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Visual Diagnosis of Tree Boosting Methods.

Shixia Liu, Jiannan Xiao, Junlin Liu

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    Developing high-performance tree boosting models is time-consuming. Our new tool, BOOSTVis, uses visual diagnosis to help experts quickly analyze and improve tree boosting training, saving significant time and effort.

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    Area of Science:

    • Machine Learning
    • Data Visualization
    • Computer Science

    Background:

    • Tree boosting is a powerful machine learning technique but model development is often inefficient.
    • Numerous trial-and-error experiments are typically required for high-performance tree boosting models.
    • Existing methods lack efficient diagnostic tools for the tree boosting training process.

    Purpose of the Study:

    • To develop a visual diagnosis tool, BOOSTVis, for analyzing and diagnosing tree boosting model training.
    • To accelerate the development of high-performance tree boosting models.
    • To provide experts with better insights into the training dynamics.

    Main Methods:

    • Development of BOOSTVis, a visual diagnosis tool for tree boosting.
    • Integration of a temporal confusion matrix visualization.
    • Combination with t-SNE projection and tree visualization for comprehensive analysis.

    Main Results:

    • BOOSTVis enables quick analysis and diagnosis of tree boosting training.
    • The tool provides a comprehensive overview of model performance.
    • Case studies on the Otto Group Product Classification dataset show informative feedback and guidance.

    Conclusions:

    • BOOSTVis effectively aids experts in understanding and diagnosing tree boosting algorithms.
    • The visual components facilitate identification of issues in the training process.
    • The tool streamlines the development of effective tree boosting models.