A Credit Risk Model with Small Sample Data Based on G-XGBoost
Currently existing credit risk models, e.g., Scoring Card and Extreme Gradient Boosting (XGBoost), usually have requirements for the capacity of modeling samples. The small sample size may result in the adverse outcomes for the trained models which may neither achieve the expected accuracy nor disti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.1987707 |