iml is an R package that interprets the behavior and explains
predictions of machine learning models.
It implements model-agnostic interpretability methods - meaning they can
be used with any machine learning model.
- Feature importance
- Partial dependence plots
- Individual conditional expectation plots (ICE)
- Accumulated local effects
- Tree surrogate
- LocalModel: Local Interpretable Model-agnostic Explanations
- Shapley value for explaining single predictions
Read more about the methods in the Interpretable Machine Learning book.
Start an interactive notebook tutorial by clicking on this badge
The package can be installed directly from CRAN and the development version from GitHub:
# Stable version
install.packages("iml")
# Development version
remotes::install_github("giuseppec/iml")Changes of the packages can be accessed in the NEWS file.
First we train a Random Forest to predict the Boston median housing
value. How does lstat influence the prediction individually and on
average? (Accumulated local effects)
library("iml")
library("randomForest")
data("Boston", package = "MASS")
rf = randomForest(medv ~ ., data = Boston, ntree = 50)
X = Boston[which(names(Boston) != "medv")]
model = Predictor$new(rf, data = X, y = Boston$medv)
effect = FeatureEffects$new(model)
effect$plot(features = c("lstat", "age", "rm"))
Please check the contribution guidelines
If you use iml in a scientific publication, please cite it as:
Molnar, Christoph, Giuseppe Casalicchio, and Bernd Bischl. "iml: An R package for interpretable machine learning." Journal of Open Source Software 3.26 (2018): 786.
BibTeX:
@article{molnar2018iml,
title={iml: An R package for interpretable machine learning},
author={Molnar, Christoph and Casalicchio, Giuseppe and Bischl, Bernd},
journal={Journal of Open Source Software},
volume={3},
number={26},
pages={786},
year={2018}
}© 2018 - 2022 Christoph Molnar
The contents of this repository are distributed under the MIT license. See below for details:
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This work is funded by the Bavarian State Ministry of Education, Science and the Arts in the framework of the Centre Digitisation.Bavaria (ZD.B)