Predicting success in United States Air Force pilot training using machine learning techniques

Abstract

The chronic pilot shortage that has plagued the United States Air Force over the past three years poses a national-level problem that senior military members are working to overcome. Unfortunately, not all pilot candidates successfully complete the necessary training requirements to become fully qualified Air Force pilots, which wastes critical time and resources and only further exacerbates the pilot shortage problem. Therefore, it is important for the Air Force to carefully consider whom they select to attend pilot training. This research examines historical specialized undergraduate pilot training (SUPT) candidate data leveraging a variety of machine learning techniques to obtain insights on candidate success. Computational experimentation is performed to determine how selected machine learning techniques and their respective hyperparameters affect solution quality. Results reveal that the extremely randomized tree machine learning technique can achieve nearly 94% accuracy in predicting candidate success. Additional analysis indicates degree type and commissioning source are the most important features in determining candidate success. Ultimately, this research can inform the modification of future SUPT candidate selection criteria and other related Air Force personnel policies.