  Despite recent advances in synthetic data generation, the scientific
community still lacks a unified consensus on its usefulness. It is commonly
believed that synthetic data can be used for both data exchange and boosting
machine learning (ML) training. Privacy-preserving synthetic data generation
can accelerate data exchange for downstream tasks, but there is not enough
evidence to show how or why synthetic data can boost ML training. In this
study, we benchmarked ML performance using synthetic tabular data for four use
cases: data sharing, data augmentation, class balancing, and data
summarization. We observed marginal improvements for the balancing use case on
some datasets. However, we conclude that there is not enough evidence to claim
that synthetic tabular data is useful for ML training.
