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Muthanna Journal of Pure Science最新文献

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"Predicting Absenteeism at Work Using Machine Learning Algorithms “使用机器学习算法预测工作缺勤
Pub Date : 1900-01-01 DOI: 10.52113/2/07.01.2020/1-12
Samir Qaisar Ajmi
"To work in the commercial environment, the company needs to be a major competitor in the business market, which depends mainly on the company's resources. One of the most important resources is the employees. Based on that, the absence of the employees from work leads to deterioration and reduce production in the institutions which leads to heavy losses. There are many reasons why employees are absent from work. Those may include health problems and social occasions. The purpose of this paper was to apply machine learning techniques to predict the absenteeism at work. There are four methods have been used in this research ( neural network(NN) technique ,decision tree (DT) technique, support vector machine (SVM) technique and logistic regression (LR) technique. . decision tree model has the highest accuracy equals to 83.33% with AUC 0.834 and the support vector machine has the lowest accuracy equals to 68.47 % with AUC 0.760."
“要在商业环境中工作,公司需要成为商业市场的主要竞争对手,这主要取决于公司的资源。最重要的资源之一就是员工。在此基础上,员工的缺勤会导致事业单位的恶化和生产减少,从而导致严重的损失。员工旷工的原因有很多。这些可能包括健康问题和社交场合。本文的目的是应用机器学习技术来预测工作中的缺勤情况。本研究采用了神经网络(NN)技术、决策树(DT)技术、支持向量机(SVM)技术和逻辑回归(LR)技术。决策树模型准确率最高,为83.33%,AUC为0.834;支持向量机准确率最低,为68.47%,AUC为0.760。
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Muthanna Journal of Pure Science
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