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Posted to issues@spark.apache.org by "Seth Hendrickson (JIRA)" <ji...@apache.org> on 2016/11/15 22:45:58 UTC

[jira] [Created] (SPARK-18456) Use matrix abstraction for LogisitRegression coefficients during training

Seth Hendrickson created SPARK-18456:
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             Summary: 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.



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