A hands-on lab showing how “improving” a single metric (AUC/accuracy/F1) can worsen real-world outcomes. Includes metric audits, slice checks, cost-sensitive evaluation, threshold tuning, and decision policies you can defend, so dashboards don’t quietly ship bad decisions.
data-science machine-learning metrics decision-making calibration kpi classification data-analysis confusion-matrix model-evaluation interpretability roc-auc precision-recall mlops cost-sensitive-learning model-monitoring business-metrics tradeoffs threshold-tuning model-risk
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Updated
Apr 26, 2026