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https://issues.apache.org/jira/browse/SPARK-17001?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15422572#comment-15422572
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Apache Spark commented on SPARK-17001:
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User 'srowen' has created a pull request for this issue:
https://github.com/apache/spark/pull/14663
> Enable standardScaler to standardize sparse vectors when withMean=True
> ----------------------------------------------------------------------
>
> Key: SPARK-17001
> URL: https://issues.apache.org/jira/browse/SPARK-17001
> Project: Spark
> Issue Type: Improvement
> Affects Versions: 2.0.0
> Reporter: Tobi Bosede
> Priority: Minor
>
> When withMean = true, StandardScaler will not handle sparse vectors, and
> instead throw an exception. This is presumably because subtracting the mean
> makes a sparse vector dense, and this can be undesirable.
> However, VectorAssembler generates vectors that may be a mix of sparse and
> dense, even when vectors are smallish, depending on their values. It's common
> to feed this into StandardScaler, but it would fail sometimes depending on
> the input if withMean = true. This is kind of surprising.
> StandardScaler should go ahead and operate on sparse vectors and subtract the
> mean, if explicitly asked to do so with withMean, on the theory that the user
> knows what he/she is doing, and there is otherwise no way to make this work.
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