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

June, 2019
IDA document: D-10712
FFRDC: Systems and Analyses Center
Type: Documents
Division: Strategy, Forces and Resources Division
Authors:
Authors
Julie Pechacekn, Alan Gelder, Cullen Roberts, Joe King, James Bishop, Michael Guggisberg, Yev Kirpichevsky See more authors
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