Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models

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Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models

Tabular Foundation Models (TFMs) have emerged as novel approaches for tabular predictive tasks, demonstrating competitive predictive performance to ensemble tree-based models. Most TFMs are trained and evaluated on independent and identically distributed data, but this assumption changes in real-world scenarios due to distribution shifts, which compromise the robustness of models. Limited research has been conducted of TFMs under distribution shifts. We present an empirical evaluation of Out-Of-

Source: ArXiv AI