A. Ziegler

Calibrating machine learning approaches for probability estimation without calibration data

A novel calibration method is proposed, SimCal, which uses synthetic data generated from the model development data in conjunction with marginal statistics from the calibration cohort to address the challenge of calibrating statistical prediction models for binary outcomes when training data is lacking.