mlr3 <img class="emoji" alt="heart" src="https://cdn.jsdelivr.net/gh/qinwf/awesome-R@3c66da6e291bcc0520b1649125b0bed750896a9a/heart.png" height="20" align="absmiddle" width="20">
A modern, object-oriented machine learning framework for R, providing efficient building blocks for ML workflows.
What is mlr3 <img class="emoji" alt="heart" src="https://cdn.jsdelivr.net/gh/qinwf/awesome-R@3c66da6e291bcc0520b1649125b0bed750896a9a/heart.png" height="20" align="absmiddle" width="20">?
mlr3 is a modern, object-oriented machine learning framework for the R programming language. It provides a structured and efficient way to build, train, evaluate, and tune machine learning models through a modular system of tasks, learners, and resampling strategies. The framework addresses the limitations of its predecessor, mlr, by offering a cleaner architecture, better extensibility, and improved performance.
Data scientists, statisticians, and researchers using R for machine learning projects who need a flexible and robust framework for model development and evaluation. It is particularly suited for those building complex ML workflows or requiring integration with specialized extensions.
Developers choose mlr3 for its modern R6-based design, which enables clear object management and stateful operations, combined with the speed of data.table and a rich ecosystem of extension packages. Its focus on modularity and type safety makes it both powerful for advanced users and approachable for beginners through comprehensive documentation and the mlr3verse meta-package.
Overview
mlr3: Machine Learning in R - next generation
Use Cases
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