Measurement noise limits the advantage of nonlinear models over linear models in biomedical prediction

In three linesarXiv paper demonstrates that on biomedical tabular data, measurement noise limits the advantage of nonlinear models (deep networks, gradient boosting) over linear regression. Degree-k interactions are attenuated by the k-th power of feature reliability, while linear components are attenuated only once. Analysis of 140 UK Biobank tasks confirms this noise signature.

Summary generated by Claude — human-verified