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.
poster + paper
Calibration; confidence interval; medical ML; trustable AI;
English
32nd Medical Informatics Europe Conference, MIE 2022 - 27 May 2022 through 30 May 2022
2022
Studies in Health Technology and Informatics
9781643682846
2022
294
127
128
none
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/394400
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