1 Introduction and Overview
The mlr3 (Lang et al. 2019) package and ecosystem provide a generic, object-oriented, and extensible framework for classification, regression, survival analysis, and other machine learning tasks for the R language (R Core Team 2019). We do not implement any learners ourselves, but provide a unified interface to many existing learners in R. This unified interface provides functionality to extend and combine existing learners, intelligently select and tune the most appropriate technique for a task, and perform large-scale comparisons that enable meta-learning. Examples of this advanced functionality include hyperparameter tuning, feature selection, and ensemble construction. Parallelization of many operations is natively supported.
mlr3 provides a domain-specific language for machine learning in R. We target both practitioners who want to quickly apply machine learning algorithms and researchers who want to implement, benchmark, and compare their new methods in a structured environment. The package is a complete rewrite of an earlier version of mlr that leverages many years of experience to provide a state-of-the-art system that is easy to use and extend. It is intended for users who have basic knowledge of machine learning and R and who are interested in complex projects that use advanced functionality as well as one-liners to quickly prototype specific tasks.
Why a Rewrite?
mlr (Bischl et al. 2016) was first released to CRAN in 2013, with the core design and architecture dating back much further. Over time, the addition of many features has led to a considerably more complex design that made it harder to build, maintain, and extend than we had hoped for. With hindsight, we saw that some of the design and architecture choices in mlr made it difficult to support new features, in particular with respect to pipelines. Furthermore, the R ecosystem as well as helpful packages such as data.table have undergone major changes in the meantime. It would have been nearly impossible to integrate all of these changes into the original design of mlr. Instead, we decided to start working on a reimplementation in 2018, which resulted in the first release of mlr3 on CRAN in July 2019. The new design and the integration of further and newly developed R packages (R6, future, data.table) makes mlr3 much easier to use, maintain, and more efficient compared to mlr.
We follow these general design principles in the mlr3 package and ecosystem.
- Backend over frontend. Most packages of the mlr3 ecosystem focus on processing and transforming data, applying machine learning algorithms, and computing results. We do not provide graphical user interfaces (GUIs); visualizations of data and results are provided in extra packages.
- Embrace R6 for a clean, object-oriented design, object state-changes, and reference semantics.
- Embrace data.table for fast and convenient data frame computations.
- Unify container and result classes as much as possible and provide result data in
data.tables. This considerably simplifies the API and allows easy selection and “split-apply-combine” (aggregation) operations. We combine
R6to place references to non-atomic and compound objects in tables and make heavy use of list columns.
- Defensive programming and type safety.
All user input is checked with
checkmate(Lang 2017). Return types are documented, and mechanisms popular in base R which “simplify” the result unpredictably (e.g.,
[.data.frame) are avoided.
- Be light on dependencies. One of the main maintenance burdens for mlr was to keep up with changing learner interfaces and behavior of the many packages it depended on. We require far fewer packages in mlr3 to make installation and maintenance easier.
mlr3 requires the following packages:
- backports: Ensures backward compatibility with older R releases. Developed by members of the mlr3 team.
- checkmate: Fast argument checks. Developed by members of the mlr3 team.
- mlr3misc: Miscellaneous functions used in multiple mlr3 extension packages. Developed by the mlr3 team.
- mlr3measures: Performance measures for classification and regression. Developed by members of the mlr3 team.
- paradox: Descriptions of parameters and parameter sets. Developed by the mlr3 team.
- R6: Reference class objects.
Extension of R’s
- digest: Hash digests.
- uuid: Unique string identifiers.
- lgr: Logging facility.
- mlbench: A collection of machine learning data sets.
None of these packages adds any extra recursive dependencies to mlr3.
mlr3 provides additional functionality through extra packages:
Bischl, Bernd, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, and Zachary M. Jones. 2016. “mlr: Machine Learning in R.” Journal of Machine Learning Research 17 (170): 1–5. http://jmlr.org/papers/v17/15-066.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.
R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.