| Title | A Machine Learning Study on the Model Performance of Human Resources Predictive Algorithms |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Shi, Yong |
| Conference Name | 2022 4th International Conference on Applied Machine Learning (ICAML) |
| Date Published | jul |
| Keywords | Algorithm, Biological system modeling, composability, data mining, Data preprocessing, Decision Tree, ecological, machine learning, machine learning algorithms, Model, Predictive models, privacy, pubcrawl, resilience, Resiliency, statistical analysis, Support vector machines |
| Abstract | A good ecological environment is crucial to attracting talents, cultivating talents, retaining talents and making talents fully effective. This study provides a solution to the current mainstream problem of how to deal with excellent employee turnover in advance, so as to promote the sustainable and harmonious human resources ecological environment of enterprises with a shortage of talents.This study obtains open data sets and conducts data preprocessing, model construction and model optimization, and describes a set of enterprise employee turnover prediction models based on RapidMiner workflow. The data preprocessing is completed with the help of the data statistical analysis software IBM SPSS Statistic and RapidMiner.Statistical charts, scatter plots and boxplots for analysis are generated to realize data visualization analysis. Machine learning, model application, performance vector, and cross-validation through RapidMiner's multiple operators and workflows. Model design algorithms include support vector machines, naive Bayes, decision trees, and neural networks. Comparing the performance parameters of the algorithm model from the four aspects of accuracy, precision, recall and F1-score. It is concluded that the performance of the decision tree algorithm model is the highest. The performance evaluation results confirm the effectiveness of this model in sustainable exploring of enterprise employee turnover prediction in human resource management. |
| DOI | 10.1109/ICAML57167.2022.00082 |
| Citation Key | shi_machine_2022 |
A Machine Learning Study on the Model Performance of Human Resources Predictive Algorithms