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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • Gradient-boosted decision trees (GBDTs) are a popular machine learning technique.
    • Current GBDT models typically handle only a single output variable.
    • Existing multi-output strategies ignore inter-variable correlations, leading to inefficient tree structures.

    Purpose of the Study:

    • To introduce GBDT-MO, a novel method for learning gradient-boosted decision trees with multiple outputs.
    • To address the redundancy and inefficiency of current multi-output GBDT approaches.
    • To enhance the performance of GBDTs in scenarios with multiple correlated output variables.

    Main Methods:

    • Developed GBDT-MO, where each leaf predicts all or a subset of variables by summing objective gains across outputs.
    • Extended histogram approximation to the multiple-output scenario for accelerated training.
    • Evaluated GBDT-MO on diverse synthetic and real-world datasets.

    Main Results:

    • GBDT-MO demonstrates superior accuracy compared to existing methods.
    • The proposed method significantly improves both training and inference speeds.
    • Learned tree structures are more efficient due to the consideration of variable correlations.

    Conclusions:

    • GBDT-MO offers a general and effective solution for multi-output gradient-boosted decision trees.
    • The method enhances computational efficiency and predictive performance.
    • GBDT-MO represents a significant advancement for machine learning tasks with multiple output variables.