Python functions -- cross-validation methods from a data-driven perspective
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bin, txt, zipUpdated Jul 24, 2024
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EmailClick to copy linkLink copiedCiteYanwen Wang; Yanwen Wang (2024). Python functions -- cross-validation methods from a data-driven perspective [Dataset]. http://doi.org/10.5281/zenodo.12804224bin, zip, txtAvailable download formatsUnique identifierhttps://doi.org/10.5281/zenodo.12804224Dataset updatedJul 24, 2024AuthorsYanwen Wang; Yanwen WangLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyTime period coveredJun 28, 2024DescriptionThis is the organized python functions of proposed methods in Yanwen Wang PhD research. Researchers can directly use these functions to conduct spatial+ cross-validation (SP-CV), adversarial-validation-based dissimilarity quantification method (AVD), and dissimilarity-adaptive cross-validation (DA-CV). The description of how to run codes is in Readme.txt. The descriptions of functions are in functions.docx.
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FacebookTwitterEmailAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This is the organized python functions of proposed methods in Yanwen Wang PhD research. Researchers can directly use these functions to conduct spatial+ cross-validation (SP-CV), adversarial-validation-based dissimilarity quantification method (AVD), and dissimilarity-adaptive cross-validation (DA-CV). The description of how to run codes is in Readme.txt. The descriptions of functions are in functions.docx.