Making the black box more transparent: Understanding the physical implications of machine learning

Amy McGovern; Ryan Lagerquist; David John Gagne; G. Eli Jergensen; Kimberly L. Elmore; Cameron R. Homeyer; Travis Smith

Journal ArticleOPEN ACCESS

Making the black box more transparent: Understanding the physical implications of machine learning

Bulletin of the American Meteorological Society (2019) 100(11) 2175-2199

DOI: 10.1175/BAMS-D-18-0195.1

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Abstract

Machine learning model interpretation and visualization focusing on meteorological domains are introduced and analyzed.

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CITATION STYLE

APA

McGovern, A., Lagerquist, R., Gagne, D. J., Jergensen, G. E., Elmore, K. L., Homeyer, C. R., & Smith, T. (2019). Making the black box more transparent: Understanding the physical implications of machine learning. Bulletin of the American Meteorological Society, 100(11), 2175–2199. https://doi.org/10.1175/BAMS-D-18-0195.1

Making the black box more transparent: Understanding the physical implications of machine learning

Abstract

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