An interpretable credit risk assessment model with boundary sample identification
Background Interpretability is a key requirement for ensuring that credit risk assessment models are trustworthy and compliant with regulatory standards. Simultaneously, effectively distinguishing between noise samples and boundary samples is crucial for improving the accuracy of credit risk predict...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-06-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-2988.pdf |
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