Estimating classification consistency of machine learning models for screening measures

This article illustrates novel quantitative methods to estimate classification consistency in machine learning models used for screening measures. Screening measures are used in psychology and medicine to classify individuals into diagnostic classifications. In addition to achieving high accuracy, it is ideal for the screening process to have high classification consistency, which means that respondents would be classified into the same group every time if the assessment was repeated. Although machine learning models are increasingly being used to predict a screening classification based on individual item responses, methods to describe the classification consistency of machine learning models have not yet been developed. This article addresses this gap by describing methods to estimate classification inconsistency in machine learning models arising from two different sources: sampling error during model fitting and measurement error in the item responses. These methods use data resampling techniques such as the bootstrap and Monte Carlo sampling. These methods are illustrated using three empirical examples predicting a health condition/diagnosis from item responses. R code is provided to facilitate the implementation of the methods. This article highlights the importance of considering classification consistency alongside accuracy when studying screening measures and provides the tools and guidance necessary for applied researchers to obtain classification consistency indices in their machine learning research on diagnostic assessments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).