Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations
This work addresses synthetic data generation for tabular data, particularly in financial applications like credit scoring, but it appears incremental as it builds on existing methods like SparsePCA and XGBoost.
The authors tackled synthetic tabular data generation by proposing a minimalist framework using SparsePCA and XGBoost, applied to credit scoring data, and found it offers an alternative to raw and quantile perturbation for robustness testing.
We propose and study a minimalist approach towards synthetic tabular data generation. The model consists of a minimalistic unsupervised SparsePCA encoder (with contingent clustering step or log transformation to handle nonlinearity) and XGboost decoder which is SOTA for structured data regression and classification tasks. We study and contrast the methodologies with (variational) autoencoders in several toy low dimensional scenarios to derive necessary intuitions. The framework is applied to high dimensional simulated credit scoring data which parallels real-life financial applications. We applied the method to robustness testing to demonstrate practical use cases. The case study result suggests that the method provides an alternative to raw and quantile perturbation for model robustness testing. We show that the method is simplistic, guarantees interpretability all the way through, does not require extra tuning and provide unique benefits.