Synthetic DataEvaluationMLQuality
Synthetic Data Quality: Metrics Beyond “Looks Real”
•7 min read
Synthetic data is only useful when it improves downstream performance. Measure coverage, fidelity, bias, and leakage—then test on real-world holdouts.
Define success by downstream tasks
The best metric is simple: does the model trained with synthetic data perform better on a realistic validation set? If not, the synthetic set is noise.
Track coverage (rare cases), fidelity (plausibility), and privacy (leakage risk) as separate dimensions.
Avoid leakage
If synthetic data is generated from sensitive sources, you need leakage tests and strict access control.
A practical approach is to generate from abstracted schemas and distributions, not raw user records.