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 noisy or implicitly defined by a user-specified gradient. We propose ScoreStop, a gradient-based early-stopping rule that casts the stopping decision at each iteration as a test of the null hypothesis that the current predictor is the population risk minimizer. We use a functional score test, computed on validation data, with a statistic that is scale-invariant in the update direction, with a known asymptotic distr...
| Authors | Oliver J. Hines, Christian L. Hines |
|---|---|
| Primary category | stat.ML |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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