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It is often observed that the probabilistic predictions given by a machinelearning model can disagree with averaged actual outcomes on specific subsetsof data, which is also known as the issue of miscalibration. It is responsiblefor the unreliability of practical machine learning systems. For example, inonline advertising, an ad can receive a click-through rate prediction of 0.1over some population of users where its actual click rate is 0.15. In suchcases, the probabilistic predictions have to be fixed before the system can bedeployed.  In this paper, we first introduce a new evaluation metric named field-levelcalibration error that measures the bias in predictions over the sensitiveinput field that the decision-maker concerns. We show that existing post-hoccalibration methods have limited improvements in the new field-level metric andother non-calibration metrics such as the AUC score. To this end, we proposeNeural Calibration, a simple yet powerful post-hoc calibration method thatlearns to calibrate by making full use of the field-aware information over thevalidation set. We present extensive experiments on five large-scale datasets.The results showed that Neural Calibration significantly improves againstuncalibrated predictions in common metrics such as the negative log-likelihood,Brier score and AUC, as well as the proposed field-level calibration error.

Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions