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https://github.com/giuseppec/iml

iml: interpretable machine learning R package
https://github.com/giuseppec/iml

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iml: interpretable machine learning R package

Host: GitHub
URL: https://github.com/giuseppec/iml
Owner: giuseppec
License: other
Created: 2017-10-25T11:36:53.000Z (about 7 years ago)
Default Branch: main
Last Pushed: 2024-05-22T04:09:03.000Z (7 months ago)
Last Synced: 2024-05-22T11:25:15.719Z (7 months ago)
Language: R
Homepage: https://giuseppec.github.io/iml/
Size: 802 MB
Stars: 487
Watchers: 23
Forks: 88
Open Issues: 55
Metadata Files:
- Readme: README.Rmd
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codemeta: codemeta.json

Awesome Lists containing this project

jimsghstars - giuseppec/iml - iml: interpretable machine learning R package (R)

README

        ---

output: github_document

---

[![tic](https://github.com/christophM/iml/workflows/tic/badge.svg?branch=master)](https://github.com/christophM/iml/actions)

[![CRAN Status Badge](http://www.r-pkg.org/badges/version/iml)](https://CRAN.R-project.org/package=iml)

[![CRAN Downloads](http://cranlogs.r-pkg.org/badges/grand-total/iml)](https://CRAN.R-project.org/package=iml)

[![codecov.io](https://codecov.io/github/christophM/iml/coverage.svg?branch=master)](https://codecov.io/github/christophM/iml?branch=master)

[![DOI](http://joss.theoj.org/papers/10.21105/joss.00786/status.svg)](https://doi.org/10.21105/joss.00786)

# iml

 `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.

## Features

- 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](https://christophm.github.io/interpretable-ml-book/agnostic.html) book.

## Tutorial

Start an interactive notebook tutorial by clicking on this badge [![Binder](http://mybinder.org/badge.svg)](http://beta.mybinder.org/v2/gh/christophM/iml/master?filepath=./notebooks/tutorial-intro.ipynb)

```{r global_options, include=FALSE}

library(knitr)

opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE,

  fig.path = "man/figures/README-")

set.seed(42)

```

## Installation

The package can be installed directly from CRAN and the development version from GitHub:

```{r, results = "hide", eval = FALSE}

# Stable version

install.packages("iml")

# Development version

remotes::install_github("christophM/iml")

```

## News

Changes of the packages can be accessed in the [NEWS file](https://christophm.github.io/iml/news/index.html).

## Quickstart

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)

```{r}

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"))

```

## Contribute

Please check the [contribution guidelines](CONTRIBUTING.md)

## Citation

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:

```{tex}

@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}

}

```

## License

```{r, results='asis', echo=FALSE}

cat(sprintf(paste0("© 2018 - %s [Christoph Molnar](https://christophm.github.io/)"),

  format(Sys.time(), "%Y")))

```

The contents of this repository are distributed under the MIT license. See

below for details:

```

The MIT License (MIT)

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.

```

## 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)