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. 2020 Apr 3;10:5866. doi: 10.1038/s41598-020-62821-2

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Classification performance as a result of hyperparameter tuning for accuracy, precision, recall, and F1 for Hecht, Troiano, Choi, and Hees. Each figure also shows the effect that optimizing for one metric has on the remaining metrics. For example, optimizing for recall results in a very low precision score for all four algorithms. Besides optimized scores, we also show the default classification scores obtained by using the default hyperparameter values for easy comparison; which can also be viewed in Fig. 4. Error bars indicate the 95% confidence interval.