The InterModel Vigorish (IMV) as a flexible and portable approach for quantifying predictive accuracy with binary outcomes
Year: 2021
Type: preprint
Abstract: Understanding the ``fit'' of models designed to predict binary outcomes has been a long-standing problem. We propose a flexible, portable, and intuitive metric for quantifying the change in accuracy between two predictive systems in the case of a binary outcome: the InterModel Vigorish (IMV). The IMV is based on an analogy to weighted coins, well-characterized physical systems with tractable probabilities. The IMV is always a statement about the change in fit relative to some baseline model---which can be as simple as the prevalence---whereas other metrics are stand-alone measures that need to be further manipulated to yield indices related to differences in fit across models. Moreover, the IMV is consistently interpretable independent of baseline prevalence. We contrast this metric with alternatives in numerous simulations. The IMV is more sensitive to estimation error than many alternatives and also shows distinctive sensitivity to prevalence. We then showcase its flexibility across examples spanning the social, biomedical, and physical sciences. We also demonstrate how it can be used to provide straightforward interpretation of logistic regression coefficients. The IMV allows for precise answers to questions about changes in model fit in a variety of settings in a manner that will be useful for furthering research with binary outcomes.
Authors: Benjamin Domingue, Charles Rahal, Jessica Faul, Jeremy Freese, Klint Kanopka, Alexandros Rigos, Ben Stenhaug, Ajay Tripathi
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4235949290