You've built a machine learning model that looks good in offline testing, and your team wants confidence that it will hold up when data and usage patterns change. You need a practical evaluation approach that goes beyond a single validation score.
How do you ensure that your machine learning models are robust?
Stability across cross-validation foldsCalibration of predicted probabilitiesThreshold sensitivity for business decisionsConfusion matrix behavior across segments and time