Stop Deleting "Useless" Features: Why Univariate AUC is Lying to You
This story is from 2026-10-13. It is preserved in the archive; the latest stories are on the live feed.
In high-stakes domains like BFSI (Banking, Financial Services, and Insurance) and Healthcare, the most valuable predictive signals are often hiding in plain sight. Many data teams follow a standard pruning pipeline: calculate the univariate AUC for every feature, and if a feature scores near 0.5 (r…
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- 2026-10-13 11:52 · DEV Community — Machine Learning
Stop Deleting "Useless" Features: Why Univariate AUC is Lying to You