Harnessing Adaptive Intelligence for Strategic Human Capital Retention

The productivity of an organization is negatively impacted by employee attrition, which increases operational costs and hinders the effectiveness of human resource (HR) management systems. To explore this seemingly unresolvable problem, the study introduces a new solution using the TabNet deep learning model because it suits tabular data and provides both accuracy and interpretability. Unlike traditional models which require extensive manual feature engineering where low transparency renders the model impractical for HR use, TabNet does not suffer from such limitations. The model’s sequential attention mechanism enables dynamic feature selection along with offering insight into the prediction process while still retaining predictive performance. This research seeks to predict employee retention using HR records that include demographic information, job satisfaction levels, and work attendance patterns. Standard performance evaluation metrics were used to evaluate the TabNet model, including accuracy, F1 score, precision, recall, and log loss. The results were exceptional, achieving 97.2% accuracy, 98.1% F1 score, 99.0% recall, and 1.47 log loss outperforming traditional machine learning techniques. The results demonstrate the model’s ability to help HR departments shift from reactive to proactive workforce management, accurately predicting high-risk cases of attrition and aiding in the formulation of retention strategies, validating the model's ability.