基于XGBoost和LightGBM算法的贷款违约预测模型分析与比较

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摘要

针对现阶段信息不对称和不可控因素导致的贷款违约,本文将使用XGBoost和LightGBM两种算法模型,对申请人的相关信息进行提取和筛选,构建贷款违约预测模型,预测贷款违约情况。并对两种不同模型进行比较和评价,为金融机构选择和构建贷款违约预测模型提供数据参考,在一定程度上降低自身风险和银行损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis and Comparison of Loan Default Prediction Models Based on XGBoost and LightGBM Algorithm
Based on the default loans caused by information asymmetry and uncontrollable factors at this stage, this paper will use two algorithm models, XGBoost and LightGBM, to extract and screen the relevant information of the applicant and build a loan default prediction model to predict the default situation of the loan. And the two different models were compared and evaluated to provide data reference for financial institutions to select and build the loan default prediction model to reduce their risks and bank losses to a certain extent.
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