Demystifying Turnover Prediction: How ResidualError Characteristics Shape Model Performance

Abstract Employee turnover, particularly the loss of highly skilled personnel, poses a crit-ical challenge for organizations, leading to decreased productivity and increasedrecruitment costs. Several intelligent systems are designed to predict voluntaryemployee churn. These methods typically follow a two-step approach: data col-lection and prediction algorithm design. System performance is often evaluatedusing conventional metrics like accuracy, precision, and recall. However, a cru-cial limitation exists: these metrics primarily assess the algorithm’s effectiveness,potentially neglecting the fundamental importance of data quality. This paperemphasizes the critical role of data quality in employee turnover prediction sys-tems. It is obvious that poor data quality, arising from inadequate preparationor missing essential features, can significantly degrade system performance. Toilluminate this relationship, we propose the analysis of residual errors. This statis-tical approach provides a valuable supplementary tool for advanced performanceanalysis, enabling us to quantify the quality of the data fed into the predictionalgorithm. By incorporating residual error analysis, we can gain a more compre-hensive understanding of prediction systems’s performance and identify potentialdata quality issues. This, could facilitate the development of more robust andreliable employee turnover prediction models.