美国海军陆战队的消耗预测:比较四种预测方法
Juan Manuel Alzate Vanegas1, William Wine2, Fritz Drasgow1
1Department of Psychology, University of Illinois, Champaign, Illinois, USA.
概括
机器学习模型,包括分类树和随机森林,在预测美国海军陆战队 (USMC) 延迟招募计划中的自愿退伍方面表现优于后勤回归.
科学领域:
- 心理评估 心理评估
- 在行为科学中的机器学习.
- 选择军事人员的军事人员.
背景情况:
- 在军事计划中,训练消耗带来了重大挑战.
- 美国海军陆战队 (USMC) 延迟征兵计划面临着消耗问题.
- 预测建模可以优化招聘和保留策略.
研究的目的:
- 将物流回归的预测性能与机器学习模型 (分类树,随机森林) 进行比较.
- 用定制适应性人格评估系统 (TAPAS) 评分来评估模型在预测训练磨损的准确性.
- 根据错误分类错误类型和磨损原因分析性能.
主要方法:
- 利用后勤回归,分类树和随机森林进行预测建模.
- 员工从量身定制的适应性人格评估系统 (TAPAS) 中得分.
- 评估了预测退伍率的模型,该模型是在延迟入伍计划的分层50%退伍率样本中进行了预测.
主要成果:
- 与物流回归相比,机器学习模型显示出更高的预测性能.
- 这种超越表现在预测自愿性消耗方面尤为明显.
- 对各种消耗原因和错误分类类型的模型有效性进行了分析.
结论:
- 机器学习分类模型为预测军事训练磨损提供了增强的能力.
- 当使用先进的机器学习分析TAPAS分数时,可以提高消耗预测的准确性.
- 这些发现支持将机器学习整合到优化美军陆战队招聘和培训流程中.
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