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Interpretable machine learning learns complex interactions of urban features to understand socio‐economic inequality

Author(s): (Zachry Department of Civil and Environmental Engineering Texas A&M University College Station Texas USA)
(Department of Computer Science and Engineering Texas A&M University College Station Texas USA)
(Department of Computer Science and Engineering Texas A&M University College Station Texas USA)
(Zachry Department of Civil and Environmental Engineering Texas A&M University College Station Texas USA)
Medium: journal article
Language(s): English
Published in: Computer-Aided Civil and Infrastructure Engineering, , n. 14, v. 38
Page(s): 2013-2029
DOI: 10.1111/mice.12972
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1111/mice.12972.
  • About this
    data sheet
  • Reference-ID
    10708786
  • Published on:
    21/03/2023
  • Last updated on:
    02/09/2023
 
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