Credit Scoring XGBoost

Summary

Model Details

• Model type: XGBoost classifier
• Task: Binary classification (default risk)
• Input: Tabular features engineered from Home Credit raw tables
• Output: Probability of default
• Artifact: credit_scoring_xgb.pkl

Intended Use

• Demonstrate a credit scoring pipeline with MLflow tracking
• Provide a lightweight baseline for experimentation

Limitations

• Not validated for real-world credit decisions
• No fairness or regulatory compliance audit
• Performance depends on data preprocessing used in the training pipeline

How to Use

Input Expectations

• The model expects the same feature engineering used in the training pipeline.
• See the project notebooks for the exact preprocessing steps and column names.

Reproducibility

• Training code and experiments are tracked in the main project repo:
  • src/train.py
  • notebooks/01_data_preparation.ipynb
  • notebooks/03_model_comparison.ipynb

Training Data

Metrics