Identifying at-risk populations is essential for designing effective energy poverty interventions. Using data from the HILDA Survey, a longitudinal dataset representative of the Australian population, and a multidimensional index of energy poverty, we develop a machine learning model combined...

Identifying at-risk populations is essential for designing effective energy poverty interventions. Using data from the HILDA Survey, a longitudinal dataset representative of the Australian population, and a multidimensional index of energy poverty, we develop a machine learning model combined...

Recent studies focus on machine learning (ML) algorithms for predicting employee churn (ECn) to save probable economic loss, technology leakage, and customer and knowledge transference. However, can human resource professionals rely on algorithms for prediction? Can they decide when the process...