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. 2022 Mar 23;13:1553. doi: 10.1038/s41467-022-29283-8

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© The Author(s) 2022

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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Fig. 4 — a Machine learning were trained by 712 samples from marine habitats and used to predict the antibiotic resistance threats in global marine habitats. b Accuracy rate of machine learning with different discretization methods. K-means exhibited higher accuracy rate than equal frequency and the best model (accuracy rate = 76.06%) were chosen for the further prediction. c The ROC plots confirmed the high performance of the best model in classification of risk ranks. d Latitude as well as the climate change stressors exhibited the high importance in predicting the antibiotic resistance risk. The full name of each indicator can be found in Supplementary Data 12. e The map of ARG risk in marine habitats with prediction results by machine learning, which was drawn by ArcGIS in 20′ × 20′ resolution.