Predictive Analytics of Employee Attrition using K-Fold Methodologies
Автор: V. Kakulapati, Shaik Subhani
Журнал: International Journal of Mathematical Sciences and Computing @ijmsc
Статья в выпуске: 1 vol.9, 2023 года.
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Currently, every company is concerned about the retention of their staff. They are nevertheless unable to recognize the genuine reasons for their job resignations due to various circumstances. Each business has its approach to treating employees and ensuring their pleasure. As a result, many employees abruptly terminate their employment for no apparent reason. Machine learning (ML) approaches have grown in popularity among researchers in recent decades. It is capable of proposing answers to a wide range of issues. Then, using machine learning, you may generate predictions about staff attrition. In this research, distinct methods are compared to identify which workers are most likely to leave their organization. It uses two approaches to divide the dataset into train and test data: the 70 percent train, the 30 percent test split, and the K-Fold approaches. Cat Boost, LightGBM Boost, and XGBoost are three methods employed for accuracy comparison. These three approaches are accurately generated by using Gradient Boosting Algorithms.
Machine learning, Gradient Boosting Algorithms, K-Fold approaches, Light GBM Boost, and XGBoost
Короткий адрес: https://sciup.org/15019043
IDR: 15019043 | DOI: 10.5815/ijmsc.2023.01.03
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