  Machine learning models deployed in real-world applications are often
evaluated with precision-based metrics such as F1-score or AUC-PR (Area Under
the Curve of Precision Recall). Heavily dependent on the class prior, such
metrics make it difficult to interpret the variation of a model's performance
over different subpopulations/subperiods in a dataset. In this paper, we
propose a way to calibrate the metrics so that they can be made invariant to
the prior. We conduct a large number of experiments on balanced and imbalanced
data to assess the behavior of calibrated metrics and show that they improve
interpretability and provide a better control over what is really measured. We
describe specific real-world use-cases where calibration is beneficial such as,
for instance, model monitoring in production, reporting, or fairness
evaluation.
