EM with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation

21 Jan 2019Amr AlexandariAnshul KundajeAvanti Shrikumar

Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed. Label shift arises in settings like medical diagnosis, where a classifier trained to predict disease given symptoms must be adapted to scenarios where the baseline prevalence of the disease is different... (read more)

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