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Application of Mathematical Model Using Random Forest in Performance Appraisal Management of Cadres in Free Trade Zone

机译:基于随机森林的数学模型在自贸区干部绩效考核管理中的应用

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摘要

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. ? 2022 Jie Zhang.
机译:为提高干部绩效科学考核效率,本文首先分析了大数据背景下自贸区干部绩效考核现状,并引入自贸区和随机森林(RF)算法。其次,基于干部评价指标,建立自贸区干部绩效评价体系。最后,实现随机森林算法模型对自贸区干部绩效考核。此外,通过实际数据验证模型的性能,包括获取模型的最佳参数和最重要的指标,以及RF算法与其他模型的性能对比。结果表明:自贸区干部绩效最终分为中、好、合格、优秀4个等级。自贸区干部绩效存在明显职级差异。部分原因是一些合格的干部缺乏强烈的竞争意识和业务能力,不宣传自贸区干部的工作,不及时与群众沟通。在数据处理中,对缺失的18个实验数据进行了补充,得到最佳模型参数如下:NTree = 200,MTry = 1。干部绩效考核最重要的指标是廉政建设、依法办事能力和业务能力。这里得到的RF算法的准确率为71.4%。RF算法对整体样本、训练样本和测试样本的预测准确率分别为94%、96%和86%,高于其他常用模型。得到了分类效果良好的射频算法,为干部绩效考核的科学管理提供了参考。?2022 张杰.

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