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Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models

Journal of computational and graphical statistics, 2022-07, Vol.31 (3), p.766-778 [Peer Reviewed Journal]

2022 The Author(s). Published with license by Taylor & Francis Group, LLC. 2022 ;ISSN: 1061-8600 ;EISSN: 1537-2715 ;DOI: 10.1080/10618600.2021.2007935

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  • Title:
    Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models
  • Author: Inglis, Alan ; Parnell, Andrew ; Hurley, Catherine B.
  • Subjects: Black-box ; Model explanation ; Model visualization
  • Is Part Of: Journal of computational and graphical statistics, 2022-07, Vol.31 (3), p.766-778
  • Description: Variable importance, interaction measures, and partial dependence plots are important summaries in the interpretation of statistical and machine learning models. In this article, we describe new visualization techniques for exploring these model summaries. We construct heatmap and graph-based displays showing variable importance and interaction jointly, which are carefully designed to highlight important aspects of the fit. We describe a new matrix-type layout showing all single and bivariate partial dependence plots, and an alternative layout based on graph Eulerians focusing on key subsets. Our new visualizations are model-agnostic and are applicable to regression and classification supervised learning settings. They enhance interpretation even in situations where the number of variables is large. Our R package vivid (variable importance and variable interaction displays) provides an implementation. Supplementary files for this article are available online.
  • Publisher: Taylor & Francis
  • Language: English
  • Identifier: ISSN: 1061-8600
    EISSN: 1537-2715
    DOI: 10.1080/10618600.2021.2007935
  • Source: Taylor & Francis Open Access Journals
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