{"id":18520174,"url":"https://github.com/mlr-org/mlr3extralearners","last_synced_at":"2026-04-03T08:02:49.019Z","repository":{"id":37402811,"uuid":"286446599","full_name":"mlr-org/mlr3extralearners","owner":"mlr-org","description":"Extra learners for use in 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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":["machine-learning","mlr3","r","r-package"],"created_at":"2024-11-06T17:18:54.542Z","updated_at":"2026-04-03T08:02:49.013Z","avatar_url":"https://github.com/mlr-org.png","language":"R","funding_links":["https://github.com/sponsors/mlr-org"],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  cache = FALSE,\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  echo = TRUE\n)\nlibrary(mlr3extralearners)\nlibrary(magrittr)\n```\n\n# mlr3extralearners\n\nPackage website: [release](https://mlr3extralearners.mlr-org.com/) \\|\n[dev](https://mlr3extralearners.mlr-org.com/dev/)\n\nExtra Learners for **[mlr3](https://github.com/mlr-org/mlr3/)**.\n\n\u003c!-- badges: start --\u003e\n[![RCMD Check](https://github.com/mlr-org/mlr3extralearners/actions/workflows/rcmdcheck.yml/badge.svg)](https://github.com/mlr-org/mlr3extralearners/actions/workflows/rcmdcheck.yml)\n[![StackOverflow](https://img.shields.io/badge/stackoverflow-mlr3-orange.svg)](https://stackoverflow.com/questions/tagged/mlr3)\n[![Mattermost](https://img.shields.io/badge/chat-mattermost-orange.svg)](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.08331/status.svg)](https://doi.org/10.21105/joss.08331)\n\u003c!-- badges: end --\u003e\n\n## What is mlr3extralearners?\n\n`mlr3extralearners` contains all learners from mlr3 that are not in `mlr3learners` or the core packages.\nAn overview of all learners within the `mlr3verse` can be found [here](https://mlr-org.com/learners.html).\n\n`mlr3extralearners` lives on GitHub and will not be on CRAN.\n\nYou can install the package as follows:\n\n```{r, eval = FALSE}\n# latest GitHub release\npak::pak(\"mlr-org/mlr3extralearners@*release\")\n\n# development version\npak::pak(\"mlr-org/mlr3extralearners\")\n```\n\nAlternatively, you can add the following to your .Rprofile, which allows you to install `mlr3extralearners` via `install.packages()`.\n\n```{r, eval = FALSE}\n# .Rprofile\noptions(repos = c(\n  mlrorg = \"https://mlr-org.r-universe.dev\",\n  CRAN = \"https://cloud.r-project.org/\"\n))\n```\n\n## Quick Start\n\nThe package includes functionality for detecting if you have the required packages installed\nto use a learner, and ships with the function `install_learner` which can install all required\nlearner dependencies.\n\n```{r, echo=TRUE, eval = FALSE}\nlibrary(mlr3extralearners)\nlrn(\"regr.gbm\")\n#\u003e Warning: Package 'gbm' required but not installed for Learner 'regr.gbm'\n#\u003e \u003cLearnerRegrGBM:regr.gbm\u003e: Gradient Boosting\n#\u003e * Model: -\n#\u003e * Parameters: keep.data=FALSE, n.cores=1\n#\u003e * Packages: mlr3, mlr3extralearners, gbm\n#\u003e * Predict Types:  [response]\n#\u003e * Feature Types: integer, numeric, factor, ordered\n#\u003e * Properties: importance, missings, weights\n\ninstall_learners(\"regr.gbm\")\n\nlearner = lrn(\"regr.gbm\")\nlearner\n#\u003e \u003cLearnerRegrGBM:regr.gbm\u003e: Gradient Boosting\n#\u003e * Model: -\n#\u003e * Parameters: keep.data=FALSE, n.cores=1\n#\u003e * Packages: mlr3, mlr3extralearners, gbm\n#\u003e * Predict Types:  [response]\n#\u003e * Feature Types: integer, numeric, factor, ordered\n#\u003e * Properties: importance, missings, weights\n```\n\n```{r, echo = FALSE}\nlearner = lrn(\"regr.gbm\")\n```\n\nYou can now use the learner to fit a model and make predictions.\n\n```{r}\ntask = tsk(\"california_housing\")\ntask\nsplit = partition(task)\nlearner$train(task, split$train)\nlearner$predict(task, split$test)\n```\n\nYou can learn more about using learners by [reading our book](https://mlr3book.mlr-org.com/chapters/chapter1/introduction_and_overview.html#mlr3-by-example).\n\n## Extending mlr3extralearners\n\nAn in-depth tutorial on how to add learners can be found in the [package website](https://mlr3extralearners.mlr-org.com/articles/extending.html).\n\n## Acknowledgements\n\nThis R package is developed as part of the [Mathematical Research Data Initiative](https://www.mardi4nfdi.de/about/mission).\n\n## Citing mlr3extralearners\n\nIf you use `mlr3extralearners`, please cite our [JOSS paper](https://doi.org/10.21105/joss.08331):\n\n```{r echo = FALSE, comment = \"\"}\ntoBibtex(citation(\"mlr3extralearners\"))\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmlr-org%2Fmlr3extralearners","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmlr-org%2Fmlr3extralearners","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmlr-org%2Fmlr3extralearners/lists"}