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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-18456) Use matrix abstraction for LogisitRegression coefficients during training
Date Tue, 15 Nov 2016 23:12:58 GMT

     [ https://issues.apache.org/jira/browse/SPARK-18456?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-18456:
------------------------------------

    Assignee: Apache Spark

> 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: Apache Spark
>            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.



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