Resources

Documentation

Entry points to learn about mlr3.

  Book

Central entry point to the mlr3verse.

  Gallery

Collection of case studies and demos.

  R6 Introduction

The mlr3 ecosystem is build on R6 classes. The link gives an introduction to R6.

  Future Package

Link to the future framework that is used to parallelize functions in mlr3.

  Developer Information

Link to mlr3 developer wiki.

Cheat Sheets

The essential things neatly summarized. Perfectly printed out next to the keyboard or on a second monitor.

  mlr3

Core package cheat sheet.

  mlr3tuning

Tuning cheat sheet.

  mlr3fselect

Feature selection cheat sheet.

  mlr3pipelines

Pipelines cheat sheet.

Videos

Recorded tutorials and lectures we have given.

  useR2019 talk

Short intro to mlr3.

  useR2019 talk

Short intro to mlr3pipelines and mlr3tuning.

  useR2020 tutorial

Tutorial on mlr3, mlr3tuning and mlr3pipelines.

  ODSC talk 2021

Into to mlr3spatiotempcv and mlr3spatial.

Courses/Lectures

Material from teaching at our universities.

  I2ML course

Introduction to ML course. Free video lectures, slides, quizzes. Exercises use mlr3.

  mlr-outreach

Slides and other material for teaching mlr3.

Peer-reviewed Articles

A more scientific view on our packages and the packages we depend on.

  • Lang et al. (2019): about the base package mlr3
  • Binder et al. (2021): building machine learning pipelines with mlr3pipelines
  • Sonabend et al. (2021): probabilistic regression with mlr3proba (including survival analysis)
  • Bengtsson (2021): the parallelization framework package future we build upon
  • Lang (2017): package checkmate for argument checking and defensive programming
  • Lang, Bischl, and Surmann (2017): parallelization framework batchtools for high-performance computing clusters, used via future or mlr3batchmark

Tutorial Papers

  • Pargent, Schoedel, and Stachl (2023): An Introduction to Machine Learning for Psychologists in R
  • Zhao et al. (2024): Tutorial on survival modeling with applications to omics data. Tutorial Website.

References

Bengtsson, Henrik. 2021. “A Unifying Framework for Parallel and Distributed Processing in R Using Futures.” The R Journal 13 (2): 208–27. https://doi.org/10.32614/RJ-2021-048.
Binder, Martin, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, and Bernd Bischl. 2021. “mlr3pipelines - Flexible Machine Learning Pipelines in R.” Journal of Machine Learning Research 22 (184): 1–7. https://jmlr.org/papers/v22/21-0281.html.
Lang, Michel. 2017. “checkmate: Fast Argument Checks for Defensive R Programming.” The R Journal 9 (1): 437–45. https://doi.org/10.32614/RJ-2017-028.
Lang, Michel, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, and Bernd Bischl. 2019. “mlr3: A Modern Object-Oriented Machine Learning Framework in R.” Journal of Open Source Software, December. https://doi.org/10.21105/joss.01903.
Lang, Michel, Bernd Bischl, and Dirk Surmann. 2017. “Batchtools: Tools for R to Work on Batch Systems.” The Journal of Open Source Software, no. 10 (February). https://doi.org/10.21105/joss.00135.
Pargent, Florian, Ramona Schoedel, and Clemens Stachl. 2023. “Best Practices in Supervised Machine Learning: A Tutorial for Psychologists.” Advances in Methods and Practices in Psychological Science 6 (3): 25152459231162559. https://doi.org/10.1177/25152459231162559.
Sonabend, Raphael, Franz J Király, Andreas Bender, Bernd Bischl, and Michel Lang. 2021. “mlr3proba: An R Package for Machine Learning in Survival Analysis.” Bioinformatics, February. https://doi.org/10.1093/bioinformatics/btab039.
Zhao, Zhi, John Zobolas, Manuela Zucknick, and Tero Aittokallio. 2024. “Tutorial on survival modeling with applications to omics data.” Bioinformatics, March. https://doi.org/10.1093/BIOINFORMATICS/BTAE132.