Calibration is typically performed by comparing the predicted outcomes from a model to the actual observed outcomes. The process involves several steps:
Data Collection: Gather a dataset that includes both predicted and observed outcomes. Statistical Analysis: Use statistical methods such as calibration plots, Hosmer-Lemeshow test, or Brier score to assess the agreement between predicted and observed values. Model Adjustment: If discrepancies are found, adjust the model parameters to better align with the observed data. Validation: Validate the adjusted model using a separate dataset to ensure that it performs well with new data.
