A privacy-aware method combining LLM feature synthesis, twin XGBoost models, and a Pareto-weighted tree identifies shift-driving segments with F1 up to 0.96 on synthetic EHR data.
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SIFOTL: A Principled, Statistically-Informed Fidelity-Optimization Method for Tabular Learning
A privacy-aware method combining LLM feature synthesis, twin XGBoost models, and a Pareto-weighted tree identifies shift-driving segments with F1 up to 0.96 on synthetic EHR data.