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A General Framework for Abstention Under Label Shift
[article]
2022
arXiv
pre-print
In safety-critical applications of machine learning, it is often important to abstain from making predictions on low confidence examples. Standard abstention methods tend to be focused on optimizing top-k accuracy, but in many applications, accuracy is not the metric of interest. Further, label shift (a shift in class proportions between training time and prediction time) is ubiquitous in practical settings, and existing abstention methods do not handle label shift well. In this work, we
arXiv:1802.07024v5
fatcat:i7dx6yhyjnfojll3lg6lihnz3y