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The InterModel Vigorish (IMV) as a flexible and portable approach for quantifying predictive accuracy with binary outcomes

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  • Benjamin W Domingue
  • Charles Rahal
  • Jessica Faul
  • Jeremy Freese
  • Klint Kanopka
  • Alexandros Rigos
  • Ben Stenhaug
  • Ajay Shanker Tripathi

Abstract

Understanding the “fit” of models designed to predict binary outcomes has been a long-standing problem across the social sciences. 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 consider its performance using examples spanning the social and natural sciences. 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 and the understanding of social outcomes.

Suggested Citation

  • Benjamin W Domingue & Charles Rahal & Jessica Faul & Jeremy Freese & Klint Kanopka & Alexandros Rigos & Ben Stenhaug & Ajay Shanker Tripathi, 2025. "The InterModel Vigorish (IMV) as a flexible and portable approach for quantifying predictive accuracy with binary outcomes," PLOS ONE, Public Library of Science, vol. 20(3), pages 1-24, March.
    • Handle: RePEc:plo:pone00:0316491
    DOI: 10.1371/journal.pone.0316491
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    1. Eli Puterman & Jordan Weiss & Benjamin A. Hives & Alison Gemmill & Deborah Karasek & Wendy Berry Mendes & David H. Rehkopf, 2020. "Predicting mortality from 57 economic, behavioral, social, and psychological factors," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(28), pages 16273-16282, July.
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