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.
Campagner, A., Famiglini, L., Cabitza, F. (2022). A Confidence Interval-Based Method for Classifier Re-Calibration. In Studies in Health Technology and Informatics (pp.127-128). IOS Press [10.3233/SHTI220413].
A Confidence Interval-Based Method for Classifier Re-Calibration
Campagner A.
;Famiglini L.;Cabitza F.
2022
Abstract
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.
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