# How to create a MLJModelInterface.Model interface of a complex model?

**URL:** <https://discourse.julialang.org/t/how-to-create-a-mljmodelinterface-model-interface-of-a-complex-model/55860>\
**Category:** Machine Learning\
**Tags:** mlj\
**Created:** [February 23, 2021, 4:24pm UTC](https://discourse.julialang.org/t/how-to-create-a-mljmodelinterface-model-interface-of-a-complex-model/55860 "2021-02-23T16:24:42Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![sylvaticus](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sylvaticus/32/203883_2.png) [@sylvaticus](https://discourse.julialang.org/u/sylvaticus)\
**Post date:** [February 23, 2021, 4:24pm UTC](https://discourse.julialang.org/t/how-to-create-a-mljmodelinterface-model-interface-of-a-complex-model/55860/1 "2021-02-23T16:24:42Z")

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Hi there,  
I am trying to build a MLJ interface for some ML algorithms in the [BetaML](https://github.com/sylvaticus/BetaML.jl) package.

I am starting from the Decision Trees, but I have a few questions.

The function creating (and fitting) the tree is:

```julia
buildTree(x, y::Array{Ty,1}; maxDepth = size(x,1), minGain=0.0, minRecords=2, maxFeatures=size(x,2), forceClassification=false, splittingCriterion = (Ty <: Number && !forceClassification) ? variance : gini, mCols=nothing) where {Ty}

```

1. As you can see some parameters depend by default by the data, like `maxFeatures` depends on the dimensionality of the explanation variables. I understood that model parameters should be part of the model struct, but how do I set defaults without seeing the data ?
2. Even more hard, the algorithm that I am trying to wrap automatically performs a regression or a classification task (and, in the later case, it returns a probability distribution) depending on the type of the label, with the option to override the task with `forceClassification`. As in ML there are different type of models, probabilistic and deterministic, which one do I choose ? Or should I wrap it as two separate MLJ models ?
3. Most of my models support `Missing` data in the input. I read that `Missing` is a scientific type per se. Should I declare an Union of supported types then, including the `Missing` ?
4. I have a case where my model doesn’t fit the fit/predict workflow, that is a model that (using GMM/EM) predicts the missing values in a matrix, based on the degree of similarities of the other elements of the columns to the other rows. How to I wrap it with MLJ ?
5. Where can I find real-case examples ? For example, [DecisionTrees.jl](https://github.com/bensadeghi/DecisionTree.jl) seems to be available through MLJ, but there is no code in the GitHub repo concerning MLJ…

Thank you!

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<div class="post-metadata">

**Author:** ![sylvaticus](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sylvaticus/32/203883_2.png) [@sylvaticus](https://discourse.julialang.org/u/sylvaticus)\
**Post date:** [February 25, 2021, 10:26am UTC](https://discourse.julialang.org/t/how-to-create-a-mljmodelinterface-model-interface-of-a-complex-model/55860/2 "2021-02-25T10:26:19Z")

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While I did somehow managed to write [the MLJ interface for a deterministic model](https://github.com/sylvaticus/BetaML.jl/blob/master/src/Trees.jl#L657), I am trying to write the interface for a probabilistic model whose `predict(model,X)` method returns a vector of dictionary of `label => prob`.

I normally use arrays of T for the Y, but I saw that it works also with Y being a `CategoricalArray`.

However I am stuck here now, and don’t know hot to return the prediction in the format wanted by MLJ.

[EDIT]: I move this post under this Thread to consolidate it…