Class Prior Estimation under Covariate Shift: No Problem?. (arXiv:2206.02449v2 [stat.ML] UPDATED)

We show that in the context of classification the property of source and
target distributions to be related by covariate shift may be lost if the
information content captured in the covariates is reduced, for instance by
dropping components or mapping into a lower-dimensional or finite space. As a
consequence, under covariate shift simple approaches to class prior estimation
in the style of classify and count with or without adjustment are infeasible.
We prove that transformations of the covariates that …

arxiv ml prior shift