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. 2017 Nov 16;34(8):1261–1269. doi: 10.1093/bioinformatics/btx727

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© The Author 2017. Published by Oxford University Press.

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

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Fig. 4. — Spline transformation outperforms piecewise linear transformation in terms of generalization accuracy, hyper-parameter robustness and training efficiency. (a–c) Test accuracy (auPR) comparing spline transformation to piecewise linear transformation for all the tasks presented in the paper (Figs 2c and d and 3c). Black represents statistically significant difference (P < 0.0001, Wilcoxon test on 200 bootstrap samples, Bonferroni correction for multiple testing). (d and e) Training and hyper-parameter tuning metrics for the branchpoint task. PL, piecewise linear. (d) Validation accuracy (auPR) of all the hyper-parameter trials. (e) Training curves (validation loss per epoch) of 10 best hyper-parameter trials (transparent lines) and their average (solid line)