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Understanding Global Feature Contributions With Additive Importance Measures.

, , и . NeurIPS, (2020)

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James M Scott

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Feature Removal Is a Unifying Principle for Model Explanation Methods., , и . CoRR, (2020)Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges., , , , и . KDD, стр. 3511-3512. ACM, (2020)An Implicit Representation of Chordal Comparabilty Graphs in Linear-Time., , , , и . WG, том 4271 из Lecture Notes in Computer Science, стр. 168-178. Springer, (2006)Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data., , и . CoRR, (2018)Algorithms to estimate Shapley value feature attributions., , , и . CoRR, (2022)Fixing Model Bugs with Natural Language Patches., , , и . EMNLP, стр. 11600-11613. Association for Computational Linguistics, (2022)Summarize with Caution: Comparing Global Feature Attributions., , , , , , , и . IEEE Data Eng. Bull., 44 (4): 14-27 (2021)Explainable Machine Learning for Science and Medicine.. University of Washington, USA, (2019)Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning., , , , и . ICLR, OpenReview.net, (2022)Understanding Global Feature Contributions With Additive Importance Measures., , и . NeurIPS, (2020)