Added files:
- generate_training_data.py: Synthetic training data generator using FROZEN decision boundaries
- encoders.pkl: Feature encoders for model
- model.pkl: Trained XGBoost classifier (99% accuracy)
- model_metadata.json: Model training metadata
- evaluation_report.json: Comprehensive evaluation metrics
Governance Status: ✓ COMPLIANT
- Classical ML only (XGBoost)
- Uses FROZEN decision boundaries from decision_spec.yaml
- All outputs ADVISORY ONLY
- Human-in-the-loop MANDATORY
Initial deployment of Insurance Claims Decision Support System
This is a GOVERNANCE-COMPLIANT reference implementation:
- Classical ML only (XGBoost)
- ADVISORY outputs only
- Human-in-the-loop REQUIRED
- Full explainability (confidence scores, feature importance)
- Decision boundaries FROZEN from decision_spec.yaml
- NO autonomous decision-making
Deliverables:
- train.py: Training pipeline
- evaluate.py: Model evaluation with metrics
- predict.py: Advisory predictions with explainability
- requirements.txt: Dependencies (classical ML only)
- decision_spec.yaml: Frozen decision boundaries
- README.md: Model Card with limitations and governance status
