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    This survey systematically reviews visualization for machine learning (VIS4ML) works, focusing on data types and data-centric tasks. It categorizes data, outlines ML tasks, and analyzes trends to guide future research in VIS4ML.

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    Area of Science:

    • Computer Science
    • Data Science
    • Machine Learning

    Background:

    • The field of visualization for machine learning (VIS4ML) has rapidly expanded over the past decade.
    • Understanding and interpreting machine learning (ML) models is crucial for their effective deployment.
    • Data quality significantly influences ML model performance, necessitating a data-centric approach to VIS4ML.

    Purpose of the Study:

    • To systematically review and organize the growing body of VIS4ML research.
    • To provide a data-centric perspective on VIS4ML, summarizing works based on data types and associated ML tasks.
    • To identify current trends and future research directions in VIS4ML.

    Main Methods:

    • Categorization of common data types handled by ML models (five types) with explanations of their unique features.
    • Identification and summarization of six data-centric tasks within the ML pipeline that utilize visualization for model interpretation, diagnosis, and refinement.
    • Analysis of 143 surveyed papers based on their focus across data types, data-centric tasks, and their intersections.

    Main Results:

    • A structured overview of VIS4ML works, categorized by five distinct data types and their suitability for different ML models.
    • Identification of six key data-centric tasks where visualization aids in understanding, diagnosing, and refining ML models.
    • Analysis of the distribution of research across data types and tasks, revealing current research hotspots and gaps.

    Conclusions:

    • The study provides a comprehensive framework for understanding VIS4ML through a data-centric lens.
    • The findings highlight the importance of data characteristics and data-centric tasks in the development of effective visualization tools for ML.
    • The analysis offers insights into prospective research directions, guiding future innovation in the VIS4ML field.