A Unified Approach to Interpreting Model Predictions
May 22, 2017 · View a PDF of the paper titled A Unified Approach to Interpreting Model Predictions, by Scott Lundberg and Su-In Lee
4.2 Model-Specific Approximations While Kernel SHAP improves the sample efficiency of model-agnostic estimations of SHAP values, by restricting our attention to specific model types, we …
The score of a feature value indicates how relevant it is for this output label. One of the most popular attribution scores is the so-called SHAP score [Lundberg and Lee, 2017; Lundberg, S. …
SHAP (Lundberg and Lee (2017)) is a popular method for measuring variable importance in machine learning models. In this paper, we study the algorithm used to estimate SHAP scores …
A Unified Approach to Interpreting Model Predictions - NeurIPS
Authors Scott M Lundberg, Su-In Lee Abstract Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the …
Machine Learning (ML) is becoming increasingly popular in fluid dynamics. Powerful ML algorithms such as neural networks or ensemble methods are notoriously difficult to interpret. …
A Perspective on Explainable Artificial Intelligence Methods: SHAP …
May 3, 2023 · eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to …
erg & Lee, 2017; Mitchell et al., 2022). The Kernel SHAP method is especially popular, as it performs well in practice for a variety of models, requiring just a accurate estimates to φ1, . . . , …
by Lundberg and Lee (2017) Shapley values are the only solution that satisfies properties of Efficiency, Symmetry, Dummy and Additivity. Proposed KernelSHAP, an alternative, kernel …
In this paper, we focus on SHAP values (Lundberg and Lee 2017) – Shapley values (Shapley 1953) with a condi-tional expectation of the model prediction as the set func-tion. Shapley …
4.2 Model-Specific Approximations While Kernel SHAP improves the sample efficiency of model-agnostic estimations of SHAP values, by restricting our attention to specific model types, we …
A Perspective on Explainable Artificial Intelligence Methods: SHAP …
Jun 17, 2024 · Abstract eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods …
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