R package lime: Local Interpretable Model-Agnostic Explanations. When building complex models, it is often difficult to explain why the model should be trusted. While global measures such as accuracy are useful, they cannot be used for explaining why a model made a specific prediction. ’lime’ (a port of the ’lime’ ’Python’ package) is a method for explaining the outcome of black box models by fitting a local model around the point in question an perturbations of this point. The approach is described in more detail in the article by Ribeiro et al. (2016) <arXiv:1602.04938>.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
- Szymon Maksymiuk, Alicja Gosiewska, Przemyslaw Biecek: Landscape of R packages for eXplainable Artificial Intelligence (2020) arXiv
- Hubert Baniecki; Przemyslaw Biecek: modelStudio: Interactive Studio with Explanations for ML Predictive Models (2019) not zbMATH
- Sellereite, Nikolai; Jullum, Martin: shapr: An R-package for explaining machine learning models with dependence-aware Shapley values (2019) not zbMATH