mlr3: Machine Learning in R

An open-source collection of R packages providing a unified interface for machine learning in the R language. Successor of mlr.

A scientifically designed and easy to learn interface.
More than 100 connected machine learning algorithms.
Light on dependencies.
Convenient parallelization with the future package.
State-of-the-art optimization algorithms.
Dataflow programming with pipelines.

Get Started

Install the full mlr3 universe at once.


You can also use our Docker images. Read our book or start with a gallery post.


Basic Machine Learning

Get to know the basic building blocks of machine learning in mlr3. Train your first learner and estimate its performance with resampling. Compare the performance of learners with benchmarking.


Optimize the hyperparameters of a classification tree on the Palmer Penguins data set. Become familiar with search spaces and transformations. Fit a final model with optimized hyperparameters for predicting new data.


Build a preprocessing pipeline for missing data in the German Credit data set. Optimize the parameters of the pipeline and stack multiple learners into an ensemble model. Learn about techniques to tackle challenging data sets.

Feature Selection

Start a feature selection on the Titanic data set. Learn about different optimization algorithms and fit a final model. Estimate the performance of the optimized feature set with nested resampling.