"Machine learning in the boardroom: Gender diversity prediction using b" by Haroon ur Rashid Khan, Waqas Bin Khidmat et al.
This paper addresses the crucial issue of boardroom diversity and proposes a novel approach utilizing machine learning to predict gender diversity on the boards of Chinese publicly-traded companies from 2008 to 2017. The study employs tree-based boosting with under-sampling as the machine learning technique. Various tree-based boosting techniques are utilized, and the evaluation is based on accuracy, precision, recall, F1 scores, and ROC scores. The findings reveal that extreme Gradient Boosting (XGBoost) with undersampling outperforms other models in terms of predictive performance. Moreover,...
Source: uow.edu.au