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Posted to issues@spark.apache.org by "Nick Pentreath (JIRA)" <ji...@apache.org> on 2016/11/21 06:08:58 UTC
[jira] [Resolved] (SPARK-18456) Use matrix abstraction for
LogisitRegression coefficients during training
[ https://issues.apache.org/jira/browse/SPARK-18456?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Nick Pentreath resolved SPARK-18456.
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Resolution: Fixed
Assignee: Seth Hendrickson
Fix Version/s: 2.1.0
> Use matrix abstraction for LogisitRegression coefficients during training
> -------------------------------------------------------------------------
>
> Key: SPARK-18456
> URL: https://issues.apache.org/jira/browse/SPARK-18456
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Seth Hendrickson
> Assignee: Seth Hendrickson
> Priority: Minor
> Fix For: 2.1.0
>
>
> This is a follow up from [SPARK-18060|https://issues.apache.org/jira/browse/SPARK-18060]. The current code for logistic regression relies on manually indexing flat arrays of column major coefficients, which can be messy and is hard to maintain. We can use a matrix abstraction instead of a flat array to simplify things. This will make the code easier to read and maintain.
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