Synthetic Data Metrics (SDMetrics) is an open source Python library for evaluating tabular synthetic data. Compare synthetic data against real data using a variety metrics, generate visual reports and share them with your team.
Flexible, Intuitive Evaluation
The SDMetrics library is model-agnostic, meaning you can use it with synthetic data created by any model at any time.
The SDMetrics library welcomes contributions from active research areas! Browse our Metrics in Beta and experiment with cutting edge methods to evaluate your data.
Owned & Maintained by DataCebo
The SDMetrics library is a part of the Synthetic Data Vault Project, first created at MIT's Data to AI Lab in 2016. After 4 years of research and traction with enterprise, we created DataCebo in 2020 with the goal of growing the project.
Today, DataCebo is the proud developer of the SDV, the largest ecosystem for synthetic data generation & evaluation.