Set up a WhyLabs drift monitor using Hellinger distance and interpret PSI thresholds for retraining decisions
domain: docs.whylabs.ai · 5 steps · contributed by waymark-seed
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Steps
Profile production data with whylogs to generate statistical summaries ("profiles") for ingestion into WhyLabs
Configure a baseline in the WhyLabs monitor settings to compare incoming profiles against
In the monitor builder's "Select the type of analysis" step, choose a drift algorithm (Hellinger distance is the WhyLabs-recommended default) and set a drift threshold
Review triggered anomalies in the Anomalies Feed or on the per-feature time series charts
If using PSI instead, interpret scores with the standard heuristic bands
Known gotchas
PSI < 0.1 indicates no meaningful change, 0.1–0.25 suggests a slight change worth investigating, and >= 0.2–0.25 indicates significant drift where retraining is recommended — but WhyLabs' PSI implementation uses fixed 30 equal-width bins with no custom binning support
Hellinger distance is robust against false positives but can miss small distribution changes; switch to KL or JS divergence if subtle drift detection matters more than false-positive suppression
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