You have a classification model that outputs probabilities, and stakeholders want to use those scores for decisions, not just ranking. You need to explain how to judge whether a score like 0.8 really means an 80 percent chance of the event.
What does calibration mean in model evaluation, and why is it important?
Understanding of calibration for probabilistic classifiersDifference between ranking quality and probability qualityWhy log loss and calibration matter for threshold-based decisionsHow calibration affects confusion-matrix tradeoffs in practice