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    Model exploration using conditional visualization

    Hurley, Catherine B. (2021) Model exploration using conditional visualization. WIREs Computational Statistics, 13 (1). ISSN 1939-5108

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    Ideally, statistical parametric model fitting is followed by various summary tables which show predictor contributions, visualizations which assess model assumptions and goodness of fit, and test statistics which compare models. In contrast, modern machine-learning fits are usually black box in nature, offer high performing predictions but suffer from an interpretability deficit. We examine how the paradigm of conditional visualization can be used to address this, specifically to explain predictor contributions, assess goodness of fit, and compare multiple, competing fits. We compare visualizations from techniques including trellis, condvis, visreg, lime, partial dependence, and ice plots. Our examples use random forest fits, but all techniques presented are model agnostic. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical Learning and Exploratory Methods of the Data Sciences > Modelling Methods

    Item Type: Article
    Additional Information: Copyright: open access Cite as:Hurley, CB. Model exploration using conditional visualization. WIREs Comput Stat. 2021; 13:e1503.
    Keywords: black box; interaction; machine learning; visualization;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 16447
    Identification Number:
    Depositing User: Dr. Catherine Hurley
    Date Deposited: 29 Aug 2022 10:33
    Journal or Publication Title: WIREs Computational Statistics
    Publisher: Wiley
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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