AI RESEARCH
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
arXiv CS.LG
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ArXi:2606.02384v1 Announce Type: new Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures. At the same time, feature engineering remains a critical yet underexplored component of real-world modeling pipelines that is entirely absent from modern benchmarks, which creates an unquantified evaluation gap. In this work, we