Boosting(机器学习)
计算机科学
索引(排版)
梯度升压
消费(社会学)
人工智能
中国
计量经济学
财务
经济
政治学
社会学
随机森林
社会科学
万维网
法学
作者
Linjiang Zhou,Xiaochuan Shi,Yaxiong Bao,Lihua Gao,Chao Ma
标识
DOI:10.1016/j.frl.2023.104489
摘要
Recently, the role of digital finance in promoting consumer upgrades has become increasingly evident. By applying boosting trees and Shapley values, we proposed an explainable artificial intelligence method to obtain more effective analysis results than those obtained using linear regression models. We studied the China Household Finance Survey data and the Digital Financial Inclusion Index of Peking University, which includes data from 34,643 and 40,013 households in 2019 and 2017, respectively. Our research shows that, the explainable artificial intelligence techniques can provide more innovative economic insights.
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