University of Illinois Urbana-Champaign

Classification trees outperform logistic regression predictions of attrition in the U.S. Marine Corps

Alzate Vanegas, Juan Manuel

Permalink

https://hdl.handle.net/2142/108465

Description

Title
Classification trees outperform logistic regression predictions of attrition in the U.S. Marine Corps
Author(s)
Alzate Vanegas, Juan Manuel
Issue Date
2020-07-15
Director of Research (if dissertation) or Advisor (if thesis)
Drasgow, Fritz
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • Attrition
  • turnover
  • TAPAS
  • logistic regression
  • machine learning
  • CART
  • LASSO
  • random forests
Abstract
The present study compared the performance of machine learning classification models against logistic regression in the context of predicting training attrition from the Delayed Enlistment Program in the United States Marine Corps (UMSC) with scores from the Tailored Adaptive Personality Assessment System (TAPAS). The base-rate of attrition was low which made the model training process difficult, but the random-forest model outperformed logistic regression in predicting cases of attrition in a stratified 50% attrition sample.
Graduation Semester
2020-08
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/108465
Copyright and License Information
© 2020 Juan Manuel Alzate Vanegas

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
Dissertations and Theses - Psychology
Dissertations and Theses from the Dept. of Psychology