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    Model-Agnostic Visual Explanation of Machine Learning Models Based on Heat Map

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    Date
    2019
    Author
    Sawada, Shoko ORCID
    Toyoda, Masashi ORCID
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    Abstract
    It is essential to assess the trustworthiness of the machine learning models when deploying them to real-world applications, such as healthcare and risk management, in which domain experts need to make critical decisions. We propose a visual analysis method for supporting domain experts to understand and improve a given machine learning model based on a model-agnostic interpretable explanation technique. Our visualization method provides a heat map matrix as an overview of the model explanation and helps efficient feature engineering and data cleaning. We demonstrate our visualization method on a text classification task.
    BibTeX
    @inproceedings {10.2312:eurp.20191140,
    booktitle = {EuroVis 2019 - Posters},
    editor = {Madeiras Pereira, João and Raidou, Renata Georgia},
    title = {{Model-Agnostic Visual Explanation of Machine Learning Models Based on Heat Map}},
    author = {Sawada, Shoko and Toyoda, Masashi},
    year = {2019},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-088-8},
    DOI = {10.2312/eurp.20191140}
    }
    URI
    https://doi.org/10.2312/eurp.20191140
    https://diglib.eg.org:443/handle/10.2312/eurp20191140
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA