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.

过去三年困扰美国空军的长期飞行员短缺问题构成了高级军事人员正在努力克服的国家级问题。不幸的是，并非所有飞行员候选人都能成功完成必要的培训要求，成为完全合格的空军飞行员，这浪费了关键的时间和资源，只会进一步加剧飞行员短缺问题。因此，空军必须仔细考虑他们选择谁参加飞行员培训。本研究利用各种机器学习技术检查历史专业本科飞行员培训 (SUPT) 候选人数据，以获得有关候选人成功的见解。执行计算实验以确定所选机器学习技术及其各自的超参数如何影响解决方案质量。结果表明，极其随机的树机器学习技术在预测候选成功方面可以达到近 94% 的准确率。额外的分析表明学位类型和委托来源是确定候选人成功的最重要特征。最终，这项研究可以为未来 SUPT 候选人选择标准和其他相关空军人员政策的修改提供信息。额外的分析表明学位类型和委托来源是确定候选人成功的最重要特征。最终，这项研究可以为未来 SUPT 候选人选择标准和其他相关空军人员政策的修改提供信息。额外的分析表明学位类型和委托来源是确定候选人成功的最重要特征。最终，这项研究可以为未来 SUPT 候选人选择标准和其他相关空军人员政策的修改提供信息。