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BEAMES: Interactive Multimodel Steering, Selection, and Inspection for Regression Tasks.

Subhajit Das, Dylan Cashman, Remco Chang

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    Summary
    This summary is machine-generated.

    This study introduces multimodel steering, enabling users to inspect and steer multiple machine learning models. This approach enhances model selection for diverse tasks and datasets, improving tailored machine learning model development.

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    Area of Science:

    • Machine Learning
    • Human-Computer Interaction
    • Visual Analytics

    Background:

    • Interactive model steering allows users to tailor machine learning models to specific domains and tasks.
    • Current tools typically support steering only a single model, which may be suboptimal.
    • The choice of machine learning model is critical for effective analysis.

    Purpose of the Study:

    • To present a novel technique for inspecting and steering multiple machine learning models.
    • To enable users to explore a broader set of learning algorithms and model types.
    • To facilitate regression tasks through a visual analytic prototype supporting multimodel steering.

    Main Methods:

    • Developed a technique for users to inspect and steer multiple machine learning models.
    • Integrated this technique into a visual analytic prototype named BEAMES.
    • Enabled users to perform regression tasks using multimodel steering within BEAMES.

    Main Results:

    • Demonstrated the effectiveness of the BEAMES prototype through a specific use case.
    • Showcased the capability of steering and sampling from a diverse set of models.
    • Validated the potential for improved model selection and tailoring.

    Conclusions:

    • Multimodel steering offers a more flexible and effective approach to machine learning model development.
    • The BEAMES prototype provides a practical implementation for multimodel steering in regression tasks.
    • This technique has broader implications for interactive visual analytics and machine learning.