关键词: 离职预测, 不平衡数据, 随机森林

Abstract: Under the background of the deepening of the reform of electric power system, the electric power companies in the remote areas of western China are faced with more serious personnel loss. Employee turnover prediction has attracted more and more attention in power companies. However, traditional prediction algorithms cannot effectively solve the imbalance problem of employee turnover data set in power companies. Based on this, this paper proposes a random forest algorithm based on interval variables, using the human resources data set of Qinghai Electric Power Company from 2009 to 2017 for empirical analysis, and comparing it with the prediction results of decision trees, support vector machines, and random forest algorithms. The results show that the algorithm is more suitable for solving the problem of imbalance data and has higher prediction accuracy. At the same time, the important characteristics of employee turnover are analyzed; and it can provide decision-making basis for the human resource management of related power companies.

Key words: turnover prediction, imbalanced data, random forest