Every time anyone builds ML models, the focus quickly moves from overall numbers and highly technical measures of model health to how confident we are in each predicted result. In essence, can my model reliably deliver accurate results across the board? Aggregate metrics such as accuracy, recall, precision, R2, and AUPRC/AUROC are often not enough…
