{"id":13401132,"url":"https://github.com/giuseppec/iml","last_synced_at":"2025-12-12T00:54:05.134Z","repository":{"id":29198844,"uuid":"108261564","full_name":"giuseppec/iml","owner":"giuseppec","description":"iml: interpretable machine learning R package","archived":false,"fork":false,"pushed_at":"2024-05-22T04:09:03.000Z","size":841463,"stargazers_count":487,"open_issues_count":55,"forks_count":88,"subscribers_count":23,"default_branch":"main","last_synced_at":"2024-05-22T11:25:15.719Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://giuseppec.github.io/iml/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/giuseppec.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":"codemeta.json"}},"created_at":"2017-10-25T11:36:53.000Z","updated_at":"2024-05-28T07:38:55.184Z","dependencies_parsed_at":"2023-02-19T04:30:44.519Z","dependency_job_id":"64b6ebd5-d131-478e-b32c-ddb6733c9597","html_url":"https://github.com/giuseppec/iml","commit_stats":{"total_commits":659,"total_committers":14,"mean_commits":47.07142857142857,"dds":0.2382397572078907,"last_synced_commit":"a72863fe9fa42ac54c0dbfef16f7bcd512b17218"},"previous_names":["giuseppec/iml","christophm/iml"],"tags_count":14,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giuseppec%2Fiml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giuseppec%2Fiml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giuseppec%2Fiml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giuseppec%2Fiml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/giuseppec","download_url":"https://codeload.github.com/giuseppec/iml/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247737788,"owners_count":20987721,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-07-30T19:00:59.052Z","updated_at":"2025-12-12T00:54:05.052Z","avatar_url":"https://github.com/giuseppec.png","language":"R","funding_links":[],"categories":["R"],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n\u003c!-- badges: start --\u003e\n\n[![r-cmd-check](https://github.com/giuseppec/iml/actions/workflows/r-cmd-check.yml/badge.svg)](https://github.com/giuseppec/iml/actions)\n[![CRAN Status\nBadge](https://www.r-pkg.org/badges/version-ago/iml)](https://CRAN.R-project.org/package=iml)\n[![CRAN\nDownloads](https://cranlogs.r-pkg.org/badges/grand-total/iml)](https://CRAN.R-project.org/package=iml)\n[![codecov.io](https://codecov.io/github/christophM/iml/coverage.svg?branch=master)](https://codecov.io/github/christophM/iml?branch=master)\n[![DOI](http://joss.theoj.org/papers/10.21105/joss.00786/status.svg)](https://doi.org/10.21105/joss.00786)\n\u003c!-- badges: end --\u003e\n\n# iml\n\n `iml ` is an R package that interprets the behavior and explains predictions of machine learning models. \u003cimg src=\"https://github.com/christophM/iml/blob/master/man/figures/iml.png?raw=true\" align=\"right\" height=140/\u003e\n It implements model-agnostic interpretability methods - meaning they can be used with any machine learning model.\n\n## Features\n\n- Feature importance\n- Partial dependence plots\n- Individual conditional expectation plots (ICE)\n- Accumulated local effects\n- Tree surrogate\n- LocalModel: Local Interpretable Model-agnostic Explanations\n- Shapley value for explaining single predictions\n\nRead more about the methods in the [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/agnostic.html) book.\n\n## Tutorial\n\nStart 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)\n\n```{r global_options, include=FALSE}\nlibrary(knitr)\nopts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE,\n  fig.path = \"man/figures/README-\")\nset.seed(42)\n```\n\n## Installation\n\nThe package can be installed directly from CRAN and the development version from GitHub:\n\n```{r, results = \"hide\", eval = FALSE}\n# Stable version\ninstall.packages(\"iml\")\n\n# Development version\nremotes::install_github(\"christophM/iml\")\n```\n\n## News\n\nChanges of the packages can be accessed in the [NEWS file](https://christophm.github.io/iml/news/index.html).\n\n## Quickstart\n\nFirst we train a Random Forest to predict the Boston median housing value.\nHow does `lstat` influence the prediction individually and on average? (Accumulated local effects)\n\n```{r}\nlibrary(\"iml\")\nlibrary(\"randomForest\")\ndata(\"Boston\", package = \"MASS\")\nrf = randomForest(medv ~ ., data = Boston, ntree = 50)\nX = Boston[which(names(Boston) != \"medv\")]\nmodel = Predictor$new(rf, data = X, y = Boston$medv)\neffect = FeatureEffects$new(model)\neffect$plot(features = c(\"lstat\", \"age\", \"rm\"))\n```\n\n## Contribute\n\nPlease check the [contribution guidelines](CONTRIBUTING.md)\n\n## Citation\n\nIf you use iml in a scientific publication, please cite it as:\n\n```\nMolnar, Christoph, Giuseppe Casalicchio, and Bernd Bischl. \"iml: An R package for interpretable machine learning.\" Journal of Open Source Software 3.26 (2018): 786.\n```\n\nBibTeX:\n\n```{tex}\n@article{molnar2018iml,\n  title={iml: An R package for interpretable machine learning},\n  author={Molnar, Christoph and Casalicchio, Giuseppe and Bischl, Bernd},\n  journal={Journal of Open Source Software},\n  volume={3},\n  number={26},\n  pages={786},\n  year={2018}\n}\n\n```\n\n## License\n\n```{r, results='asis', echo=FALSE}\ncat(sprintf(paste0(\"\u0026copy; 2018 - %s [Christoph Molnar](https://christophm.github.io/)\"),\n  format(Sys.time(), \"%Y\")))\n```\n\nThe contents of this repository are distributed under the MIT license. See\nbelow for details:\n```\nThe MIT License (MIT)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n```\n\n## Funding\n\nThis work is funded by the Bavarian State Ministry of Education, Science and the Arts in the framework of the Centre Digitisation.Bavaria (ZD.B)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgiuseppec%2Fiml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgiuseppec%2Fiml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgiuseppec%2Fiml/lists"}