[
https://issues.apache.org/jira/browse/SPARK-18456?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15668689#comment-15668689
]
Apache Spark commented on SPARK-18456:
--------------------------------------
User 'sethah' has created a pull request for this issue:
https://github.com/apache/spark/pull/15893
> 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
> Priority: Minor
>
> 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
