A Prediction Method Based on Extreme Gradient Boosting Tree Model and its Application

Autor:	Yongchang Lao, Jiakai Zhou, Fangzhong Qi, Xiaobao Fang
Rok vydání:	2021
Předmět:	History Computer science Extreme gradient boosting Algorithm Decision tree model Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1995:012017
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1995/1/012017
Popis:	Improving the accuracy of financing risk prediction is of great significance to the healthy development of grid enterprises. Taking a provincial-level power grid company as the research object, the financing risk index system is constructed by considering multiple dimensions, and the monthly financing risk index RI of power grid enterprises from 2015-2018 is determined based on entropy weight and comprehensive index method, while the financing risk prediction model is constructed with the help of extreme gradient boosting tree model. The empirical results show that compared with support vector regression and BP neural network models, the financing risk prediction model constructed based on the extreme gradient boosting model has an excellent performance in terms of prediction accuracy and stability.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b6a90a6f44a73a30ac2bf288562afb25 https://doi.org/10.1088/1742-6596/1995/1/012017 Zobrazit plný text záznamu