A Unified Approach to Interpreting Model Predictions (1705.07874v2)

Abstract: Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.

A Unified Approach to Interpreting Model Predictions

In the paper titled "A Unified Approach to Interpreting Model Predictions," Scott M. Lundberg and Su-In Lee present a comprehensive framework named SHAP (SHapley Additive exPlanations) designed to interpret predictions from complex models. Recognizing the increasing demand for model interpretability in machine learning, this work addresses a vital need by unifying various existing explanation methods under a single theoretical framework rooted in cooperative game theory.

Introduction to the SHAP Framework

The motivation behind the SHAP framework lies in the often-observed trade-off between model accuracy and interpretability. Modern predictive models such as ensemble methods and deep neural networks, while highly accurate, pose significant interpretability challenges. Various methods have been proposed to address this, such as LIME, DeepLIFT, and others, yet the relationships and relative advantages between these methods have remained unclear.

SHAP tackles this problem by introducing additive feature attribution methods. The explanation model in SHAP is a linear function of binary variables, reflecting the presence or absence of feature values. The authors delineate the SHAP values, derived from Shapley values in cooperative game theory, as a unique solution within this class that satisfies properties like local accuracy, missingness, and consistency.

Additive Feature Attribution Methods

The comprehensive framework demonstrates that multiple current methods, including LIME, DeepLIFT, and Layer-Wise Relevance Propagation, share the same additive feature attribution mechanism described by SHAP. This unification has far-reaching implications, as it underscores a common foundation for these methods and allows for direct comparisons and improvements.

1. LIME: SHAP framework aligns with LIME's local linear approximations by formalizing the explanation model's form and connecting it to game theory.
2. DeepLIFT: While initially different in approach, DeepLIFT's recursive explanation methodology can be seen as another instantiation within the additive feature attribution methods when the reference values are interpreted appropriately.
3. Layer-Wise Relevance Propagation: Recognized as equivalent to a subset of DeepLIFT, SHAP framework also incorporates LRP within its unified theoretical base.

Theoretical Basis and Properties

The paper rigorously proves that there is only one method within the additive class that adheres to local accuracy, missingness, and consistency: the SHAP values. The SHAP values are obtained by modeling the explained output as the conditional expectation of the function of interest given the feature values. The work leverages classic cooperative game theory results, ensuring that the derived SHAP values are robust and theoretically justified.

Implementation and Approximation Methods

Despite their theoretical soundness, exact computation of SHAP values is challenging. The authors present several approximation methods to make SHAP practical:

1. Kernel SHAP: An implementation that connects SHAP values to weighted linear regression, offering a model-agnostic estimation method with improved sample efficiency over traditional methods.
2. Deep SHAP: A model-specific variant that adapts DeepLIFT to approximate SHAP values by backpropagating Shapley values through the network's components.

Experimental Evaluation

Evaluations include computational efficiency comparisons and user studies to validate the alignment of SHAP values with human intuition. Kernel SHAP demonstrates superior performance in sample efficiency compared to LIME and traditional Shapley sampling values. Additionally, user studies indicate that SHAP values are more consistent with human explanations, particularly in the context of models where SHAP addresses key shortcomings of other methods.

Implications and Future Directions

The SHAP framework's introduction has several practical and theoretical implications. Practically, it provides a robust method for interpreting complex models, which is essential for domains requiring high interpretability, such as healthcare and finance. Theoretically, it sets the stage for future research to explore faster and more efficient estimation methods and extend the framework to capture more complex interactions and explanation models.

In summary, "A Unified Approach to Interpreting Model Predictions" significantly advances the field of interpretable machine learning by proposing a theoretically sound and practically feasible method for model interpretation. The coherence and robustness of the SHAP framework unify previous methods and pave the way for future innovations in the area of model interpretability. The paper's rigorous approach ensures its findings are both reliable and broadly applicable across various complex models, marking an essential step forward in the interpretability of machine learning models.