iml

iml: An R package for Interpretable Machine Learning - Published in JOSS (2018)

https://github.com/giuseppec/iml

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 3 DOI reference(s) in README
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Academic publication links
Links to: joss.theoj.org
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Committers with academic emails
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (19.1%) to scientific vocabulary

Keywords from Contributors

standardization

Scientific Fields

Artificial Intelligence and Machine Learning Computer Science - 83% confidence

Last synced: 4 months ago · JSON representation

Repository

iml: interpretable machine learning R package

Basic Info

Host: GitHub
Owner: giuseppec
License: other
Language: R
Default Branch: main
Homepage: https://giuseppec.github.io/iml/
Size: 804 MB

Statistics

Stars: 501
Watchers: 21
Forks: 87
Open Issues: 62
Releases: 9

Created about 8 years ago · Last pushed 11 months ago

Metadata Files

Readme Changelog Contributing License Codemeta

README.Rmd

---
output: github_document
---



[![r-cmd-check](https://github.com/giuseppec/iml/actions/workflows/r-cmd-check.yml/badge.svg)](https://github.com/giuseppec/iml/actions)
[![CRAN Status
Badge](https://www.r-pkg.org/badges/version-ago/iml)](https://CRAN.R-project.org/package=iml)
[![CRAN
Downloads](https://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)

Owner

Name: Giuseppe Casalicchio
Login: giuseppec
Kind: user

Repositories: 65
Profile: https://github.com/giuseppec

CodeMeta (codemeta.json)

{
  "@context": "https://raw.githubusercontent.com/codemeta/codemeta/master/codemeta.jsonld",
  "@type": "Code",
  "author": [
    {
      "@id": "https://orcid.org/0000-0003-2331-868X",
      "@type": "Person",
      "email": "christoph.molnar@gmail.com",
      "name": "Christoph Molnar",
      "affiliation": "Department of Statistics, LMU Munich"
    }
  ],
  "identifier": "",
  "codeRepository": "https://github.com/giuseppec/iml",
  "datePublished": "2018-06-19",
  "dateModified": "2018-06-19",
  "dateCreated": "2018-06-19",
  "description": "iml is an R package that offers a general toolbox for making machine learning models interpretable.",
  "keywords": "machine learning, interpretable machine learning, R",
  "license": "MIT",
  "title": "iml: An R package for Interpretable Machine Learning",
  "version": "v0.5.2"
}

GitHub Events

Total

Issues event: 4
Watch event: 9
Issue comment event: 2
Member event: 1
Push event: 10
Pull request event: 1
Create event: 1

Last Year

Issues event: 4
Watch event: 9
Issue comment event: 2
Member event: 1
Push event: 10
Pull request event: 1
Create event: 1

Committers

Last synced: 9 months ago

All Time

Total Commits: 683
Total Committers: 15
Avg Commits per committer: 45.533
Development Distribution Score (DDS): 0.234

Past Year

Commits: 21
Committers: 2
Avg Commits per committer: 10.5
Development Distribution Score (DDS): 0.048

Top Committers

Name	Email	Commits
Christoph Molnar	c**r@g**m	523
pat-s	p**z@g**m	106
giuseppec	g**o@g**m	23
QWERTZ11	c**g@o**m	6
Mirka Henninger	m**l@m**e	6
github-actions[bot]	4****]	4
Martin Walter	m**r@w**e	3
Simon Tietze	s**e@b**m	3
Pietro Monticone	p**s@g**m	2
Grant Irvine-Smith	g**h@i**m	2
pfistfl	p**f@g**m	1
g-rho	g**k@w**e	1
be-marc	m**r@p**e	1
Mark Roepke	m**5@g**m	1
Julia	j**d@o**m	1

Committer Domains (Top 20 + Academic)

posteo.de: 1 investecmail.com: 1 ben-energy.com: 1 mirka-henninger.de: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 76
Total pull requests: 17
Average time to close issues: about 1 month
Average time to close pull requests: 1 day
Total issue authors: 31
Total pull request authors: 7
Average comments per issue: 0.95
Average comments per pull request: 1.35
Merged pull requests: 16
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 4
Pull requests: 2
Average time to close issues: 2 days
Average time to close pull requests: about 1 hour
Issue authors: 4
Pull request authors: 1
Average comments per issue: 0.0
Average comments per pull request: 0.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

christophM (37)
dandls (4)
berndbischl (4)
ugroempi (3)
aiwalter (2)
see24 (2)
pat-s (2)
nhejazi (2)
MathiasCara (1)
juliabrosig (1)
giuseppec (1)
jamonterotena (1)
pfistfl (1)
be-marc (1)
Sophon-0 (1)

Pull Request Authors

christophM (8)
QWERTZ11 (2)
aiwalter (2)
be-marc (2)
simon-tietze (1)
snvv (1)
juliabrosig (1)
markroepke (1)

Top Labels

Issue Labels

enhancement (14) bug (4) help wanted (2) good first issue (2)

Pull Request Labels

Packages

Total packages: 2
Total downloads:
- cran 4,826 last-month
Total docker downloads: 21,868

Total dependent packages: 8
(may contain duplicates)
Total dependent repositories: 42
(may contain duplicates)
Total versions: 24
Total maintainers: 1

cran.r-project.org: iml

Interpretable Machine Learning

Homepage: https://giuseppec.github.io/iml/
Documentation: http://cran.r-project.org/web/packages/iml/iml.pdf
License: MIT + file LICENSE
Latest release: 0.11.4
published 11 months ago

Versions: 17
Dependent Packages: 8
Dependent Repositories: 42
Downloads: 4,826 Last month
Docker Downloads: 21,868

Rankings

Docker downloads count: 0.6%

Forks count: 0.7%

Stargazers count: 0.7%

Average: 3.6%

Dependent repos count: 4.0%

Downloads: 7.4%

Dependent packages count: 8.1%

Maintainers (1)

giuseppe.casalicchio@lmu.de

Last synced: 4 months ago

proxy.golang.org: github.com/giuseppec/iml

Documentation: https://pkg.go.dev/github.com/giuseppec/iml#section-documentation
License: other
Latest release: v0.11.1
published over 3 years ago

Versions: 7
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.5%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 4 months ago

Dependencies

DESCRIPTION cran

Formula * imports
Metrics * imports
R6 * imports
checkmate * imports
data.table * imports
future * imports
future.apply * imports
ggplot2 * imports
prediction * imports
ALEPlot * suggests
MASS * suggests
bench * suggests
bit64 * suggests
caret * suggests
covr * suggests
e1071 * suggests
future.callr * suggests
glmnet * suggests
gower * suggests
h2o * suggests
keras >= 2.2.5.0 suggests
knitr * suggests
mlr * suggests
mlr3 * suggests
party * suggests
partykit * suggests
patchwork * suggests
randomForest * suggests
ranger * suggests
rmarkdown * suggests
rpart * suggests
testthat * suggests
yaImpute * suggests

.github/workflows/tic.yml actions

actions/checkout v3 composite
actions/upload-artifact v2 composite
pat-s/always-upload-cache v3 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-tinytex v2 composite

.github/workflows/update-tic.yml actions

actions/checkout v3 composite
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