CXPlain: Causal Explanations for Model Interpretation under Uncertainty

NeurIPS 2019 Patrick SchwabWalter Karlen

Feature importance estimates that inform users about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and interpreting machine-learning models. However, providing fast and accurate estimates of feature importance for high-dimensional data, and quantifying the uncertainty of such estimates remain open challenges... (read more)

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