Adding Models for General Use
The machine learning tools provided by MLJ can be applied to the models in any package that imports MLJModelInterface and implements the API defined there, as outlined in this document.
This is a reference document, which has become rather sprawling over the evolution of the MLJ project. We recommend starting with Quick start guide, which covers the main points relevant to most new model implementations.
Interface code can be hosted by the package providing the core machine learning algorithm, or by a stand-alone "interface-only" package, using the template MLJExampleInterface.jl (see Where to place code implementing new models below). For a list of packages implementing the MLJ model API (natively, and in interface packages) see here.
Important
MLJModelInterface is a very light-weight interface allowing you to define your interface, but does not provide the functionality required to use or test your interface; this requires MLJBase. So, while you only need to add MLJModelInterface to your project's [deps], for testing purposes you need to add MLJBase to your project's [extras] and [targets]. In testing, simply use MLJBase in place of MLJModelInterface.
It is assumed the reader has read the Getting Started section of the MLJ manual. To implement the API described here, some familiarity with the following packages is also helpful:
ScientificTypes.jl (for specifying model requirements of data)
Distributions.jl (for probabilistic predictions)
CategoricalArrays.jl (essential if you are implementing a model handling data of
MulticlassorOrderedFactorscitype; familiarity withCategoricalPoolobjects required)Tables.jl (if your algorithm needs input data in a novel format).
In MLJ, the basic interface exposed to the user, built atop the model interface described here, is the machine interface. After a first reading of this document, the reader may wish to refer to MLJ Internals for context.