A Confidence Interval-Based Method for Classifier Re-Calibration
Andrea Campagner, Lorenzo Famiglini, Federico Cabitza · Studies in health technology and informatics · 2022
We propose a re-calibration method for Machine Learning models, based on computing confidence intervals for the predicted confidence scores. We show the effectiveness of the proposed method on a COVID-19 diagnosis benchmark.