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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