Model Calibration
Model Calibration is the process of ensuring that a machine learning model's predicted probabilities accurately reflect the likelihood of the corresponding outcomes. A well-calibrated model should produce probabilities that align closely with observed results.
What is Model Calibration?
A model is calibrated when predictions assigned a particular probability occur at approximately that frequency in practice. For example, among predictions given a 70% probability of an event, the event should occur roughly 70% of the time. Calibration can be assessed using tools such as calibration curves and reliability diagrams.
Why is Model Calibration Important?
Accurate probability estimates are important when predictions are used to assess risk or support decisions. A model can have good classification accuracy while still producing poorly calibrated probabilities. Calibration helps organizations make better decisions based on the confidence of model predictions.
Common use cases
Model Calibration is commonly used in healthcare risk prediction, fraud detection, credit scoring, weather forecasting, classification systems, and other applications where reliable probability estimates are important.