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https://github.com/JuliaAI/MLJModels.jl

Home of the MLJ model registry and tools for model queries and mode code loading
https://github.com/JuliaAI/MLJModels.jl

julia machine-learning mlj

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Home of the MLJ model registry and tools for model queries and mode code loading

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# MLJModels



[![Build Status](https://github.com/JuliaAI/MLJModels.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/MLJModels.jl/actions)



Repository of the "built-in" models available for use in the

[MLJ](https://github.com/JuliaAI/MLJ.jl) MLJ machine

learning framework; and the home of the MLJ model registry.



For instructions on integrating a new model with MLJ visit

[here](https://JuliaAI.github.io/MLJ.jl/dev/adding_models_for_general_use/)



### Contents



 - [Who is this repo for?](#who-is-this-repo-for)

 - [What is provided here?](#what-is-provided-here)

 - [Instructions for updating the MLJ model registry](#instructions-for-updating-the-mlj-model-registry)



## Who is this repo for?



General users of the MLJ machine learning platform should refer to

[MLJ home page](https://JuliaAI.github.io/MLJ.jl/dev/)

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](#instructions-for-updating-the-mlj-model-registry) new MLJ

model interfaces, whether they be:



- implemented **natively** in a

  package providing the core machine learning algorithm, as in

  [`EvoTrees.jl`](https://github.com/Evovest/EvoTrees.jl/blob/master/src/MLJ.jl); or

  

- implemented in a separate **interface package**, such as

  [MLJDecisionTreeInterface.jl](https://github.com/JuliaAI/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](/src/registry/Models.toml).



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](/src/builtins), such as `OneHotEncoder`

  and `ConstantClassifier`. 



- the MLJ [model registry](src/registry/Metadata.toml), 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](https://JuliaAI.github.io/MLJ.jl/dev/adding_models_for_general_use/).

  



## 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](https://github.com/JuliaAI/MLJ.jl/issues). 



**Administrator instructions.** These are given in the

`MLJModels.@update` document string. After registering the model, make a PR to MLJ 

updating [this dictionary of model descriptors](https://github.com/JuliaAI/MLJ.jl/blob/dev/docs/ModelDescriptors.toml) 

to ensure the new models appear in the right places in MLJ's [Model Browser](https://JuliaAI.github.io/MLJ.jl/dev/model_browser/#Model-Browser)