https://github.com/mlr-org/mlr3automl

https://github.com/mlr-org/mlr3automl

Last synced: 16 days ago
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• Host: GitHub
• URL: https://github.com/mlr-org/mlr3automl
• Owner: mlr-org
• Created: 2023-11-09T08:48:35.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-25T16:19:40.000Z (5 months ago)
• Last Synced: 2025-03-24T04:08:34.712Z (4 months ago)
• Language: R
• Size: 1.67 MB
• Stars: 6
• Watchers: 5
• Forks: 1
• Open Issues: 17
• Metadata Files:
  • Readme: README.Rmd

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README

        
---

output: github_document

---

```{r, include = FALSE}

lgr::get_logger("mlr3")$set_threshold("warn")

lgr::get_logger("bbotk")$set_threshold("warn")

set.seed(1)

options(

    datatable.print.nrows = 10,

    datatable.print.class = FALSE,

    datatable.print.keys = FALSE,

    width = 100)

# mute load messages

library("mlr3automl")

```

# mlr3automl 

Package website: [release](https://mlr3automl.mlr-org.com/) | [dev](https://mlr3automl.mlr-org.com/dev/)

[![r-cmd-check](https://github.com/mlr-org/mlr3automl/actions/workflows/r-cmd-check.yml/badge.svg)](https://github.com/mlr-org/mlr3automl/actions/workflows/r-cmd-check.yml)

[![CRAN Status](https://www.r-pkg.org/badges/version-ago/mlr3automl)](https://cran.r-project.org/package=mlr3automl)

[![StackOverflow](https://img.shields.io/badge/stackoverflow-mlr3-orange.svg)](https://stackoverflow.com/questions/tagged/mlr3)

[![Mattermost](https://img.shields.io/badge/chat-mattermost-orange.svg)](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)

**mlr3automl** is the Automated Machine Learning (AutoML) package of the [mlr3](https://mlr-org.com/) ecosystem.

It automatically selects the most suitable machine learning algorithm and tunes its hyperparameters for a given task.

The package includes 10 learners from the `mlr3learners` package, ranging from simple models like `glmnet` to more powerful algorithms such as `ranger` and `xgboost`.

Leveraging the `mlr3pipelines` package, it constructs sophisticated preprocessing graphs with multiple parallel branches, which are jointly optimized using the `mlr3tuning` package.

The optimization is driven by Asynchronous Decentralized Bayesian Optimization (ADBO), enabling efficient and scalable AutoML.

## Installation

Install the development version from GitHub:

```{r eval = FALSE}

remotes::install_github("mlr-org/mlr3automl")

```

## Examples

```{r eval = FALSE}

library("mlr3automl")

rush_plan(n_workers = 2)

task = tsk("spam")

learner = lrn("classif.auto",

  terminator = trm("evals", n_evals = 100)

)

learner$train(task)

```