Research on human salary management and performance optimization system under machine learn-ing
Based on the fact that the current research is not ideal for human salary management and per-formance optimization,a model which introduces machine learning is constructed to improve salary and per-formance.Based on XGBoost and GBDT algorithm,the influence of subjective and objective factors on sala-ry is studied.RMSE error analysis is carried out on the model accuracy rate through optimization,and the accuracy rate of XGBoost algorithm model is RMSE22.36 and that of GBDT algorithm model is 39.85.The analytic hierarchy process is used to study and evaluate the weight of each performance indicator,which re-alizes the scientific setting of performance indicators and the dynamic adjustment of data monitoring under the framework of machine learning,and verifies the effectiveness of this model for the research of human salary management and performance optimization system.
machine learningsalary managementperformance optimizationdynamic adjustmentana-lytic hierarchy process
万方数据
维普
