iml: interpretable machine learning R package
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README.md

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iml: interpretable machine learning

iml is an R package that interprets the behaviour and explains predictions of machine learning models. It implements model-agnostic interpretability methods - meaning they can be used with any machine learning model.

Currently implemented:

  • Feature importance
  • Partial dependence plots
  • Individual conditional expectation plots (ICE)
  • 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

Tutorial

Start an interactive notebook tutorial by clicking on the badge: Binder

Installation

The package can be installed directly from CRAN and the develoopment version from github:

# Stable version
install.packages("iml")
# Development version
devtools::install_github("christophM/iml")

News

Changes of the packages can be accessed in the NEWS file shipped with the package.

Examples

First we train a randomForest to predict the Boston median housing value. How does lstat influence the prediction individually and on average? (Partial dependence plot and ICE)

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)
pdp.obj = Partial$new(model, feature = "lstat")
pdp.obj$plot()

Contribute

Please check the contribution guidelines

License

© 2018 Christoph Molnar

The contents of this repository are distributed under the MIT license. See below for details:

The MIT License (MIT)

Copyright (c) 2018 Christoph Molnar

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Python Implementation

Referring to https://github.com/datascienceinc/Skater

Funding

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)