Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions

Keyphrases

• Financial Institutions 100%
• Credit Evaluation 100%
• Extreme Gradient Boosting Tree 100%
• Classification Accuracy 75%
• Imbalanced Data 50%
• Multi-stage Modeling 50%
• Logistic Regression 25%
• Cluster-based 25%
• Support Vector Machine 25%
• Data Preprocessing 25%
• Undersampling 25%
• Single-stage 25%
• Operative 25%
• Self-organizing Algorithm 25%
• Credit Risk Models 25%
• Assessment Indicators 25%
• XGBoost Classifier 25%
• XGBoost 25%

Computer Science

• Assessment Model 100%
• Extreme Gradient Boosting 100%
• Gradient Boosting Tree 100%
• Classification Accuracy 50%
• Imbalanced Data 50%
• Logistic Regression 25%
• Data Preprocessing 25%
• Support Vector Machine 25%

Mathematics

• Credit Risk 100%
• Boosting Tree 100%
• Support Vector Machine 25%
• Risk Model 25%
• Logistic Regression 25%