A unified interface and infrastructure for machine learning in R, supporting classification, regression, clustering, and survival analysis.
mlr is a machine learning framework for R that provides a unified interface to various algorithms and infrastructure for experiments. It solves the problem of R's lack of standardized interfaces by offering tools for resampling, hyperparameter tuning, feature selection, and benchmarking, streamlining the machine learning workflow.
Data scientists, researchers, and practitioners using R for machine learning who need a comprehensive toolkit for reproducible experiments and algorithm benchmarking.
Developers choose mlr for its extensive feature set, including built-in parallelization, OpenML integration, and flexibility to extend or customize experiments, all within a consistent S3 interface.
Machine Learning in R
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Provides a standardized S3 interface for classification, regression, clustering, and survival analysis, simplifying access to diverse R methods as highlighted in the README.
Includes built-in methods for resampling, hyperparameter tuning, and feature selection, reducing boilerplate code for reproducible workflows.
Supports configurators like iterated F-racing and sequential model-based optimization, enabling efficient hyperparameter tuning as detailed in the features list.
Connector for the Open Machine Learning server allows easy sharing of datasets and experiments, fostering collaborative research as mentioned in the introduction.
The package is retired and no longer actively developed; only severe bugs are fixed, limiting future enhancements and compatibility with new R versions.
Users must transition to mlr3 for ongoing support, but not all mlr features are yet implemented in mlr3, creating potential gaps in functionality.
The extensive infrastructure can be overwhelming for simple machine learning tasks, introducing unnecessary setup and learning curve for basic use cases.