Synthesizers
The SDV offers a variety of synthesizers, which use different algorithms to generate synthetic data.
Don't know where to start? We recommend the Gaussian Copula Synthesizer. This is synthesizer models data quickly, which high statistical quality. It also supports customizationa and many options for sampling.
Use a classical ML algorithm to learn from real data and generate synthetic data. This synthesizer is the most customizable, with faster performance than other approaches.
Use GAN-based ML algorithm to learn from real data and generate synthetic data. This model can create synthetic data at a high fidelity given enough training time.
Generate synthetic data from scratch using only the metadata. This synthesizer produces unlimited single table data with the correct formatting.
Use a variational autoencoder ML model to learn from real data and generate synthetic data. This model can create synthetic data at a high fidelity given enough training time.
[Experimental] Copula GAN Synthesizer
Use an hybrid ML model to learn from the real data and generate synthetic data. This algorithm combines classicial statistics with GAN-based modeling.
Which synthesizer should I use?
Use the table below to determine the right synthesizer for your needs.
| Feature | ＊ Day Z | Gaussian Copula | CTGAN | TVAE |
|---|---|---|---|---|
Synthesize with only metadata | ||||
Add constraints | ||||
Understand & modify | ||||
Anonymize columns | ||||
Learn | ||||
Sample with conditions | ||||
Availability | Enterprise | Public | Public | Public |
＊SDV Enterprise Features. These features are only available for licensed, enterprise users. To learn more about the SDV Enterprise features and purchasing a license, visit our website.
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