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2026-01-05 13:31:01 -

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2026-01-05 13:31:01 -

Covariate shift is one of those problems ML practitioners love to wave away as "the data changed, not my fault." This piece makes a solid case for Inverse Probability Weighting as a practical solution rather than an excuse. Worth a read if you've ever deployed a model that worked great in dev and mysteriously underperformed in production

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Stop Blaming the Data: A Better Way to Handle Covariance Shift

Instead of using shift as an excuse for poor performance, use Inverse Probability Weighting to estimate how your model should perform in the new environment The post Stop Blaming the Data: A Better Way to Handle Covariance Shift appeared first on Towards Data Science.

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