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Activity Number:
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619
- Topics in Defense and National Security
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Type:
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Contributed
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Date/Time:
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Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Defense and National Security
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Abstract #304412
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Presentation
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Title:
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A New Military Retention Prediction Model: Machine Learning for High-Fidelity Prediction
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Author(s):
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Michael Guggisberg* and Julie Pechacek and Alan Gelder and James Bishop and Cullen Roberts and Joseph King and Yevgeniy Kirpichevsky
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Companies:
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Institute for Defense Analyses and Institute for Defense Analyses and Institute for Defense Analyses and Institute for Defense Analyses and Institute for Defense Analyses and Institute for Defense Analyses and Institute for Defense Analyses
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Keywords:
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machine learning;
neural network;
survival;
retention;
military
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Abstract:
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The Department of Defense (DoD) is the largest employer in the United States of America with 2.15 million service members in 2019. The DoD must anticipate retention of service members to effectively perform its mission. A team from the Institute of Defense Analyses began development of the Retention Prediction Model (RPM), an application of a feed-forward neural network on a novel dataset. The RPM is a tool that predicts retention of service members in the DoD and can be used to inform DoD leadership about anticipated retention at the service member level. Prediction on an out-of-sample set results in a concordance no worse than 0.78 for any given year or 0.73 for the restricted mean survival time.
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Authors who are presenting talks have a * after their name.
