Minimizing expected loss measured by a proper scoring rule, such as Brier
score or log-loss (cross-entropy), is a common objective while training a
probabilistic classifier. If the data have experienced dataset shift where the
class distributions change post-training, then often the model's performance
will decrease, over-estimating the probabilities of some classes while
under-estimating the others on average. We propose unbounded and bounded
general adjustment (UGA and BGA) methods that transform all predictions to
(re-)equalize the average prediction and the class distribution. These methods
act differently depending on which proper scoring rule is to be minimized, and
we have a theoretical guarantee of reducing loss on test data, if the exact
class distribution is known. We also demonstrate experimentally that, when in
practice the class distribution is known only approximately, there is often
still a reduction in loss depending on the amount of shift and the precision to
which the class distribution is known.

提出了无界和有界的调整方法，以平均预测与类分布相等化，以最小化得分规则。实验结果表明在实际应用中，即使类分布仅近似已知，仍然可以根据移位量和类分布的精度减少损失。