  Trustworthy classifiers are essential to the adoption of machine learning
predictions in many real-world settings. The predicted probability of possible
outcomes can inform high-stakes decision making, particularly when assessing
the expected value of alternative decisions or the risk of bad outcomes. These
decisions require well-calibrated probabilities, not just the correct
prediction of the most likely class. Black-box classifier calibration methods
can improve the reliability of a classifier's output without requiring
retraining. However, these methods are unable to detect subpopulations where
calibration could also improve prediction accuracy. Such subpopulations are
said to exhibit "hidden heterogeneity" (HH), because the original classifier
did not detect them. This paper proposes a quantitative measure for HH. It also
introduces two similarity-weighted calibration methods that can address HH by
adapting locally to each test item: SWC weights the calibration set by
similarity to the test item, and SWC-HH explicitly incorporates hidden
heterogeneity to filter the calibration set. Experiments show that the
improvements in calibration achieved by similarity-based calibration methods
correlate with the amount of HH present and, given sufficient calibration data,
generally exceed calibration achieved by global methods. HH can therefore serve
as a useful diagnostic tool for identifying when local calibration methods
would be beneficial.
