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ScoreStop offers gradient-based early stopping for decision trees

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ScoreStop, a novel gradient-based early stopping method for gradient boosted decision trees. This new technique frames the stopping decision as a statistical test for overfitting, using functional score tests on validation data. ScoreStop offers a scale-invariant statistic with a known asymptotic distribution, making it competitive with traditional loss-based methods and applicable to various implicit and data-dependent loss functions. AI

IMPACT Introduces a statistically rigorous method for preventing overfitting in decision trees, potentially improving model generalization.

RANK_REASON The cluster contains an academic paper detailing a new method for machine learning.

Read on arXiv stat.ML →

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

ScoreStop offers gradient-based early stopping for decision trees

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The cluster contains an academic paper detailing a new method for machine learning.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Oliver J. Hines, Christian L. Hines ·

    ScoreStop: Gradient-based early stopping using functional score tests

    arXiv:2606.02740v1 Announce Type: new Abstract: Gradient boosted decision trees require a stopping rule to avoid overfitting. The standard rule monitors a validation loss and stops if the loss fails to improve for a fixed patience period. However, the patience parameter has no in…

  2. arXiv stat.ML TIER_1 English(EN) · Christian L. Hines ·

    ScoreStop: Gradient-based early stopping using functional score tests

    Gradient boosted decision trees require a stopping rule to avoid overfitting. The standard rule monitors a validation loss and stops if the loss fails to improve for a fixed patience period. However, the patience parameter has no interpretable scale and validation losses can be n…

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