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LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers propose LoBoost, a model-native local conformal prediction method that improves uncertainty quantification for gradient-boosted trees.
Open sourceOneBench interpretation
Institutional assessment
So what
Applying model-native local conformal prediction to gradient-boosted trees provides mathematically rigorous uncertainty intervals for risk-critical tabular models like credit scoring.
Do what
Ask your model validation and risk teams to evaluate LoBoost for inclusion in the bank's standard tabular model evaluation toolkit.