Search Results for author:
Found 3 papers, 2 papers with code
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.
NLS: an accurate and yet easy-to-interpret regression method
We develop NLS (neural local smoother), a method that is complex enough to give good predictions, and yet gives solutions that are easy to be interpreted without the need of using a separate interpreter.
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
