The Shapley value has become the basis for several methods that attribute the prediction of a machine-learning model on an input to its base features. The use of the Shapley value is justified by citing the uniqueness result from (Shapley, 1953), which shows that it is the only method ...
This value function has been used in the sensitivity analysis of neural networks in Sundararajan and Najmi (2019) and in the DeepLIFT explanation model (Shrikumar et al., 2017). Because the value function (5) produces Shapley values which depend on the initial evaluation point x0, Mase et ...
Sundararajan M, Najmi A (2019) The many Shapley values for model explanation. In: Proceedings of the ACM conference. ACM, New York Google Scholar Tian J, Pearl J (2002) A general identification condition for causal effects. In: Eighteenth national conference on artificial intelligence. American...
(2019). The many Shapley values for model explanation. In Proceedings of the ACM conference. New York: ACM. Tian, J., & Pearl, J. (2002). A general identification condition for causal effects. In Eighteenth national conference on artificial intelligence (pp. 567–573). Menlo Park, CA: ...
The potential of the Shapley-value, Chapter 9 in the shapley-value: essays in honor of Lloyd S (1988). "Values of Smooth Nonatomic Games: The Method of Multilinear Approximation," in The Shapley Value: Essays in Honor of Lloyd S. Shapley (A. ... S Hart,A Mas-Colell 被引量: 24发表...
shap.plots.bar(shap_values) SHAP has specific support for natural language models like those in the Hugging Face transformers library. By adding coalitional rules to traditional Shapley values we can form games that explain large modern NLP model using very few function evaluations. Using this funct...
shapr — Prediction Explanation with Dependence-Aware Shapley Values. Homepage: https://norskregnesentral.github.io/shapr/, https://github.com/NorskRegnesentral/shapr/ Report bugs for this package: https://github.com/NorskRegnesentral/shapr/iss ... Resources Readme License View license ...
We use a deep neural network architecture and interpret the model results through the spatial pattern of SHAP values. In doing so, we can understand the model prediction on a hierarchical basis, looking at how the predictor set controls the overall susceptibility as well as doing the same at ...
The Shapley value probably is the most eminent single-point solution concept for TU-games. In its standard characterization, the null player property indicates the absence of solidarity among the players. First, we replace the null player property by a new axiom that guarantees null players non-...
SHAP can use Shapley values to explain the complex relationships between inputs and outcomes. The SHAP interpreter is based on the Shapley value theory, which explains the prediction results of the model by calculating the contribution of each feature to the model output. It not only reflects the...