PubMed: https://pubmed.ncbi.nlm.nih.gov/38222359/ Paper accepted by AMIA 2023
ApAr is a decision-analytic utility-based approach to evaluating predictive models that communicates the range of prior probability and test cutoffs for which the model has positive utility.
This repository hosts the code to replicate the Applicability Area approach to ML model evaluation.
- Key functions are in the src/ApplicabilityArea_ApAr.py
- We also showcase an example using the Pima Indians Diabetes dataset
Additional simulations and examples will be added.