A New Military Retention Prediction Model: Machine Learning for High-Fidelity Forecasting

Abstract

Using machine learning algorithms and 18 years of data, we predict individual-level attrition among active duty personnel in all military Services, with hold-out sample prediction accuracies typically exceeding 70%. Importantly, our methodology accommodates both right and left-censoring of observed career paths, and significantly outperforms traditional survival analysis. Using these individual-level predictions, we generate aggregate predicted force profiles which closely align with historical actuals. This and other features offer a rich slate of observations for further empirical analysis, and suggest new policy levers for managing attrition.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2019
Accession Number
AD1122258

Entities

People

  • Alan Gelder
  • Cullen Roberts
  • James Bishop
  • Joe King
  • Julie Pechacek
  • Michael Guggisberg
  • Yev Kirpichevsky

Organizations

  • Institute for Defense Analyses

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Active Duty
  • Algorithms
  • Attrition
  • Classification
  • Contracts
  • Delphi Method
  • Department Of Defense
  • Learning
  • Machine Learning
  • Management Personnel
  • Military Personnel
  • Neural Networks
  • Observation
  • Organizational Structure
  • Personnel Retention
  • Reliability
  • Survival

Readers

  • Computational Modeling and Simulation
  • Naval Personnel Management
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks