Comparative Robustness of Tabular Evaluation Metrics on Noisy, Sparse, Mixed-Type Real-World Datasets

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 goal: How do the proposed tabular evaluation metrics compare in robustness when applied to real-world, mixed-type tabular datasets with varying levels of sparsity and noise?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.8/10.