Regression Recalibration by Learning PIT Map Values

In an era dominated by large-scale machine learning models, poor calibration severely limits the trustworthiness of the results. As we increasingly rely on complex systems, recalibration becomes essential, where the objective is to find a mapping that improves the model’s probabilistic predictions. Motivated by the well-known quantile recalibration approach, we explore a broad class of recalibration functions based on learning transformations on the probability integral transform values that includes quantile recalibration as a special case. We derive solutions for the optimal mapping under this class of functions, and we propose a novel recalibration method that outperforms quantile recalibration in both calibration and sharpness in our empirical study. Additionally, we demonstrate the utility of our method in a case study on predicting global stratospheric temperatures following Mt. Pinatubo’s eruption using a convolutional neural network, where we show that our method can be used to adjust the model’s predictions for the post-eruption climate.