Calculate SHAP from shapley values
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Calculating SHAP from Shapley Values: A Comprehensive Overview
Introduction to Shapley Values in Machine Learning
Shapley values, originating from cooperative game theory, have become a cornerstone in explainable AI (XAI) for attributing the contribution of individual features in complex machine learning models. They uniquely satisfy a set of axiomatic properties, making them a robust choice for model interpretation . However, calculating exact Shapley values can be computationally intensive, leading to the development of various approximations and extensions.
Kernel SHAP: Efficient Approximation of Shapley Values
Kernel SHAP is a popular method that approximates Shapley values efficiently, especially in high-dimensional spaces. This method assumes feature independence, which can sometimes lead to inaccurate explanations. To address this, an extended version of Kernel SHAP has been developed to handle dependent features, providing more accurate approximations for both linear and non-linear models.
Faith-Shap: Extending Shapley Values to Interactions
While Shapley values traditionally assign attributions to individual features, extending them to feature interactions is complex. Faith-Shap introduces a faithful interaction index by extending linear approximations to higher-order polynomials. This method maintains the standard Shapley axioms (dummy, symmetry, linearity, and efficiency) and provides a unique interaction index, offering a natural generalization of Shapley values to interactions.
Pref-SHAP: Explaining Preferences with Shapley Values
Pref-SHAP is a framework designed to explain preference models using Shapley values. It adapts the value functions for pairwise comparison data and can model context-specific information, such as surface type in sports. This approach has been shown to provide richer and more insightful explanations compared to baseline methods.
SHAP-IQ: Unified Approximation for Shapley Interactions
SHAP-IQ is a novel method for approximating Shapley interactions for any-order feature interactions. It uses a sampling-based approach to compute interaction indices that satisfy key axioms like linearity, symmetry, and the dummy axiom. This method offers theoretical guarantees for approximation quality and variance estimates, making it a robust choice for high-dimensional models.
FastSHAP: Real-Time Shapley Value Estimation
FastSHAP addresses the computational cost of calculating Shapley values by using a learned explainer model. This method estimates Shapley values in a single forward pass, significantly speeding up the process while maintaining high-quality explanations. FastSHAP leverages a learning approach inspired by the Shapley value's weighted least squares characterization and can be trained using standard optimization techniques.
Applications in Pharmaceutical Research
The SHAP methodology has been applied to interpret machine learning models in pharmaceutical research, particularly for compound potency and multi-target activity predictions. By identifying and prioritizing features that determine compound classification and activity, SHAP enhances the interpretability of complex models, including deep neural networks and model ensembles.
Conclusion
Shapley values provide a theoretically sound method for feature attribution in machine learning models. Various extensions and approximations, such as Kernel SHAP, Faith-Shap, Pref-SHAP, SHAP-IQ, and FastSHAP, have been developed to address computational challenges and extend the applicability of Shapley values to interactions and real-time estimation. These advancements enhance the interpretability and practical utility of machine learning models across different domains.
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