Model Interpretability and Explainability

Add interpretability and explainability to my ML model.

Model details:
- Model type: [TREE/NEURAL NETWORK/ENSEMBLE]
- Task: [CLASSIFICATION/REGRESSION]
- Stakeholders: [TECHNICAL/BUSINESS/REGULATORY]

Interpretability requirements:
1. Global explanations:
   - Feature importance ranking
   - Partial dependence plots
   - Feature interaction detection
2. Local explanations:
   - SHAP values calculation
   - LIME explanations
   - Counterfactual examples
3. Neural network specific:
   - Attention visualization
   - Grad-CAM for CNNs
   - Integrated gradients
4. Visualization:
   - SHAP summary plots
   - Waterfall charts
   - Force plots
5. Validation:
   - Explanation consistency
   - Fidelity metrics
   - Human evaluation
6. Reporting:
   - Automated explanation reports
   - Non-technical summaries
   - Regulatory documentation

Address both debugging and compliance needs.