Let’s see it in practice for our first use case: performing data simulation on the UCI Adult Income dataset to see what the data would look like if there was no gender income gap. Once a two-table model is trained, one can simply generate more data and provide a new subject table as the seed for the linked table. The process is even easier in that case, as the pre-and post-processing steps are not required. Note that this same kind of conditional data generation can also be performed for two-table setups.
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