Alan Turing Institute Releases ML Framework Written in Julia

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May 9 · 3 min read

UK-based national research organization The Alan Turing Institute has released a new machine learning toolbox, Machine Learning in Julia (MLJ), which provides a uniform interface enabling users to easily train, evaluate, and tune machine learning models. This open-source framework is written in the high-performance scientific programming language Julia.

Inspired by Machine Learning in R (MLR), the Alan Turing Institute launched the development of the MLJ project last December and released its official version V 0.1.0 last week. The MLJ GitHub’s over 200 stars are the most among institute projects.

The major feature of MLJ is learning networks, a flexible model composition pipelining step that combines machine learning models more flexibly via techniques such as ensembling, stacking, and pipelining. Below is a schematic of a simple two-model stack viewed as a network.

Other features include:

  • Automated tuning of hyperparameters, including composite models. Tuning implemented as a model wrapper for composition with other meta-algorithms.
  • Metadata available without loading model code. Basis of a “task” interface and facilitates model composition.
  • Automatically match models to specified learning tasks, to streamline benchmarking and model selection.
  • Improves support for Bayesian statistics and probabilistic graphical models.
  • Present and manipulate data in your favorite Tables.jl format.
  • Enables model implementations to properly account for classes seen in training but not in evaluation.

The Alan Turing Institute believes MLJ’s features and functionality make it a better alternative than ScikitLearn.jl, a Julia wrapper for the popular Python library scikit-learn. Click the GitHub page for more detailed information.


: Tony Peng | : Michael Sarazen


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