Joseph K. Bradley commented on SPARK-21005:

I don't actually see why this is a problem: If a feature is categorical, we 
should not silently convert it to continuous.  To use a high-arity categorical 
feature in a decision tree, one should convert it to a different representation 
first, such as hashing to a set of bins with HashingTF.

That said, I do think we should clarify this behavior in the VectorIndexer 
docstring.  I know it's been a long time since you sent your PR, but would you 
want to update it to simply update the docs?  If you're busy now, I'd be happy 
to take it over though.  Thanks!

> VectorIndexerModel does not prepare output column field correctly
> -----------------------------------------------------------------
>                 Key: SPARK-21005
>                 URL: https://issues.apache.org/jira/browse/SPARK-21005
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 2.1.1
>            Reporter: Chen Lin
>            Priority: Major
> From my understanding through reading the documentation,  VectorIndexer 
> decides which features should be categorical based on the number of distinct 
> values, where features with at most maxCategories are declared categorical. 
> Meanwhile, those features which exceed maxCategories are declared continuous. 
> Currently, VectorIndexerModel works all right with a dataset which has empty 
> schema. However, when VectorIndexerModel is transforming on a dataset with 
> `ML_ATTR` metadata, it may not output the expected result. For example, a 
> feature with nominal attribute which has distinct values exceeding 
> maxCategorie will not be treated as a continuous feature as we expected but 
> still a categorical feature. Thus, it may cause all the tree-based algorithms 
> (like Decision Tree, Random Forest, GBDT, etc.) throw errors as "DecisionTree 
> requires maxBins (= $maxPossibleBins) to be at least as large as the number 
> of values in each categorical feature, but categorical feature $maxCategory 
> has $maxCategoriesPerFeature values. Considering remove this and other 
> categorical features with a large number of values, or add more training 
> examples.".
> Correct me if my understanding is wrong.
> I will submit a PR soon to resolve this issue.

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