Employee Attrition Prediction using Nested Ensemble Learning Techniques

In many industries, including the IT industry, rising employee attrition is a major concern. Hiring a candidate for an unsuitable job because of issues with the employment process can lead to employee attrition. Thus, enhancing the employment process would reduce the attrition rate. This paper aims to investigate the effect of ensemble learning techniques on enhancing the employment process by predicting employee attrition. This paper applied a two-layer nested ensemble model to the IBM HR Analytics Employee Attrition & Performance dataset. The performance of this model was compared to that of the random forest (RF) algorithm as a baseline for comparison. The results showed that the proposed model outperformed the baseline algorithm. The RF model achieved an accuracy of 94.2417%, an F1-score of 94.2%, and an AUC of 98.4%. However, the proposed model had the highest performance. It outperformed with an accuracy of 94.5255%, an F1-score of 94.5%, and an AUC of 98.5%. The performance of the proposed model was compared with that of the baseline comparison algorithm by using a paired t-test. According to the paired t-test, the performance of the proposed model was statistically better than that of the baseline comparison algorithm at the significance level of 0.05. Thus, the two-layer nested ensemble model improved the employee attrition prediction.