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. 2017 Apr 12;7:46368. doi: 10.1038/srep46368

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Copyright © 2017, The Author(s)

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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Illustration of out-of-sample testing using (a) interior-hole -sampling and (b) exterior-chord -sampling. In each case, data is extracted from a training set based on its projection onto the 2-D PCA-space, leaving a substantial gap in image space coverage where the trained model is then tested. Despite this deficiency, performance is not necessarily hindered, as there are still successful predictions from the trained model as shown in the example (c) interior-hole and (d) exterior-chord test sets. Points in (c,d) with a higher PMR value (and therefore better prediction) are red, while a lower PMR value is blue.

Inline graphic — Illustration of out-of-sample testing using (a) interior-hole -sampling and (b) exterior-chord -sampling. In each case, data is extracted from a training set based on its projection onto the 2-D PCA-space, leaving a substantial gap in image space coverage where the trained model is then tested. Despite this deficiency, performance is not necessarily hindered, as there are still successful predictions from the trained model as shown in the example (c) interior-hole and (d) exterior-chord test sets. Points in (c,d) with a higher PMR value (and therefore better prediction) are red, while a lower PMR value is blue.