Recall when to use precision, recall, AUC, calibration, and threshold views in predictive analytics.
When should precision lead the review? When action capacity is scarce and false positives waste meaningful time, money, or trust. Example: the top 300 churn outreach accounts must be worth contacting. When should recall lead the review? When missed positives are the expensive or harmful error. Example: missing a critical supply delay is worse than reviewing an extra shipment. AUC versus threshold table Use AUC to compare rankers; use thresholds to choose the operating rule. The model says 0.82, so it means 82 percent probability, right? Better line Only if calibration shows cases scored near 0.82 become positive about 82…
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