Classification trees outperform logistic regression predictions of attrition in the U.S. Marine Corps
Alzate Vanegas, Juan Manuel
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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
Date of Ingest
2020-10-07T20:59:42Z
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.
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