kdpsingh / mlr3

mlr3: Machine Learning in R - next generation

mlr3

A clean, object-oriented rewrite of mlr.

Why a rewrite?

mlr was first released to CRAN in 2013. Its core design and architecture date back even further. The addition of many features has led to a feature creep which makes mlr hard to maintain and hard to extend. We also think that while mlr was nicely extensible in some parts (learners, measures, etc.), other parts were less easy to extend from the outside. Also, many helpful R libraries did not exist at the time mlr was created, and their inclusion would result in non-trivial API changes.

Design principles

• Only the basic building blocks for machine learning are implemented in this package.

• Focus on computation here. No visualization or other stuff. That can go in extra packages.

• Overcome the limitations of R's S3 classes with the help of R6.

• Embrace R6, clean OO-design, object state-changes and reference semantics. This might be less "traditional R", but seems to fit mlr nicely.

• Embrace data.table for fast and convenient data frame computations.

• Combine data.table and R6, for this we will make heavy use of list columns in data.tables.

• Be light on dependencies. mlr3 requires the following packages:

  • backports: Ensures backward compatibility with older R releases. Developed by members of the mlr team. No recursive dependencies.
  • checkmate: Fast argument checks. Developed by members of the mlr team. No extra recursive dependencies.
  • mlr3misc Miscellaneous functions used in multiple mlr3 extension packages. Developed by the mlr team. No extra recursive dependencies.
  • paradox: Descriptions for parameters and parameter sets. Developed by the mlr team. No extra recursive dependencies.
  • R6: Reference class objects. No recursive dependencies.
  • data.table: Extension of R's data.frame. No recursive dependencies.
  • digest: Hash digests. No recursive dependencies.
  • logger: Logging facility. No recursive dependencies.
  • Metrics: Package which implements performance measures. No recursive dependencies.
  • mlbench: A collection of machine learning data sets. No dependencies.

• Additional functionality that comes with extra dependencies:

  • For parallelization, mlr3 utilizes the future and future.apply packages.
  • To capture output, warnings and excepetions, evaluate and callr can be used.

State of the project

This package is currently work-in-progress. Do not use in production. The API will change.

Already implemented:

• Basic building blocks of machine learning
  • Experiment: Class to store and access a single machine learning experiment
  • Tasks for classification and regression
  • Abstract Learner
  • Resampling strategies
  • Performance measures
• Task Generators
• Single step experiments via the Experiment class
• Resampling via resample()
• Stratification and blocking / grouping.
• Benchmarking via benchmark()
• Parallelization via future.

The webpage provides, besides a short introduction, a function reference.

WiP

While mlr3 implements the building blocks for machine learning, some of the advanced features of the monolithic mlr are now shipped in multiple extension packages.