Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test

Understand

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.