Summary of Enhanced Credit Score Prediction Using Ensemble Deep Learning Model, by Qianwen Xing et al.
Enhanced Credit Score Prediction Using Ensemble Deep Learning Model
by Qianwen Xing, Chang Yu, Sining Huang, Qi Zheng, Xingyu Mu, Mengying Sun
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper combines high-performance models like XGBoost, LightGBM, and TabNet to develop a robust credit evaluation system that accurately determines credit score levels. By integrating Random Forest, XGBoost, and TabNet through the stacking technique in ensemble modeling, the approach surpasses the limitations of single models and advances precise credit score prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new model that predicts credit scores by combining XGBoost, LightGBM, and TabNet. This helps banks and financial institutions make better decisions about loans and investments. The paper uses special techniques to compare how well the model works and finds it is very accurate in predicting credit scores. |
Keywords
» Artificial intelligence » Random forest » Xgboost
