  Machine Learning (ML) models are often complex and difficult to interpret due
to their 'black-box' characteristics. Interpretability of a ML model is usually
defined as the degree to which a human can understand the cause of decisions
reached by a ML model. Interpretability is of extremely high importance in many
fields of healthcare due to high levels of risk related to decisions based on
ML models. Calibration of the ML model outputs is another issue often
overlooked in the application of ML models in practice. This paper represents
an early work in examination of prediction model calibration impact on the
interpretability of the results. We present a use case of a patient in diabetes
screening prediction scenario and visualize results using three different
techniques to demonstrate the differences between calibrated and uncalibrated
regularized regression model.
