Best Practices and Use Cases for SHAP Integration

When to Use SHAP Integration

SHAP integration provides the most value in these scenarios:

High Interpretability Requirements - Healthcare and medical diagnosis systems, financial services (credit scoring, loan approval), legal and compliance applications, hiring and HR decision systems, and fraud detection and risk assessment.

Complex Model Types - XGBoost, Random Forest, and other ensemble methods, neural networks and deep learning models, custom ensemble approaches, and any model where feature relationships are non-obvious.

Regulatory and Compliance Needs - Models requiring explainability for regulatory approval, systems where decisions must be justified to stakeholders, applications where bias detection is important, and audit trails requiring detailed decision explanations.

Performance Considerations

Dataset Size Guidelines:

Small datasets (< 1,000 samples): Use exact SHAP methods for precision

Medium datasets (1,000 - 50,000 samples): Standard SHAP analysis works well

Large datasets (50,000+ samples): Consider sampling or approximate methods

Very large datasets (100,000+ samples): Use batch processing with sampling

Memory Management:

Process explanations in batches for large datasets

Use approximate SHAP methods when exact precision isn't required

Clear intermediate results to manage memory usage

Consider model-specific optimizations (e.g., TreeExplainer for tree models)