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R does not define a standardized interface for its machine-learning algorithms. Therefore, for any non-trivial experiments, you need to write lengthy, tedious, and error-prone wrappers to call the different algorithms and unify their respective output. {mlr} provides this infrastructure so that you can focus on your experiments! The framework provides supervised methods like classification, regression, and survival analysis along with their corresponding evaluation and optimization methods, as well as unsupervised methods like clustering. It is written in a way that you can extend it yourself or deviate from the implemented convenience methods and construct your own complex experiments or algorithms.
Features
- Resample your models
- Optimize hyperparameters
- Select features
- Cope with pre- and post-processing of data and compare models in a statistically meaningful way
- Documentation available
- Examples included
- Clear S3 interface to R classification, regression, clustering and survival analysis methods
- Abstract description of learners and tasks by properties
- Convenience methods and generic building blocks for your machine learning experiments
- Resampling methods like bootstrapping, cross-validation and subsampling
- Extensive visualizations (e.g. ROC curves, predictions and partial predictions)
Project Samples
Categories
Machine LearningLicense
MIT LicenseFollow mlr
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