xBooster

xBooster converts XGBoost and CatBoost models into interpretable scorecards with SQL deployment capabilities for production systems.

What It Does

Transforms complex gradient boosting models into traditional scorecard formats that financial institutions and regulated industries can understand, validate, and deploy. Bridges the gap between ML performance and regulatory requirements.

Key Capabilities

• Model-to-Scorecard Conversion: Automatically converts XGBoost/CatBoost into point-based scoring systems
• SQL Deployment: Generates production-ready SQL queries for database deployment
• Interval Simplification: Reduces complex tree rules into simple intervals (typically 60-80% fewer rules)
• Statistical Validation: WOE, IV calculations, and comprehensive model diagnostics

Best Used For

• Credit Scoring: Converting ML models into traditional scorecard formats for regulatory approval
• Production Deployment: Moving models from Python to SQL databases without accuracy loss
• Model Validation: Meeting interpretability requirements for financial regulators
• Risk Management: Creating transparent scoring systems that stakeholders can understand and audit
• Legacy System Integration: Deploying modern ML in environments that require traditional scorecard formats

Essential for data scientists who need to deploy high-performance models in regulated environments that demand interpretability.