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Data rebalancing for data exploration

Change the target distribution of variables in your synthetic data set. This allows you to, for example, perform what-if scenarios on a granular level to better understand your customer base. Or to upsample minority classes to help downstream models pick up patterns.
With our data rebalancing feature, users can adjust variable distributions for a more representative synthetic dataset. For instance, if one demographic is overrepresented, the platform can enhance others, optimizing data for specific use cases, improving insights, ensuring fairer model training, and enabling granular 'what-if' analyses.

It also helps upsample minority classes in imbalanced datasets, enhancing model performance. Find out more about Data rebalancing in our Documentation here.


Adjust variable distribution to counteract underrepresentation or overrepresentation, promoting balanced synthetic datasets.
Allows for "what-if" scenario analysis and customization for specific use cases, yielding enhanced insights.
Aids in upsampling minority classes in imbalanced datasets, boosting the efficacy of downstream models.

Ready to try synthetic data?

The best way to learn about synthetic data is to experiment with synthetic data generation. Try it for free or get in touch with our sales team for a demo.