Calibration of Probabilities

Calibration of probabilities refers to how well predicted chances align with actual outcomes over time. For example, if a weather forecast predicts a 70% chance of rain on multiple days, then roughly 70% of those days should actually see rain for the forecast to be well-calibrated. Properly calibrated probabilities ensure that the predicted likelihoods truly reflect real-world results, making these predictions more reliable and trustworthy. This concept is important in fields like medicine, finance, and machine learning, where accurate probability estimates guide critical decisions.