# Problem using MLJ and Multiclass scitypes

**URL:** <https://discourse.julialang.org/t/problem-using-mlj-and-multiclass-scitypes/49412>\
**Category:** Machine Learning\
**Created:** [November 1, 2020, 2:02pm UTC](https://discourse.julialang.org/t/problem-using-mlj-and-multiclass-scitypes/49412 "2020-11-01T14:02:31Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![dmolina](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dmolina/32/5246_2.png) [@dmolina](https://discourse.julialang.org/u/dmolina)\
**Post date:** [November 1, 2020, 2:02pm UTC](https://discourse.julialang.org/t/problem-using-mlj-and-multiclass-scitypes/49412/1 "2020-11-01T14:02:31Z")

</div>

Hello everyone,

I am teaching a course using Scikit-Learn, and I wanted to translate my materials to MLJ (for a possible future 🙂 ), but I am having many problems. I am using MLJ#dev.

First, I have a dataframe with only 4 attributes, all of them are Multiclass and OrderedFactor. I am trying to apply a DecisionTreeClasifier, that it should be use them as it is said with  
[https://alan-turing-institute.github.io/MLJ.jl/dev/getting\_started/#A-preview-of-data-type-specification-in-MLJ-1](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/#A-preview-of-data-type-specification-in-MLJ-1)

And the types are rights, I think:

```julia
println(ScientificTypes.scitype(input_test))

```

gives me:

```julia
Table{Union{AbstractArray{Multiclass{6},1}, AbstractArray{Multiclass{5},1}, AbstractArray{OrderedFactor{4},1}}}

```

and target\_test is from type:

```julia
AbstractArray{Multiclass{2},1}

```

So, I suppose I can apply it.

However, when I do

```julia
march = machine(tree, input_test, target_test)

```

It gives me:

```nohighlight
┌ Warning: The scitype of `X`, in `machine(model, X, ...)` is incompatible with `model=DecisionTreeClassifier @140`:
│ scitype(X) = Table{Union{AbstractArray{Multiclass{6},1}, AbstractArray{Multiclass{5},1}, AbstractArray{OrderedFactor{4},1}}}
│ input_scitype(model) = Table{var"#s45"} where var"#s45"<:Union{AbstractArray{var"#s13",1} where var"#s13"<:Continuous, AbstractArray{var"#s13",1} where var"#s13"<:Count, AbstractArray{var"#s13",1} where var"#s13"<:OrderedFactor}.

```

Which could be the reason?

The evaluate! is working with cross\_entropy. However, I am not sure it is working well for the previous warning message.

---

<div class="post-metadata">

**Author:** ![tlienart](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tlienart/32/7640_2.png) [@tlienart](https://discourse.julialang.org/u/tlienart)\
**Post date:** [November 1, 2020, 3:06pm UTC](https://discourse.julialang.org/t/problem-using-mlj-and-multiclass-scitypes/49412/2 "2020-11-01T15:06:55Z")

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So it’s telling you that the expected scitype for a DecisionTreeClassifier is either Count or Continuous or Ordered Factored and that you’re feeding it something else (multi class) which is not ordered.

So what you should do is `coerce` the multi class so that they have an ordering. Or you could encode the multi class features first and then apply the decision tree.

What it’s doing here is that it’s effectively taking the multi class features as if they were ordered, using the ordering stored from the categorical array (lexicographic if I’m not mistaken); this may be fine but maybe not (which is why there’s a warning).

Note, possibly of interest: [https://github.com/bensadeghi/DecisionTree.jl/issues/92](https://github.com/bensadeghi/DecisionTree.jl/issues/92)

---

<div class="post-metadata">

**Author:** ![dmolina](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dmolina/32/5246_2.png) [@dmolina](https://discourse.julialang.org/u/dmolina)\
**Post date:** [November 1, 2020, 3:44pm UTC](https://discourse.julialang.org/t/problem-using-mlj-and-multiclass-scitypes/49412/3 "2020-11-01T15:44:27Z")

</div>

Thank you, @tlienart, I did not know how to understand the warning, I didn’t realise that it was telling me the allowed scitypes by the model. So, I guess, I can coerce all field to OrderedFactors, or using a ContinuousEncoder.

Another question, when I use a model like that, the evaluate! expect a model like cross\_entropy, is there a way to use another one (like accuracy)? The warning suggest to use check\_measure to false, but when I do that, it gives me an error message.

Thanks in advance.

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**Author:** ![tlienart](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tlienart/32/7640_2.png) [@tlienart](https://discourse.julialang.org/u/tlienart)\
**Post date:** [November 1, 2020, 6:48pm UTC](https://discourse.julialang.org/t/problem-using-mlj-and-multiclass-scitypes/49412/4 "2020-11-01T18:48:06Z")

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I’m not familiar with `check_measure` (the docs should have something on it though, otherwise you might want to open an issue about it but typically Anthony writes pretty exhaustive docs).

That said, I think it’s important to get the philosophy of MLJ: models have some input type they allow (as you saw) and also an “output type”, i.e. they can be Deterministic or Probabilistic. The former returns point wise estimators whereas the latter returns scores (“probabilities”). You can get this using the `info` function; if you’re curious about the code you can also see that in the interface: [https://github.com/alan-turing-institute/MLJModels.jl/blob/52a6e64807577e256b15b7be5f26383fa5ca3f2a/src/DecisionTree.jl#L80](https://github.com/alan-turing-institute/MLJModels.jl/blob/52a6e64807577e256b15b7be5f26383fa5ca3f2a/src/DecisionTree.jl#L80)

What that means is that, when you call `predict` on a machine with a decision tree model, you get a score out of it, not just a single class. To compute a metric of performance such as the accuracy which requires a point-wise estimator, you need to pass that score through something. e.g. typically take the class with the highest score.

So in short if you have a 3-class classification problem, the output of a prediction of the machine with a DTC might be `[0.3, 0.2, 0.5]` and to compute the accuracy then you have to do one extra step which is to take the class corresponding to the highest score (`0.5`). You can do this automatically by using the `mode` function. You can do both together using `predict_mode` (all this is in the docs btw, there’s also [a tutorial](https://alan-turing-institute.github.io/DataScienceTutorials.jl/getting-started/fit-and-predict/#training_and_testing_a_supervised_model) on it)

Cross Entropy expects a score which explains why it worked fine out of the box with it.

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<div class="post-metadata">

**Author:** ![dmolina](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dmolina/32/5246_2.png) [@dmolina](https://discourse.julialang.org/u/dmolina)\
**Post date:** [November 2, 2020, 5:23pm UTC](https://discourse.julialang.org/t/problem-using-mlj-and-multiclass-scitypes/49412/5 "2020-11-02T17:23:05Z")

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Thank you for the complete explanaition, I have already applied the suggestions to obtain the measure.

It is true that both frameworks (Scikit-learn and MLJ) have different philosophies, and sometimes a simple _translation_ in the solution of a problem is not intuitive. However, after reading carefully the excelent documentation it is simple to do it in a more MLJish way :-). When I have finished the examples, both in python with scikit-learn and in Julia with MLJ, I will post them.