Tag: shapley
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When Shapley Values Break: A Guide to Robust Model Explainability
When Shapley Values Break: A Guide to Robust Model Explainability Shapley Values are one of the most common methods for explainability, yet they can be misleading. Discover how to overcome these limitations to achieve better insights. The post When Shapley Values Break: A Guide to Robust Model Explainability appeared first on Towards Data Science. Alon…
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Introducing ShaTS: A Shapley-Based Method for Time-Series Models
Introducing ShaTS: A Shapley-Based Method for Time-Series Models Why you should not explain your time-series data with tabular Shapley methods The post Introducing ShaTS: A Shapley-Based Method for Time-Series Models appeared first on Towards Data Science. Manuel Franco de la Peña Go to original source
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Mathematically rigorous proofs for Shapley explanations
Mathematically rigorous proofs for Shapley explanations arXiv:2510.03281v1 Announce Type: new Abstract: Machine Learning is becoming increasingly more important in today’s world. It is therefore very important to provide understanding of the decision-making process of machine-learning models. A popular way to do this is by looking at the Shapley-Values of these models as introduced by Lundberg…
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Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models
Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models arXiv:2506.13900v1 Announce Type: new Abstract: Cooperative game theory has become a cornerstone of post-hoc interpretability in machine learning, largely through the use of Shapley values. Yet, despite their widespread adoption, Shapley-based methods often rest on axiomatic justifications whose relevance to feature attribution remains…
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SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation
SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation arXiv:2505.08198v1 Announce Type: new Abstract: Explainable artificial intelligence (XAI) is essential for trustworthy machine learning (ML), particularly in high-stakes domains such as healthcare and finance. Shapley value (SV) methods provide a principled framework for feature attribution in complex models but incur high computational costs,…
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Suboptimal Shapley Value Explanations
Suboptimal Shapley Value Explanations arXiv:2502.12209v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) have demonstrated strong capacity in supporting a wide variety of applications. Shapley value has emerged as a prominent tool to analyze feature importance to help people understand the inference process of deep neural models. Computing Shapley value function requires choosing a baseline…
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On Model Extrapolation in Marginal Shapley Values
On Model Extrapolation in Marginal Shapley Values arXiv:2412.13158v1 Announce Type: new Abstract: As the use of complex machine learning models continues to grow, so does the need for reliable explainability methods. One of the most popular methods for model explainability is based on Shapley values. There are two most commonly used approaches to calculating Shapley…