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https://issues.apache.org/jira/browse/SPARK-20949?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sungjun Kim updated SPARK-20949:
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Description:
Spark OneHotEncoder is different from that of scikit learn.
It makes an entry into zero vector.
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?
was:
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?
> 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 zero vector.
> 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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