{"id":34901230,"url":"https://github.com/slds-lmu/imlplots","last_synced_at":"2026-03-16T07:07:45.585Z","repository":{"id":48492718,"uuid":"128178323","full_name":"slds-lmu/imlplots","owner":"slds-lmu","description":"Create Interpretable Machine Learning plots with an interactive Shiny based dashboard","archived":false,"fork":false,"pushed_at":"2018-09-19T10:27:32.000Z","size":2661,"stargazers_count":15,"open_issues_count":4,"forks_count":4,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-13T00:56:31.306Z","etag":null,"topics":["interpretable-ml","shinydashboard"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/slds-lmu.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-04-05T08:23:30.000Z","updated_at":"2024-03-21T18:36:09.000Z","dependencies_parsed_at":"2022-07-30T01:18:00.882Z","dependency_job_id":null,"html_url":"https://github.com/slds-lmu/imlplots","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/slds-lmu/imlplots","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slds-lmu%2Fimlplots","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slds-lmu%2Fimlplots/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slds-lmu%2Fimlplots/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slds-lmu%2Fimlplots/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/slds-lmu","download_url":"https://codeload.github.com/slds-lmu/imlplots/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slds-lmu%2Fimlplots/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30571263,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-16T06:02:37.763Z","status":"ssl_error","status_checked_at":"2026-03-16T06:02:14.913Z","response_time":96,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["interpretable-ml","shinydashboard"],"created_at":"2025-12-26T08:53:16.658Z","updated_at":"2026-03-16T07:07:45.578Z","avatar_url":"https://github.com/slds-lmu.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\neditor_options: \n  chunk_output_type: console\n---\n\n# imlplots: interpretable machine learning plots\n `imlplots ` is an R package that provide an interactive Shiny dashboard for three kinds of Interpretable Machine Learning (IML) plots\n \n* Partial Dependence Plots (PDP)\n* Individual Conditional Expectation (ICE) plots\n* Accumulated Local Effect (ALE) plots\n\n\n\n```{r global_options, include=FALSE}\nlibrary(knitr)\nopts_chunk$set(echo=TRUE, warning=FALSE, message=FALSE)\nset.seed(42)\nload(\"data/boston.rda\")\nlibrary(mlr)\n```\n\n# Installation \nThe package can be installed directly from github with devtools\n```{r, results = 'hide', eval = FALSE}\n# install.packages(\"devtools\")\ndevtools::install_github('juliafried/imlplots')\nlibrary(imlplots)\n```\n\n\n# Quickstart\nYou can fit classification and regression problems from the `mlr` package and analyse possible interaction effects in a Shiny dasbhoard.\n\nFor quickstart we take the popular Boston Housing data, where we want to predict the median housing price in Boston.\n```{r}\nprint(summarizeColumns(boston)[, -c(5, 6, 7)], digits = 4)\n```\n\nFor using `imlplots` Shiny dashboard, three input arguments need to be specified\n\n* `data` - the input data\n* `task`- the learning task\n* `models` - one or several trained models\n\nWe create a regression task with `medv` as target variable.\nThe task structure is determined by `mlr` package.\n```{r}\nboston.task = makeRegrTask(data = boston, target = \"medv\")\n```\n\nThe `imlplots` dashboard allows the comparison of multiple learning algorithms, therefore we fit two different models - first a random forest and second a GLM.\n```{r}\nrf.mod = train(\"regr.randomForest\", boston.task)\nglm.mod = train(\"regr.glm\", boston.task)\n```\n\nThe input for the Shiny app is a list of learners.\n```{r}\nmod.list = list(rf.mod, glm.mod)\n```\n\nNow the Shiny app can be used.\n```{r, eval=FALSE}\nimlplots(data = boston, task = boston.task, models = mod.list)\n```\n\n## Code for Copy \u0026 Paste\n```{r, eval=FALSE}\nboston.task = makeRegrTask(data = boston, target = \"medv\")\n\nrf.mod = train(\"regr.randomForest\", boston.task)\nglm.mod = train(\"regr.glm\", boston.task)\nmod.list = list(rf.mod, glm.mod)\n\nimlplots(data = boston, task = boston.task, models = mod.list)\n```\n\n## Further Examples\n\n* Check out our [Wiki](https://github.com/juliafried/imlplots/wiki)\n* [Vignette](https://github.com/juliafried/imlplots/raw/master/vignettes/imlplots.pdf)\n\n# References\n\n* [References](https://github.com/juliafried/imlplots/raw/master/paper/references.pdf)\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fslds-lmu%2Fimlplots","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fslds-lmu%2Fimlplots","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fslds-lmu%2Fimlplots/lists"}