Bayesian Decision Theory (BDT)
Same posterior, P(p > 0.6) = 0.703716 throughout. The decision flips between a cost ratio of 3 and 4.
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Recalibrate Thresholds
Learn how to derive a decision cutoff, tune it without leakage, and reassess it after changes to probabilities, costs, data or alert behavior.
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ROC Curve (Receiver Operating Characteristic)
What the curve shows, why it is invariant to prevalence-only shifts and strictly increasing rescaling, and where that invariance becomes a blind spot.
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Partial AUC (pAUC)
A pAUC that normalizes to 0.5 can be ten times better than chance. Where the baseline actually sits, and how to standardize properly.