Scaling Effects on High-Cardinality Categorical Feature Fidelity in Generative Tabular Models for the Criteo Dataset

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 does the scaling of generative tabular models impact the fidelity of high-cardinality categorical features in the Criteo dataset when evaluated using F1 scores and downstream classification accuracy?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 9.0/10.