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iml  

Interpretable Machine Learning
View on CRAN: Click here


Download and install iml package within the R console
Install from CRAN:
install.packages("iml")

Install from Github:
library("remotes")
install_github("cran/iml")

Install by package version:
library("remotes")
install_version("iml", "0.11.4")



Attach the package and use:
library("iml")
Maintained by
Giuseppe Casalicchio
[Scholar Profile | Author Map]
First Published: 2018-03-13
Latest Update: 2022-09-08
Description:
Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) , accumulated local effects plots described by Apley (2018) , partial dependence plots described by Friedman (2001) , individual conditional expectation ('ice') plots described by Goldstein et al. (2013) , local models (variant of 'lime') described by Ribeiro et. al (2016) , the Shapley Value described by Strumbelj et. al (2014) , feature interactions described by Friedman et. al and tree surrogate models.
How to cite:
Giuseppe Casalicchio (2018). iml: Interpretable Machine Learning. R package version 0.11.4, https://cran.r-project.org/web/packages/iml. Accessed 09 Apr. 2025.
Previous versions and publish date:
0.2.1 (2018-03-13 18:14), 0.3.0 (2018-04-10 10:22), 0.4.0 (2018-04-27 18:07), 0.5.1 (2018-05-15 09:36), 0.6.0 (2018-08-17 15:00), 0.7.0 (2018-09-11 17:20), 0.7.1 (2018-10-03 11:50), 0.8.0 (2018-12-20 14:10), 0.8.1 (2019-01-02 14:10), 0.9.0 (2019-02-05 19:43), 0.10.0 (2020-03-26 11:20), 0.10.1 (2020-09-24 14:30), 0.11.0 (2022-05-12 23:10), 0.11.1 (2022-09-08 11:53), 0.11.2 (2024-03-29 08:20), 0.11.3 (2024-04-27 19:00)
Other packages that cited iml R package
View iml citation profile
Other R packages that iml depends, imports, suggests or enhances
Complete documentation for iml
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