EVALUATION OF MACHINE LEARNING APPLICABILITY FOR USMC REENLISTMENT
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Authors
Terrazas, Gustavo A.
Subjects
machine learning
talent management
reenlistment
Marine Corps
talent management
reenlistment
Marine Corps
Advisors
Ahn, Sae Young
Fan, James J.
Date of Issue
2020-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
This research examines the applicability of machine learning algorithms to best predict the probability of reenlistment of enlisted first-term Marines. Given the availability of data today, machine learning can be a useful tool to make policy decisions that can impact the future Fleet Marine Force. This thesis uses demographic data, pre-boot-camp data, performance indicators, legal data, awards data, and selective reenlistment bonus indicators to identify factors that contribute to the prediction of reenlistment. This thesis applies data from the Total Force Data Warehouse (TFDW) and fits machine learning algorithms to assess their prediction accuracy. Measuring machine learning models by accuracy alone is not sufficient. An evaluation of top predictors is conducted to choose the best-preforming machine learning algorithm. Given the data used in this thesis, the machine learning algorithm that best predicts the probability of reenlistment is the C5 algorithm. Variables associated with deployment and performance are among the top ten predictors of importance.
Type
Thesis
Description
Series/Report No
Department
Graduate School of Defense Management (GSDM)
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NPS Report Number
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Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.