for sets of 10 to 200 of the top-ranked features according to the information gain (a), chi-square (b), and RF feature permutation (c) criteria. The four types of input features used were OTUs only (orange markers), OTUs and clades (green markers), functional predictions made using PICRUSt (purple markers), and all generated features (blue markers)