Optimizing Employee Selection in Human Resource Management Using Decision Tree Algorithms

To improve staff selection procedures in human resource management, this research explores the use of sophisticated decision tree algorithms. The project intends to increase the predicted accuracy and effectiveness of finding top candidates by utilizing ensemble techniques like Random Forests and Gradient Boosting. These algorithms were put into practice using IBM SPSS Modeler to manage intricate HR information, expedite feature selection, and improve candidate forecasts. By combining several decision trees, ensemble approaches enable a thorough examination, enhancing the model's resistance to overfitting and attaining high precision. The paper shows that including these decision tree models improves accuracy by 10-15% compared to conventional techniques while lowering selection bias. This paper provides a scalable, data-driven method that highlights the efficacy of machine learning in HR applications. The efficacy of machine learning in HR applications is demonstrated by this research, which provides a scalable, data-driven method to improve hiring choices and the fit between company objectives and candidate selection.