Stratification of uncertainties recalibrated by isotonic regression and its impact on calibration error statistics

• URL: http://arxiv.org/abs/2306.05180v1
• Date: Thu, 8 Jun 2023 13:24:39 GMT
• Title: Stratification of uncertainties recalibrated by isotonic regression and its impact on calibration error statistics
• Authors: Pascal Pernot
• Abstract summary: Recalibration of prediction uncertainties by isotonic regression might present a problem for bin-based calibration error statistics. I show on an example how this might significantly affect the calibration diagnostics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Abstract Post hoc recalibration of prediction uncertainties of machine learning regression problems by isotonic regression might present a problem for bin-based calibration error statistics (e.g. ENCE). Isotonic regression often produces stratified uncertainties, i.e. subsets of uncertainties with identical numerical values. Partitioning of the resulting data into equal-sized bins introduces an aleatoric component to the estimation of bin-based calibration statistics. The partitioning of stratified data into bins depends on the order of the data, which is typically an uncontrolled property of calibration test/validation sets. The tie-braking method of the ordering algorithm used for binning might also introduce an aleatoric component. I show on an example how this might significantly affect the calibration diagnostics.

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