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Found 4 papers, 2 papers with code
Model interpretation using improved local regression with variable importance
A fundamental question on the use of ML models concerns the explanation of their predictions for increasing transparency in decision-making.
MeLIME: Meaningful Local Explanation for Machine Learning Models
Additionally, we introduce modifications to standard training algorithms of local interpretable models fostering more robust explanations, even allowing the production of counterfactual examples.
Reconstructing commuters network using machine learning and urban indicators
Here, we propose an alternative approach using machine learning and 22 urban indicators to predict the flow of people and reconstruct the intercity commuters network.
Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space
For such, they induce interpretable models on the neighborhood of the instance to be explained.
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