Visual techniques to compare predictive models

Paolo Buono, Enrico Bertini, Alessandra Legretto, Maria F. Costabile

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Predictive analysis is an important part of data analysis. Predictive models, based on Statistics or Machine Learning, are increasingly used to estimate, with a certain probability, future values of the variables that describe a phenomenon. Different models produce different results on a same dataset; thus, several models should be compared in order to identify the most suitable one. The paper is part of a larger research that aims at providing interactive visualizations that help the analysts to compare predictive models and to select the model that best fits the data. Specifically, two visualizations are presented, which support the analysts in performing some tasks of the Keim's Visual Analytics Mantra.

    Original languageEnglish (US)
    Title of host publicationCHItaly 2019 - Proceedings of the 13th Biannual Conference of the Italian SIGCHI Chapter Designing the Next Interaction
    PublisherAssociation for Computing Machinery
    ISBN (Electronic)9781450371902
    DOIs
    StatePublished - Sep 23 2019
    Event13th Biannual Conference of the Italian SIGCHI Chapter Designing the Next Interaction, CHItaly 2019 - Padua, Italy
    Duration: Sep 23 2019Sep 25 2019

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference13th Biannual Conference of the Italian SIGCHI Chapter Designing the Next Interaction, CHItaly 2019
    CountryItaly
    CityPadua
    Period9/23/199/25/19

    Keywords

    • Comparison Matrix
    • Pie-chart Matrix
    • Visual Analytics

    ASJC Scopus subject areas

    • Software
    • Human-Computer Interaction
    • Computer Vision and Pattern Recognition
    • Computer Networks and Communications

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