Resources

Useful resources for learning mlr3

Documentation

Entry points to learn about mlr3.

  Book

Central entry point to the mlr3verse.

  Gallery

Collection of case studies and demos.

  Reference Manuals

Reference manuals (via package overview).

  R6 Introduction

Introduction to R6 classes.

  Future Package

Homepage of the parallelization framework 'future'.

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 the packages and the packages we depend on.

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.
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.

References

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