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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2014/12/23 01:43:13 UTC
[jira] [Resolved] (SPARK-4907) Inconsistent loss and gradient in
LeastSquaresGradient compared with R
[ https://issues.apache.org/jira/browse/SPARK-4907?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiangrui Meng resolved SPARK-4907.
----------------------------------
Resolution: Fixed
Fix Version/s: 1.3.0
Issue resolved by pull request 3746
[https://github.com/apache/spark/pull/3746]
> Inconsistent loss and gradient in LeastSquaresGradient compared with R
> ----------------------------------------------------------------------
>
> Key: SPARK-4907
> URL: https://issues.apache.org/jira/browse/SPARK-4907
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Reporter: DB Tsai
> Fix For: 1.3.0
>
>
> In most of the academic paper and algorithm implementations, people use L = 1/2n ||A weights-y||^2 instead of L = 1/n ||A weights-y||^2 for least-squared loss. See Eq. (1) in http://web.stanford.edu/~hastie/Papers/glmnet.pdf
> Since MLlib uses different convention, this will result different residuals and all the stats properties will be different from GLMNET package in R. The model coefficients will be still the same under this change.
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