mlr3 is a modern, object-oriented R framework for machine learning. It provides core abstractions (tasks, learners, resamplings, measures, pipelines) implemented using R6 classes, enabling extensible, composable machine learning workflows. It focuses on clean design, scalability (large datasets), and integration into the wider R ecosystem via extension packages. Users can do classification, regression, survival analysis, clustering, hyperparameter tuning, benchmarking etc., often via companion packages.
Features
- Clean object-oriented design via R6, separating tasks, learners, resampling etc for modular workflows
- Efficient handling of large data: use of data.table, support for out-of-memory backends (e.g. databases)
- Parallelization support for learners, resampling, benchmarking etc via future / parallel backends
- Rich ecosystem: many extension packages for visualization, additional learners, pipelines, filters etc
- Measures and performance evaluation built in: classification, regression, survival etc with standard metrics and capacity to compute custom measures
- Support for benchmarking experiments, nested resampling, hyperparameter tuning etc through add-on packages
Project Samples
Categories
Machine LearningLicense
GNU Library or Lesser General Public License version 3.0 (LGPLv3)Follow mlr3
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