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New qshap tool decomposes R-squared for gradient-boosted trees

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced qshap, a new method for decomposing R-squared values in gradient-boosted decision trees. This tool, available in R and Python, quantifies the contribution of individual features to overall model performance by analyzing the quadratic loss of observations. qshap supports popular GBDT implementations like XGBoost, LightGBM, and Catboost, utilizing efficient C++ backends and offering specialized acceleration for oblivious trees. AI

IMPACT Provides a new method for understanding feature importance in gradient-boosted trees, potentially improving model interpretability and debugging.

RANK_REASON The cluster describes a new method and tool for analyzing machine learning models, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New qshap tool decomposes R-squared for gradient-boosted trees

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43 / 100
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Tool
The cluster describes a new method and tool for analyzing machine learning models, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, model release
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High
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Zhongli Jiang, Min Zhang, Dabao Zhang ·

    qshap: Fast Shapley Decomposition of $R^2$ for Gradient-Boosted Trees

    arXiv:2608.24104v1 Announce Type: new Abstract: Numerous methods have been developed to quantify feature attributions in individual predictions for tree ensembles. However, many applications require global measures of feature contributions to overall model performance. Although l…

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