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


coremsg

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


title_rewrite


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



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.


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


Understanding Sparse Explanation Values in Machine Learning Models
