Interpretable ML Under the Microscope: Performance, Meta-Features, and the Regression-Classification Predictability Gap

ArXi:2601.00428v2 Announce Type: replace As machine learning models are increasingly deployed in high-stakes domains, the need for interpretability has grown to meet strict regulatory and accountability constraints. Despite this interest, systematic evaluations of inherently interpretable models for tabular data remain scarce and often focus solely on aggregated performance.