Efficient and effective service delivery in Public Administration (PA) relies on the development and utilization of key performance indicators (KPIs) for evaluating and measuring performance. This paper presents an innovative framework for KPI construction within performance evaluation systems, leveraging Random Forest algorithms and variable importance analysis. The proposed approach identifies key variables that significantly influence PA performance, offering valuable insights into the critical factors driving organizational success. By integrating variable importance analysis with expert consultation, relevant KPIs can be systematically developed, ensuring that improvement strategies address performance-critical areas. The framework incorporates continuous monitoring mechanisms and adaptive phases to refine KPIs in response to evolving administrative needs. This study aims to enhance PA performance through the application of machine learning techniques, fostering a more agile and results-driven approach to public administration.

翻译：公共行政（PA）的高效服务提供依赖于关键绩效指标（KPIs）的制定与运用，以评估和衡量绩效。本文提出了一种绩效评估体系内KPI构建的创新框架，该框架利用随机森林（Random Forest）算法与变量重要性分析。所提出的方法识别出对公共行政绩效有显著影响的关键变量，为驱动组织成功的关键因素提供了有价值的洞见。通过将变量重要性分析与专家咨询相结合，可以系统地制定相关KPI，确保改进策略针对绩效关键领域。该框架纳入了持续监测机制和自适应阶段，以根据不断变化的行政需求优化KPI。本研究旨在通过应用机器学习技术提升公共行政绩效，促进一种更敏捷、更注重结果的公共行政方法。