A two-sided empirical quantile test on a model's score lets a binary classifier flag ambiguous or out-of-distribution inputs as uncertain instead of labeling them.
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A method for classification of data with uncertainty using hypothesis testing
A two-sided empirical quantile test on a model's score lets a binary classifier flag ambiguous or out-of-distribution inputs as uncertain instead of labeling them.