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Adapting to Label Shift with Bias-Corrected Calibration

Label shift refers to the phenomenon where the marginal probability p(y) of observing a particular class changes between the training and test distributions, while the conditional probability p(x|y) stays fixed. This is relevant in settings such as medical diagnosis, where a classifier trained to predict disease based on observed symptoms may need to be adapted to a different distribution where the baseline frequency of the disease is higher. Given estimates of p(y|x) from a predictive model, one can apply domain adaptation procedures including Expectation Maximization (EM) and Black-Box Shift Estimation (BBSE) to efficiently correct for the difference in class proportions between the training and test distributions. Unfortunately, modern neural networks typically fail to produce well-calibrated estimates of p(y|x), reducing the effectiveness of these approaches. In recent years, Temperature Scaling has emerged as an efficient approach to combat miscalibration. However, the effectiveness of Temperature Scaling in the context of adaptation to label shift has not been explored. In this work, we study the impact of various calibration approaches on shift estimates produced by EM or BBSE. In experiments with image classification and diabetic retinopathy detection, we find that calibration consistently tends to improve shift estimation. In particular, calibration approaches that include class-specific bias parameters are significantly better than approaches that lack class-specific bias parameters, suggesting that reducing systematic bias in the calibrated probabilities is especially important for domain adaptation.

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