Machine learning on human resource analytics predicting employee turnover - Ty, Miriam Marie C. (2017)
Abstract: In this study, we report on an attempt to approach the problem on turnover and attrition using machine learning. In the context of human resources, attrition is referred to as the gradual loss of employees over time. It is also commonly defined as employee turnover. The study conducted comparative analysis on the performances of different machine learning algorithms. From the pre-tuned scores of each model, we saw results with scores better than that of a toss-coin. We found that the best performing algorithm is the Random Forest Classifier, with a score of 99%. This is followed by another tree-based classifier, Decision Trees, with a score of 98%.