You have trained and shipped a model, and the team wants confidence that its performance will hold up as usage grows and data changes over time. You need an evaluation approach that covers validation stability, score calibration, and decision threshold quality.
How do you ensure your models are robust, scalable, and accurate?
Cross validation for stability across folds or time windowsAUC-ROC for ranking qualityCalibration for probability reliabilityThreshold tuning for business decisions