Comments (1)
I'd recommend looking at your model's performance sliced by different subgroups of the dataset to see on which groups the model is performance best/worst at. And then trying to find more training data in those areas of concern to get a better trained model.
In general, fairness evaluation for regression models is not as simple as binary classification, as many of the techniques and analysis depend on having a positive and negative class to investigate. Another approach is to turn your regression problem into a classification problem and using the binary classification fairness tools to understand more about your model in that context.
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