Take a dataset, extract the features, send to model, design model, check if we match desired output. That’s the standard Data science workflow. What we do is e

Take a dataset, extract the features, send to model, design model, check if we match desired output. That’s the standard Data science workflow. What we do is extract features in an unsupervised way and build a model to recreate the dataset from said feature. When the synthesized dataset is indistinguishable from the real thing. You should be able to build a model that gets you 90%-100% of the way there with synthetic data, then tune / retrain on the real data. The model that does decisioning itself doesn’t matter. If you needed to adjust the synthetic dataset, you’d have to either build a new generative model OR otherwise weight the dataset.