Calibration can be challenging due to several factors:
Data Quality: Poor quality or incomplete data can lead to inaccurate calibration results. Model Complexity: Complex models may require sophisticated methods for accurate calibration. Generalizability: A model calibrated on a specific population may not perform well on a different population. Overfitting: Adjusting the model too closely to the calibration data may result in overfitting, reducing its predictive power on new data.
