Files in this item

FilesDescriptionFormat

application/pdf

application/pdfALZATEVANEGAS-THESIS-2020.pdf (1MB)
(no description provided)PDF

Description

Title:Classification trees outperform logistic regression predictions of attrition in the U.S. Marine Corps
Author(s):Alzate Vanegas, Juan Manuel
Advisor(s):Drasgow, Fritz
Department / Program:Psychology
Discipline:Psychology
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(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.
Issue Date:2020-07-15
Type:Thesis
URI:http://hdl.handle.net/2142/108465
Rights Information:© 2020 Juan Manuel Alzate Vanegas
Date Available in IDEALS:2020-10-07
Date Deposited:2020-08


This item appears in the following Collection(s)

Item Statistics