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https://issues.apache.org/jira/browse/SPARK-21005?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-21005:
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    Labels: bulk-closed  (was: )

> 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
>              Labels: bulk-closed
>
> 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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