Do Tabular Foundation Models Still Need Feature Engineering?

Title:Do Tabular Foundation Models Still Need Feature Engineering?

Abstract:Feature engineering has long been a cornerstone of tabular machine learning. Tabular foundation models (TFMs) are pretrained on a wide range of tabular datasets and applied via in-context learning. Their rise raises a natural question: does manual feature construction still matter as these models become more capable? To answer this, we perform a controlled study across several versions of two major TFM families, testing a wide range of existing feature engineering techniques on benchmark datasets from TabArena. We find a consistent pattern: feature engineering gains are concentrated in earlier model generations and become negligible for the strongest models. These results suggest that stronger TFMs depend less on explicitly engineered input representations. In a complementary experiment, however, adding in-context information from related datasets still improves performance. Our findings indicate a shift in the source of performance gains for stronger TFMs: re-representing existing inputs becomes less effective, while providing additional task-relevant context remains beneficial.

Machine Learning (cs.LG)

arXiv:2609.13202 [cs.LG]

(or arXiv:2609.13202v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.13202 Focus to learn more arXiv-issued DOI via DataCite

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