Correlation of Novel Tabular Generative Metrics with Mode Collapse Detection in Imbalanced Datasets Versus Precision and Recall

Synthetic data is becoming an increasingly promising technology, and successful applications can improve privacy, fairness, and data democratization. While there are many methods for generating synthetic tabular data, the task remains non-trivial and unexplored for specific scenarios. One such scenario is survival data. Here, the key difficulty is censoring: for some instances, we are not aware of the time of event, or if one even occurred. Imbalances in censoring and time horizons cause generative models to experience three new failure modes specific to survival analysis: (1) generating too f

Research goal: How do novel tabular generative metrics correlate with mode collapse detection in imbalanced datasets compared to precision and recall across different synthetic data generators?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.1/10.