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The InterModel Vigorish (IMV) as a flexible and portable approach for quantifying predictive accuracy with binary
Benjamin W Domingue1, Charles Rahal2, Jessica Faul3
1Graduate School of Education, Stanford University, Stanford, California, United States of America.
Plos One
|March 21, 2025
Summary
We introduce the InterModel Vigorish (IMV), a new metric for assessing changes in predictive model accuracy for binary outcomes. The IMV offers consistent interpretation across different prevalence rates, aiding social science research.
Area of Science:
- Social Sciences
- Statistical Modeling
- Predictive Analytics
Background:
- Quantifying model fit for binary outcome prediction is a persistent challenge in social sciences.
- Existing metrics often require manipulation to compare model fit differences.
Purpose of the Study:
- To introduce the InterModel Vigorish (IMV), a novel metric for measuring changes in predictive accuracy.
- To provide a flexible, portable, and intuitive tool for assessing model fit.
- To offer a metric consistently interpretable across varying baseline prevalence rates.
Main Methods:
- Developed the InterModel Vigorish (IMV) based on an analogy to weighted coins.
- Conducted simulations to contrast IMV with alternative metrics.
- Applied IMV to examples from social and natural sciences.
Main Results:
- The IMV quantifies the change in accuracy between two predictive systems for binary outcomes.
- IMV is always a statement about change in fit relative to a baseline model.
- IMV demonstrates consistent interpretability irrespective of baseline prevalence.
- IMV shows greater sensitivity to estimation error and prevalence compared to some alternatives.
Conclusions:
- The InterModel Vigorish (IMV) provides a precise and interpretable method for evaluating changes in model fit.
- IMV is applicable across diverse scientific fields, enhancing research on social and natural outcomes.
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