  Machine learning models typically focus on specific targets like creating
classifiers, often based on known population feature distributions in a
business context. However, models calculating individual features adapt over
time to improve precision, introducing the concept of decoupling: shifting from
point evaluation to data distribution. We use calibration strategies as
strategy for decoupling machine learning (ML) classifiers from score-based
actions within business logic frameworks. To evaluate these strategies, we
perform a comparative analysis using a real-world business scenario and
multiple ML models. Our findings highlight the trade-offs and performance
implications of the approach, offering valuable insights for practitioners
seeking to optimize their decoupling efforts. In particular, the Isotonic and
Beta calibration methods stand out for scenarios in which there is shift
between training and testing data.
