Rebalancing allows you to create a large number of relevant business scenarios out of the few that are present in your data. Use it to simulate what-if scenarios based on your existing, historical data, or make minority classes clearly visible for downstream machine learning algorithms.

Rebalancing is only available for subject tables and can only be applied to a single column with the categorical encoding type.
Rebalancing cannot guarantee an increase in downstream model performance. Up or downsampling of a class in the original dataset only helps the downstream model in some instances.

mostly arrow Follow these steps to rebalance your data

green 1

Click Create synthetic data to begin

step 1

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Download the Insurance policy dataset , upload it, and click Proceed

step 2

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Click on the Data settings tab, scroll all the way down to find the age_bins column, and click on the cog icon to open the column settings drawer.

data settings

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Configure the column settings like in the screenshot below and click Save when done.

column settings

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Click Launch job to synthesize

launch job

mostly arrow3 Results

The resulting rebalanced data looks as follows: