Zhang J. Application of Mathematical Model Using Random Forest in Performance Appraisal Management of Cadres in Free Trade Zone.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022;
2022:6964582. [PMID:
35990147 PMCID:
PMC9391096 DOI:
10.1155/2022/6964582]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/01/2022] [Indexed: 11/17/2022]
Abstract
To improve the efficiency of scientific assessment of cadre performance, first, this work analyzes the current situation of cadre performance appraisal in the free trade zone under the background of big data, and introduces the free trade zone and Random Forest (RF) algorithm. Second, based on the cadre evaluation index, this work establishes the cadre performance evaluation system of the free trade zone. Finally, the random forest algorithm model is implemented for the performance evaluation of cadres in the free trade zone. Additionally, the model's performance is verified with the actual data, including the acquisition of the best parameters and the most important indicators of the model and the performance comparison between the RF algorithm and other models. The results show that the performance of cadres in the free trade zone is finally divided into four grades: medium, good, qualified, and excellent. There are obvious grade differences in the performance of cadres in the free trade zone. Partly because some qualified cadres lack a strong sense of competition and professional competence, do not publicize the work of cadres in the free trade zone, and do not communicate with the masses in time. In the data processing, 18 missing experimental data were supplemented, and the best model parameters were obtained as follows: NTree = 200, MTry = 1. The most important indicators of cadre performance evaluation are the construction of a clean and honest government, the ability to act in accordance with the law and the professional ability. The accuracy of the RF algorithm obtained here is 71.4%. The prediction accuracy of the RF algorithm for the overall sample, training sample, and test sample is 94%, 96%, and 86%, respectively, which are higher than those of other common models. A RF algorithm with good classification effect is obtained and this work provides a reference for the scientific management of cadre performance appraisal.
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