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. 2015 Dec 1;3(4):230–237. doi: 10.1089/big.2015.0019

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© Helena M.M. Patching et al. 2016; Published by Mary Ann Liebert, Inc.

This Open Access article is distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

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FIG. 3. — The four heatmaps show (A) the root mean square error (RMSE), (B) the coefficient of determination (R²), (C) the number of points without prediction, and (D) the number of layers in the resulting casade, when the number of predictors in each layer (m) and the data subset size (p) are varied. Our requirements for the combination of m and p were a low test RMSE, high R², with reasonable number of points without prediction that go to the experts, and fewer layers.