How do novel evaluation metrics for tabular generative models correlate with downstream classification accurac

Abstract

Abstract: Generative models have revolutionized multiple domains, yet their application to tabular data remains underexplored. Evaluating generative models for tabular data presents unique challenges due to structural complexity, large-scale variability, and mixed data types, making it difficult to intuitively capture intricate patterns. Existing evaluation metrics offer only partial insights, lacking a comprehensive measure of generative performance. To address this limitation, we propose three novel evaluation metrics: FAED, FPCAD, and RFIS. Our extensive experimental analysis, conducted on three stan

Research Question

How do novel evaluation metrics for tabular generative models correlate with downstream classification accuracy when applied to synthetic data used for pre-training large language models?

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