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. 2024 Mar 4;14:5294. doi: 10.1038/s41598-024-52941-4

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© The Author(s) 2024

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Left panel: the effect of learning rate on achieving high precision with BDTs. The learning rate is not an important parameter for low dimensions but is significant for higher dimensions. Right panel: the data volume required to train the different regressors. For lower dimensions small volumes of data ( $< 1$ M) are sufficient. However, for higher dimensions, a lot more data is necessary.