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AI Research
arXiv CS.AI·May 25·5 min read
Proxy-Based Approximation of Shapley and Banzhaf Interactions
TL;DR
Estimating Shapley and Banzhaf interactions in machine learning is challenging due to trade-offs between speed and accuracy. ProxySHAP was developed to efficiently compute these interactions using tree-based proxy models and a residual correction method.
✦ Why It Matters
Engineers can leverage ProxySHAP for more accurate model interpretability in complex machine learning applications.
Key Takeaways
- Utilize ProxySHAP for efficient interaction estimation in models.
- Apply residual correction to enhance accuracy in predictions.
- Benchmark against ProxySPEX and KernelSHAP-IQ for performance insights.
- Explore implications of interaction indices for model explainability.
Full Summary
Shapley and Banzhaf interactions are important for understanding complex relationships in machine learning models, but existing methods struggle with balancing speed and accuracy. ProxySHAP is a new tool that combines the efficiency of tree-based proxy models with a technique called residual correction to improve accuracy.
The authors derived a polynomial-time method to compute exact interaction indices for tree ensembles, avoiding the exponential complexity of previous approaches. They also analyzed how their Maximum Sample Reuse (MSR) strategy mitigates bias without increasing variance significantly.
Extensive tests demonstrated that ProxySHAP sets a new benchmark for approximation quality, particularly in large datasets with thousands of features. It achieved the lowest error rates compared to earlier methods like ProxySPEX and KernelSHAP-IQ, enhancing performance in downstream explainability tasks.
This advancement allows for more reliable insights into model behavior, crucial for developers and researchers.