Unified Approach to Interpreting Model Predictions with SHAP

In the era of complex and highly accurate machine learning models, a critical challenge has emerged - the tension between model accuracy and interpretability. While advanced models like ensemble methods and deep neural networks can achieve state-of-the-art performance on large, modern datasets, their inner workings often remain opaque, making it difficult for even experts to understand why they make certain predictions.

To address this issue, researchers Scott M. Lundberg and Su-In Lee presented a unified framework for interpreting model predictions, known as SHAP (SHapley Additive exPlanations). This work, published at the 2017 Advances in Neural Information Processing Systems (NeurIPS) conference, offers a novel approach to providing interpretable explanations for complex machine learning models.

Key Contributions of SHAP

  1. Identification of a new class of additive feature importance measures: SHAP introduces a new theoretical framework that unifies several existing interpretability methods, including LIME, Shapley sampling values, and Layer-wise Relevance Propagation. This new class of measures ensures that the explanations have a set of desirable properties, such as consistency and local accuracy.

  2. Theoretical results on the unique solution in this class: The authors prove that there is a unique solution within this new class of additive feature importance measures that satisfies these desirable properties. This solution corresponds to the Shapley values from game theory, which provide a principled way of allocating credit to each input feature.

  3. New methods with improved computational performance and/or better consistency with human intuition: Drawing from the insights gained through the unification of existing methods, the authors present new interpretability techniques that outperform previous approaches in terms of computational efficiency and alignment with human intuition.

The SHAP Framework

The key idea behind SHAP is to assign an importance value to each input feature that contributes to a particular prediction made by a machine learning model. These importance values, known as Shapley values, are computed based on the concept of Shapley values from cooperative game theory.

By leveraging this game-theoretic approach, SHAP is able to provide consistent, accurate, and intuitive explanations for the predictions of a wide range of machine learning models, including complex models like ensemble methods and deep neural networks.

The SHAP framework has been widely adopted and has inspired further research and development in the field of interpretable machine learning. The authors' work has helped to establish a more principled and unified understanding of model interpretability, paving the way for more transparent and trustworthy AI systems.

To learn more about the SHAP framework and how it can be applied to interpret your own machine learning models, I recommend exploring the resources and code available at the SHAP GitHub repository.