Predictions of attrition among US Marine Corps: Comparison of four predictive methods

Juan Manuel Alzate Vanegas, William Wine, Fritz Drasgow

Research output: Contribution to journalArticlepeer-review


The present study compared the performance of logistic regression models with that of machine learning classification models (classification trees and random forests) in the context of predicting training attrition from the Delayed Enlistment Program in the United States Marine Corps (USMC) with scores from the Tailored Adaptive Personality Assessment System (TAPAS). Performance was assessed according to the type of misclassification error and across a variety of different reasons for attrition. The base rate of attrition was low, which impeded the training process, but the machine learning models outperformed logistic regression in predicting voluntary attrition in a stratified 50% attrition sample.

Original languageEnglish (US)
Pages (from-to)147-166
Number of pages20
JournalMilitary Psychology
Issue number2
StatePublished - 2022


  • attrition
  • classification
  • logistic regression
  • machine learning

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Social Sciences (miscellaneous)
  • General Psychology


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