Research on Employee Performance Evaluation Based on Information Gain and Bagging Ensemble Learning Algorithm

Employee performance evaluation is an important part of enterprise management. Its accuracy directly affects the rationality of human resource allocation and the overall efficiency of the enterprise. This paper proposes an employee performance evaluation method based on information gain and Bagging ensemble learning algorithm, aiming to improve the accuracy and reliability of performance evaluation. Firstly, the feature selection of employee performance evaluation data is conducted through the information gain method, and the most influential features for performance evaluation are screened out, thereby reducing the data dimension and improving the training efficiency of the model. Then, multiple base learners are constructed using the Bagging ensemble learning algorithm, and the integration of base learners is used to improve the generalization ability and stability of the model. Experimental results show that this method has higher evaluation accuracy and stability on multiple real datasets than the traditional single model method, and can effectively identify outstanding employees and those who need improvement. In addition, the interpretability of the model is analyzed in this paper, providing more intuitive decision support for managers. This research provides a scientific and efficient employee performance evaluation tool for enterprises, which helps to improve enterprise management level and employee incentive effect.