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Squares: Supporting Interactive Performance Analysis for Multiclass Classifiers.

Donghao Ren, Saleema Amershi, Bongshin Lee

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    Squares, a new visualization tool, enhances machine learning performance analysis. It helps practitioners assess multiclass classification models faster and more accurately than traditional confusion matrices.

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    Area of Science:

    • Applied Machine Learning
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Effective performance analysis is crucial for developing reliable machine learning models.
    • Existing tools like confusion matrices can obscure model behavior and disconnect performance insights from underlying data.
    • Multiclass classification tasks present unique challenges in performance evaluation.

    Purpose of the Study:

    • To introduce Squares, a novel visualization tool for multiclass classification performance analysis.
    • To enable practitioners to estimate key performance metrics while retaining instance-level data insights.
    • To facilitate faster and more accurate performance assessments in applied machine learning.

    Main Methods:

    • Development of the Squares visualization tool for multiclass classification.
    • Design of a controlled study comparing Squares against the confusion matrix.
    • Evaluation of user performance in terms of speed and accuracy of task completion.

    Main Results:

    • Practitioners using Squares demonstrated significantly faster performance assessment compared to using a confusion matrix.
    • The Squares tool led to significantly higher accuracy in performance evaluation.
    • Instance-level data distribution information was effectively conveyed by Squares, aiding in effort prioritization.

    Conclusions:

    • Squares offers a superior alternative to traditional confusion matrices for multiclass classification performance analysis.
    • The visualization effectively integrates performance metrics with data distribution, improving practitioner efficiency and accuracy.
    • This approach aids machine learning practitioners in better understanding and improving their models.