xRiskLab/xBooster
Explainable Boosted Scoring with Python: turning XGBoost, LightGBM, and CatBoost into explainable scorecards
This tool helps financial risk analysts and credit scoring specialists convert complex loan application data into a simple, explainable credit scorecard. You input applicant information and it outputs a clear, points-based credit score, making it easy to understand why a loan was approved or denied. It's designed for professionals who need to transparently assess credit risk and explain their decisions.
Available on PyPI.
Use this if you need to transform intricate credit risk models into a straightforward, interpretable scoring system for credit decisions or fraud detection.
Not ideal if your primary goal is building raw, uninterpreted machine learning models without the need for human-readable scorecards or clear explanations.
Stars
54
Forks
15
Language
Python
License
MIT
Category
Last pushed
Jan 04, 2026
Commits (30d)
0
Dependencies
9
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