Tabular foundation models fail to classify statistically matched legal vs rule-violating database states, achieving only chance accuracy without rule-derived audits.
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2 Pith papers cite this work. Polarity classification is still indexing.
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The authors introduce Survey-aware Machine Learning (SaML) as a nine-step guideline that integrates survey design metadata throughout the ML lifecycle to enable valid population inference from complex health surveys.
citing papers explorer
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Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models
Tabular foundation models fail to classify statistically matched legal vs rule-violating database states, achieving only chance accuracy without rule-derived audits.
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Survey-aware Machine Learning: A Guideline for Valid Population Health Inference based on Scoping Review
The authors introduce Survey-aware Machine Learning (SaML) as a nine-step guideline that integrates survey design metadata throughout the ML lifecycle to enable valid population inference from complex health surveys.