Model Calibration
Model calibration is a statistical and machine learning concept that ensures a predictive model's output probabilities accurately reflect the true likelihood of events. It involves adjusting or evaluating a model so that when it predicts a 70% probability for an outcome, that outcome occurs approximately 70% of the time in reality. This is crucial for decision-making under uncertainty, as it builds trust in probabilistic forecasts.
Developers should learn and use model calibration when building machine learning models for applications where accurate probability estimates are critical, such as in healthcare (disease risk prediction), finance (credit scoring), or weather forecasting. It helps avoid overconfident or underconfident predictions, enabling better risk assessment and resource allocation. Calibration is especially important for models like neural networks or gradient boosting, which can produce poorly calibrated probabilities despite high accuracy.