iml
R package iml: Interpretable Machine Learning. Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) <arXiv:1801.01489>, accumulated local effects plots described by Apley (2018) <arXiv:1612.08468>, partial dependence plots described by Friedman (2001) <http://www.jstor.org/stable/2699986>, individual conditional expectation (’ice’) plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>, local models (variant of ’lime’) described by Ribeiro et. al (2016) <arXiv:1602.04938>, the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>, feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
Sorted by year (- Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Mathematical optimization in classification and regression trees (2021)
- Arya, Vijay; Bellamy, Rachel K. E.; Chen, Pin-Yu; Dhurandhar, Amit; Hind, Michael; Hoffman, Samuel C.; Houde, Stephanie; Liao, Q. Vera; Luss, Ronny; Mojsilović, Aleksandra; Mourad, Sami; Pedemonte, Pablo; Raghavendra, Ramya; Richards, John T.; Sattigeri, Prasanna; Shanmugam, Karthikeyan; Singh, Moninder; Varshney, Kush R.; Wei, Dennis; Zhang, Yunfeng: AI Explainability 360: an extensible toolkit for understanding data and machine learning models (2020)
- Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
- Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
- Hubert Baniecki; Przemyslaw Biecek: modelStudio: Interactive Studio with Explanations for ML Predictive Models (2019) not zbMATH
- Aleksandra Grudziaz, Alicja Gosiewska, Przemyslaw Biecek: survxai: an R package for structure-agnostic explanations of survival models (2018) not zbMATH