通过使用不同的机器学习方法来评估经理培训计划的有效性

Vedna Sharma, Sourabh Jain
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引用次数: 0

摘要

对管理人员的培训计划进行评估对于检查培训计划的可行性和有效性是非常必要的。由于没有考虑到直接或间接影响的质量特征,传统的对培训经理反馈的分析将变得更不精确或不准确。因此,管理者的反馈分析结果将不会被视为评估培训计划绩效的真实指标。因此,本文提出了一种自动化的解决方案,通过使用两种监督机器学习技术,即支持向量机(SVM)和决策树,来评估提供给管理人员的培训计划的绩效。本文的研究工作将有助于克服现有研究的局限性,提高管理人员培训方案的效率、相关性和可靠性,并为提高绩效提供基本需求,从而提高培训方案的标准并相应地优化培训方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Managers Training Programs Effectiveness Evaluation by using different Machine Learning Approaches
Evaluating the training program conducted for managers is very essential to check the feasibility and effectiveness of the offered training programs.. The traditional analysis of trainee managers feedback about training programs will become more prone to imprecision or inaccuracy, which are caused due to the non-consideration of quality features that will influence either directly or indirectly. Accordingly, the results of feedback analysis managerswill not be considered as a true indicator to evaluate the performance of training program. Henceforth, this paper has proposed an automated solution to evaluate the performance of training programs offered to the managers by using two supervised machine learning techniques namely support vector machine (SVM) and decision tree. The proposed research work will also help to overcome the limitations of the existing studies by enhancing the efficiency, relevancy and reliability of training programs offered to managers and also provide basic needs to enhance the performance, which will also improve the standard of training program and optimize it accordingly.
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