
The author introduces the concept of Sparse Explanation Value (SEV) to measure decision sparsity in machine learning models, emphasizing the importance of sparse explanations over globally sparse models.


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Understanding-Sparse-Explanation-Values-in-Machine-Learning-Models


Understanding Sparse Explanation Values in Machine Learning Models


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Machine learning models can provide accurate and faithful explanations without the need for global sparsity, focusing on decision sparsity instead.


sparse-and-faithful-explanations-without-sparse-models-a-new-approach-to-interpretable-machine-learning


Sparse and Faithful Explanations Without Sparse Models: A New Approach to Interpretable Machine Learning



Machine learning models can provide sparse and faithful explanations without the need for global sparsity, enhancing interpretability and decision-making processes.
