• Offical CRAN release site: http://cran.r-project.org/web/packages/mlr/index.html

• Detailed Tutorial: https://github.com/berndbischl/mlr/blob/master/doc/knitted/tutorial/README.md

• R Documentation in HTML: http://berndbischl.github.io/mlr/man/

• Run this in R to install the current GitHub version:

  devtools::install_github("mlr", username="berndbischl")

• Further installation instructions

• Travis CI:

R does not define a standardized interface for all 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. Additionally you need to implement infrastructure to resample your models, optimize hyperparameters, select features, cope with pre- and post-processing of data and compare models in a statistically meaningful way. As this becomes computationally expensive, you might want to parallelize your experiments as well. This often forces users to make crummy trade-offs in their experiments due to time constraints or lacking expert programming skills. mlr provides this infrastructure so that you can focus on your experiments! The framework currently focuses on supervised methods like classification, regression and survival analysis and their corresponding evaluation and optimization. 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.

• Clear S3 interface to R classification and regression methods
• Easy extension mechanism through S3 inheritance
• Abstract description of learners and tasks by properties
• Parameter system for learners to encode data types and constraints
• Many convenience methods and generic building blocks for your machine learning experiments
• Resampling like bootstrapping, cross-validation and subsampling
• Easy hyperparameter tuning using different optimization strategies
• Variable selection with filters and wrappers
• Parallelization is built-in
• Extension points to integrate your own stuff
• Unit-testing
• Possibility to fit, predict, evaluate and resample models
• Tune hyper-parameters of a learner with different optimization algorithms
• Feature selection with filters and wrappers
• Combine different processing steps to a complex data mining chain; enables nested resampling of optimized models

Developers use this mailing list:

https://groups.google.com/forum/?hl=de#!forum/mlr-devel

[email protected]

The github-service-hook will also send commit messages to this list.

If you are interested in the package, have a question regarding the usage or a feature request, or maybe want to help improving mlr, please send a mail to the list at [email protected] or to the maintainer Bernd Bischl at [email protected].