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. 2019 Dec 27;9:20038. doi: 10.1038/s41598-019-56527-3

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© The Author(s) 2019

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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Assessment of Gradient Boosting Machine-based models’ performance. The subplot (a) demonstrates the ROC curves and the subplot (b) precision-recall curves. Black dashed lines indicate the performance of a random classifier in case of AUC, and performance of the prediction model based on the dataset labels distribution. The subplots’ legends reflect the benchmarked models and the values of corresponding metrics with 95% confidence intervals. Here, Area under the ROC curve metric is used in subplot (a) and Average Precision in subplot (b).