Comparative analysis of boosting algorithms for predicting personal default
Accurately predicting personal default risk is crucial for financial institutions to manage credit risk effectively. This study conducts a comparative analysis of the performance of boosting algorithms, including AdaBoost, XGBoost, LightGBM, and CatBoost, in predicting personal defaults. The dataset...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Cogent Economics & Finance |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23322039.2025.2465971 |
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