Abstract: Machine learning can rapidly generate numerous predicted labels using complex prediction techniques, emerging as an efficient and low-cost labeling solution. However, most machine learning algorithms lack interpretability. This study adopts linear regression as the baseline model and proposes a prediction-powered prediction approach to leverage unlabeled data to enhance prediction performance while ensuring model interpretability. In the proposed approach, we incorporate model averaging to address the uncertainty caused by model, power tuning parameter, and machine learning algorithm selection. Simulation and applications demonstrate its promising performance. 

 

Bio：张新雨，中国科学院数学与系统科学研究院研究员。长期从事统计和计量经济学理论与应用方面的研究工作，在模型平均及其交叉领域取得了多项研究成果，同时将所提出的预测方法应用于实际问题为相关部门的决策提供了参考依据。