MLJModels
Repository of the "built-in" models available for use in the MLJ MLJ machine learning framework; and the home of the MLJ model registry.
For instructions on integrating a new model with MLJ visit here
Contents
Who is this repo for?
General users of the MLJ machine learning platform should refer to MLJ home page for usage and installation instructions. MLJModels is a dependency of MLJ that the general user can ignore.
This repository is for developers wishing to register new MLJ model interfaces, whether they be:
-
implemented natively in a package providing the core machine learning algorithm, as in
EvoTrees.jl; or -
implemented in a separate interface package, such as MLJDecisionTreeInterface.jl.
It also a place for developers to add models (mostly transformers)
such as OneHotEncoder, that are exported for "built-in" use in
MLJ. (In the future these models may live in a separate package.)
To list all model interfaces currently registered, do using MLJ or
using MLJModels and run:
-
localmodels()to list built-in models (updated when external models are loaded with@load) -
models()to list all registered models, or see this list.
Recall that an interface is loaded from within MLJ, together with the
package providing the underlying algorithm, using the syntax @load RidgeRegressor pkg=GLM, where the pkg keyword is only necessary in
ambiguous cases.
What is provided here?
MLJModels contains:
-
transformers to be pre-loaded into MLJ, located at /src/builtins, such as
OneHotEncoderandConstantClassifier. -
the MLJ model registry, listing all models that can be called from MLJ using
@load. Package developers can register new models by implementing the MLJ interface in their package and following these instructions.
Instructions for updating the MLJ model registry
Generally model registration is performed by administrators. If you have an interface you would like registered, open an issue here.
Administrator instructions. These are given in the
MLJModels.@update document string.