[jira] [Created] (SPARK-20949) Is there another reason for the onehotencoder is different from scikit learn than specified in scaladoc?

["Sungjun Kim (JIRA)" <ji...@apache.org>]

Sungjun Kim created SPARK-20949:
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             Summary: Is there another reason for the onehotencoder is different from scikit learn than specified in scaladoc?
                 Key: SPARK-20949
                 URL: https://issues.apache.org/jira/browse/SPARK-20949
             Project: Spark
          Issue Type: Question
          Components: ML
    Affects Versions: 1.6.2
            Reporter: Sungjun Kim
            Priority: Minor


Spark OneHotEncoder is different from that of scikit learn. 
It makes an entry into a vector having components are all zeros.
In scaladoc, there is a reason for this. It says that "it makes the vector entries sum up to one, and hence linearly dependent." But I don't think this is correct. Consider vectors [1.0, 0.0], [0.0, 1.0]. They sums 1 but are linearly independent obviously. Am I missing something? or Is there any other reason?



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