Interpretable Machine Learning for Employee Recruitment Prediction Using Boruta, CatBoost, Lasso, Logistic Regression, NLP, and RFE Feature Selection

Abstract

Employee recruitment is one of the crucial processes in human resource management that has a direct impact on the performance and success of the company. In the digital era, the use of Machine Learning (ML) in candidate selection processes is increasingly prevalent due to its ability to enhance efficiency, accuracy, and transparency. This research is important because conventional recruitment methods often face issues such as subjective bias, slow processing times, and limitations in assessing a candidate’s true potential. ML offers a more objective, data-driven, and faster approach, enabling companies to identify the best candidates more effectively. This study aims to identify the main features that influence recruitment decisions, as well as evaluate the effectiveness and interpretability of several ML models, namely Boruta, CatBoost, Lasso Regression, Logistic Regression, Natural Language Processing (NLP), and Recursive Feature Elimination (RFE). This study uses a dataset consisting of 1,501 samples with 10 features and one class variable (0 = Not Hired, 1 = Hired). The evaluation is carried out based on the ability of each model to identify the features that make the most significant contribution to the classification results. This study has several limitations, particularly the potential bias in the data, such as demographic bias that may be reflected in historical recruitment decisions. This could lead the ML models to replicate or even reinforce such biases. Additionally, the limited dataset size may affect the models' ability to generalize to new data. In the context of this study, the main parameter used to assess the superiority of the model is the most dominant feature or the highest feature produced by each method. The test results show that the Boruta model identifies Gender as the most influential feature, while the CatBoost, Lasso Regression, Logistic Regression, and NLP models consistently place Recruitment Strategy as the most significant feature in predicting candidate eligibility. Meanwhile, the RFE model produces Distance from the Company as the highest feature that influences recruitment decisions. The uniqueness of this study lies in its approach that integrates feature interpretability models within the real-world context of recruitment decision-making. This approach not only emphasizes prediction accuracy but also promotes transparency and a clear understanding of the rationale behind each decision. It supports the development of a fairer and more accountable selection process, particularly by minimizing unconscious bias in data-driven recruitment systems. From a practical standpoint, the findings are highly relevant for human resource professionals, as the identified key features can be used to design more objective selection strategies and enhance the efficiency of candidate evaluations. Therefore, this study makes a tangible contribution to the advancement of modern, technology-based recruitment systems that prioritize fairness and decision-making efficiency. Additionally, the selection of evaluation metrics could be further elaborated to strengthen the analysis, for example by presenting the overall accuracy of each model or comparing them with alternative approaches to provide a more comprehensive view of the models' performance.