相关实验视频

Updated: Jun 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

506

提高自动化信贷决策的透明度和公平性:一种可解释的新型混合机器学习方法

Chioma Ngozi Nwafor1, Obumneme Nwafor2, Sanjukta Brahma3

  • 1Glasgow School for Business and Society, Department of Finance, Accountancy and Risk, Glasgow Caledonia University, Glasgow, Scotland. Chioma.Nwafor@gcu.ac.uk.

Scientific reports
|October 25, 2024
PubMed
概括

结合1DCNN和XGBoost的新混合型号实现了卓越的信用评分准确性. 这种公平和可解释的模型即使删除了年龄和性别等敏感特征,也保持了高性能.

关键词:
和物流回归回归.信用风险 信用风险 信用风险可解释的人工智能一维卷积神经网络的一个维度.沙普利 添加式解释

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