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The R (R Core Team, 2019) package mlr3 and its associated ecosystem of extension packages implements a powerful, object-oriented and extensible framework for machine learning (ML) in R. It provides a unified interface to many learning algorithms available on CRAN, augmenting them with model-agnostic general-purpose functionality that is needed in every ML project, for example train-test-evaluation, resampling, preprocessing, hyperparameter tuning, nested resampling, and visualization of results from ML experiments. The package is a complete reimplementation of the mlr (Bischl et al., 2016) package that leverages many years of experience and learned best practices to provide a state-of-the-art system that is powerful, flexible, extensible, and maintainable. We target both practitioners who want to quickly apply ML algorithms to their problems and researchers who want to implement, benchmark, and compare their new methods in a structured environment. mlr3 is suitable for short scripts that test an idea, for complex multi-stage experiments with advanced functionality that use a broad range of ML functionality, as a foundation to implement new ML (meta-) algorithms (for example AutoML systems), and everything in between. Functional correctness is ensured through extensive unit and integration tests.

Several other general-purpose ML toolboxes exist for different programing languages. The most widely used ones are scikit-learn (Pedregosa et al., 2011) for Python, Weka (Hall et al., 2009) for Java, and mlj (Blaom, Kiraly, Lienart, & Vollmer, 2019) for Julia. The most important toolboxes for R are mlr, caret (Kuhn, 2008) and tidymodels …