erenturunc/mlr

mlr: Machine Learning in R

: Machine Learning in R

• Offical CRAN release site

• Detailed Tutorial (Download)

• R Documentation in HTML

• Install the development version

  devtools::install_github("berndbischl/mlr")

• Further installation instructions

• Travis CI:

Introduction

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.

Features

• Clear S3 interface to R classification, regression, clustering and survival analysis methods
• Possibility to fit, predict, evaluate and resample models
• 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
• Different visualizations for e.g. ROC curves and predictions
• Benchmarking of learners for muliple data sets
• Easy hyperparameter tuning using different optimization strategies, including potent configurators like iterated F-racing (irace) or sequential model-based optimization
• Variable selection with filters and wrappers
• Nested resampling of models with tuning and feature selection
• Cost-sensitive learning, threshold tuning and imbalance correction
• Wrapper mechanism to extend learner functionality and complex and custom ways
• Combine different processing steps to a complex data mining chain that can be jointly optimized
• OpenML connector for the Open Machine Learning server
• Extension points to integrate your own stuff
• Parallelization is built-in
• Unit-testing

If you like the package, please "star" it on Github.

News

Most NEWS regarding extensions and changes of the packages can be accessed here for the release and here for the devel version on Github.

• 2015-02-17:
  • We are currently in the process of working out a couple of project proposals for "Google Summer of Code 2015" with mlr. Have a look at our Wiki and at gscoc-r and the Google group if you are an interested student.
  • We have been informed that our tutorial "Applied Machine Learning and Efficient Model Selection with mlr" has been accepted for useR 2015 in Aarlborg. Hoping to meet all of you there in June!
• 2015-02-04:
  • mlr 2.3 released to CRAN.
• 2014-10-28:
  • mlr 2.2 released to CRAN.
  • The popular Java tool WEKA uses mlr in its RPlugin.
• 2014-10-18:
  • We have improved the tutorial A LOT. It is also based on mkdocs and knitr now. Still fixing minor things and extending a bit. Take a look yourself.
  • We might refactor the internal handling and OO structure of tasks a bit, so even more natural indexing and operations are possible. Michel is currently working on this in a branch.
  • I will talk about mlr in connection with the OpenML project next week. If you don't know this, take a look, a very cool initiative by Joaquin Vanschoren. We are developing a connector to R in general which also covers mlr here.

Get in Touch

Please use the issue tracker for problems, questions and feature requests. Don't email in most cases, as we forget these mails.

We also do not hate beginners and it is perfectly valid to mark a issue as "Question".

Please don't forget that all of us work in academia and put a lot of work into this project, simply because we like it, not because we are specifically paid for it.

We also welcome pull requests or new developers.

For everything else the maintainer Bernd Bischl can be reached here: [email protected]. He (=me) is sometimes busy, so please use the other channels for appropriate stuff first, so you get quicker responses ;-)