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Introduced on day 13

Label shift (prior shift)

A change in the distribution of the target between training and deployment while the feature distribution within each class stays the same.

In detail

Under label shift, p(y) changes and p(x given y) does not, so a classifier's ranking holds but its probabilities, precision and best threshold change. Detect it by comparing the predicted-positive rate and labelled outcomes over time; correct it with a prior adjustment or confusion-matrix based estimates. If p(x given y) also moves, the problem is a broader dataset shift.