Model Calibration

Model calibration is the process of adjusting a predictive model so that its confidence levels accurately reflect real-world outcomes. For example, if a model predicts a 70% chance of rain on multiple days, then it should indeed rain on about 70% of those days. Proper calibration ensures the model's probabilities match actual events, increasing trust and usefulness. It involves comparing predicted probabilities to observed frequencies and making adjustments to improve this alignment, so decisions based on these predictions are more reliable and grounded in reality.