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Found 4 papers, 2 papers with code
Diverse Explanations from Data-driven and Domain-driven Perspectives for Machine Learning Models
Explanations of machine learning models are important, especially in scientific areas such as chemistry, biology, and physics, where they guide future laboratory experiments and resource requirements.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Shapley Based Residual Decomposition for Instance Analysis
In this paper, we introduce the idea of decomposing the residuals of regression with respect to the data instances instead of features.
Exploring the cloud of feature interaction scores in a Rashomon set
Thus, we recommend exploring feature interaction strengths in a model class of approximately equally accurate predictive models.
Variance Tolerance Factors For Interpreting ALL Neural Networks
Black box models only provide results for deep learning tasks, and lack informative details about how these results were obtained.
