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    Understanding Machine Learning (ML) system performance is crucial. The What-If Tool offers an open-source solution for practitioners to probe, visualize, and analyze ML models with minimal coding, enhancing model interpretability and fairness.

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    Area of Science:

    • Computer Science
    • Machine Learning Engineering

    Background:

    • Developing and deploying Machine Learning (ML) systems presents a significant challenge in understanding their performance across diverse input ranges.
    • Effective evaluation and debugging are critical for reliable ML system deployment.

    Purpose of the Study:

    • To introduce the What-If Tool, an open-source application designed to facilitate the analysis and visualization of ML systems.
    • To enable practitioners to probe ML model performance, analyze feature importance, and assess fairness with minimal coding.

    Main Methods:

    • Development of an open-source application, the What-If Tool.
    • Features include interactive visualization, hypothetical scenario testing, and fairness metric measurement.
    • Designed for practitioners with minimal coding requirements.

    Main Results:

    • The What-If Tool allows users to test model performance under various conditions.
    • It facilitates the analysis of feature importance and visualization of model behavior.
    • The tool supports the measurement of ML systems using multiple fairness metrics.

    Conclusions:

    • The What-If Tool provides a practical solution for understanding and improving ML system performance.
    • Its design enables in-depth analysis and visualization, aiding in the development of more robust and fair ML models.
    • Real-life usage demonstrates its value across different organizations.