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New 'split-candidate scaling' parameter reveals double descent in GBDTs

By PulseAugur Editorial · [2 sources] ·

Researchers have identified a new capacity parameter for gradient boosting decision trees (GBDTs) called split-candidate scaling, which can lead to a phenomenon known as double descent. Unlike neural networks, GBDTs have lacked a clear single-axis capacity parameter. This new approach involves adjusting the number of split candidates, which refines the feature-quantization grid and expands the available paths for boosting updates. Experiments with XGBoost, LightGBM, and Catboost demonstrated that increasing split candidates can cause test error to peak and then decrease, a behavior not observed in random forests. AI

IMPACT Introduces a new theoretical framework for understanding GBDT capacity, potentially leading to improved model tuning and performance.

RANK_REASON The cluster describes a new research paper published on arXiv detailing a novel theoretical finding and empirical analysis of gradient boosting decision trees.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New 'split-candidate scaling' parameter reveals double descent in GBDTs

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The cluster describes a new research paper published on arXiv detailing a novel theoretical finding and empirical analysis of gradient boosting decision trees.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ryuichi Kanoh ·

    Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling

    arXiv:2608.03111v1 Announce Type: new Abstract: Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width. For gradient boosting decision trees (GBDTs), however, an analogous single-axis capacity parameter has not been established.…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling

    Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width. For gradient boosting decision trees (GBDTs), however, an analogous single-axis capacity parameter has not been established. We propose the number of split candidates as an…

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