Calibration of Predictions

Calibration of predictions refers to how well a model’s predicted probabilities align with actual outcomes. For example, if a weather model says there's a 70% chance of rain on many days, then on those days, it should rain about 70% of the time for the model to be well-calibrated. Proper calibration ensures that the probabilities given by a model are trustworthy and accurately reflect real-world chances. Good calibration is crucial for decision-making, especially when risk assessments depend on reliable probability estimates.