Research On The Application Of Weighted Random Forest In Employee Turnover Prediction

Employee turnover,especially the turnover of core employees,is currently one of the major problems faced by many organizations or enterprises.This issue is very important for the normal operation of an enterprise,because it not only affects the sustainability of other employees' work,but also affects the planning of the enterprise and the inheritance of the corporate culture.Therefore,the human resources department is increasingly aware of the importance of employee turnover and a series of chain reactions brought about afterwards,and hopes to obtain the deep reasons affecting employee turnover through effective means,so as to make quick response and minimize the loss.In response to this demand,this study proposes a new turnover prediction model based on weighted random forest,aiming at improving the ability to predict employee turnover.The specific work is as follows:(1)Analyze and studied the main reasons affecting employee turnover,and analyzed the current domestic and foreign research status of employee turnover prediction.(2)This paper summarizes several common employee turnover prediction algorithms and analyzes their advantages and disadvantages.On this basis,the random forest algorithm is introduced emphatically.Random forest has excellent generalization performance and performs well in processing high-dimensional unbalanced data.(3)The feature selection method based on random forest was used to rank the feature importance of all employees,and the important feature subset was screened out,which reduced the dimension of employee feature set.(4)This paper proposes a turnover prediction model based on weighted quadratic random forest.The core idea is to increase the discourse power of sub-classifier in the voting process through F1 value.The algorithm first constructs the basic random forest model through the training samples,and then uses the verification samples to assign weights to the decision trees in the random forest.Finally,the weighted quadratic random forest model is called to classify the test samples and evaluate the generalization performance of the model.(5)through the simulation experiment on the collected real data set and virtual data set,it can be seen that compared with the random forest,C4.5,logistic regression,BP and other algorithms,the weighted quadratic random forest algorithm proposed in the paper has better improvement in various classification evaluation indicators.At the same time,the model can also predict the turnover of employees in the enterprise and find out the core factors that affect the turnover of employees in the enterprise,such as monthly income,overtime work,age,distance from home,years of working in the company,wage growth percentage and so on.This study provides a new analytical method for human resources department to predict employee turnover more accurately,and the experimental results can effectively guide enterprises to reduce employee turnover tendency.