A Machine Learning Framework for Julia

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Introduction

MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing machine learning models written in Julia and other languages. In particular MLJ wraps a large number of scikit-learn models.

MLJ is released under the MIT licensed and sponsored by the Alan Turing Institute.

Key goals

  • Offer a consistent way to use, compose and tune machine learning models in Julia,

  • Promote the improvement of the Julia ML/Stats ecosystem by making it easier to use models from a wide range of packages,

  • Unlock performance gains by exploiting Julia's support for parallelism, automatic differentiation, GPU, optimisation etc.

Key features

  • Data agnostic, train models on any data supported by the Tables.jl interface,

  • Extensive support for model composition (pipelines and learning networks),

  • Convenient syntax to tune and evaluate (composite) models.

  • Consistent interface to handle probabilistic predictions.

  • Extensible tuning interface, to support growing number of optimization strategies, and designed to play well with model composition.

More information is available from the MLJ design paper

Reporting problems

Users are encouraged to provide feedback on their experience using MLJ and to report issues. You can do so here or on the #mlj Julia slack channel.

For known issues that are not strictly MLJ bugs, see here

Installation

Initially it is recommended that MLJ and associated packages be installed in a new environment to avoid package conflicts. You can do this with

julia> using Pkg; Pkg.activate("My_MLJ_env", shared=true)

Installing MLJ is also done with the package manager:

julia> Pkg.add("MLJ")

It is important to note that MLJ is essentially a big wrapper providing a unified access to model providing packages and so you will also need to make sure these packages are available in your environment. For instance, if you want to use a Decision Tree Classifier, you need to have DecisionTree.jl installed:

julia> Pkg.add("DecisionTree");
julia> using MLJ;
julia> @load DecisionTreeClassifier

For a list of models and their packages run

using MLJ
models()

or refer to this table.

It is recommended that you start with models marked as coming from mature packages such as DecisionTree.jl, ScikitLearn.jl or XGBoost.jl.

MLJ is supported by a number of satelite packages (MLJTuning, MLJModelInterface, etc) which the general user is not required to install directly. Developers can learn more about these here

Learning to use MLJ

The present document, although littered with examples, is primarily intended as a complete reference. For a lightning introduction to MLJ read the Getting Started section of this manual. For more leisurely and extensive tutorials, we highly recommend the MLJ Tutorials website. Each tutorial can be downloaded as a notebook or Julia script to facilitate experimentation.

Users are also welcome to join the #mlj Julia slack channel to ask questions and make suggestions.

Citing MLJ

An MLJ design paper is under review. In the meantime, please cite the software using one of the following:

https://doi.org/10.5281/zenodo.3541506

@software{anthony_blaom_2019_3541506,
  author       = {Anthony Blaom and
                  Franz Kiraly and
                  Thibaut Lienart and
                  Sebastian Vollmer},
  title        = {alan-turing-institute/MLJ.jl: v0.5.3},
  month        = nov,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {v0.5.3},
  doi          = {10.5281/zenodo.3541506},
  url          = {https://doi.org/10.5281/zenodo.3541506}
}