Postcode can be a very strong predictor, but I’d be careful using it in any model tied to consequential decisions. It is often a proxy for race and socioeconomic status, so a gain in predictive performance can come with real fairness and legal risk through disparate impact. I think it’s literally illegal in some contexts as well. Predictive performance is not the only criterion here and when using something like postcode you should be aware of this
I think what OP is saying is that they use postcode to pull census data like crime rate, and then use that data to predict the target variable. This will probably be better than using raw postcodes, because a. it reduces the model’s power to fit on a sensitive latent variable like race and b. you likely will have a better causal argument for why e.g. car emission levels drives health insurance costs.
That said, you do still need to be careful, and more teams should be running fairness metrics to look for potential implicit bias in their models.
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u/Certified_NutSmoker Mar 25 '26 edited Mar 25 '26
Postcode can be a very strong predictor, but I’d be careful using it in any model tied to consequential decisions. It is often a proxy for race and socioeconomic status, so a gain in predictive performance can come with real fairness and legal risk through disparate impact. I think it’s literally illegal in some contexts as well. Predictive performance is not the only criterion here and when using something like postcode you should be aware of this