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https://issues.apache.org/jira/browse/SPARK-2979?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-2979.
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Resolution: Fixed
Fix Version/s: 1.1.0
Issue resolved by pull request 1897
[https://github.com/apache/spark/pull/1897]
> Improve the convergence rate by minimizing the condition number in LOR with
> LBFGS
> ---------------------------------------------------------------------------------
>
> Key: SPARK-2979
> URL: https://issues.apache.org/jira/browse/SPARK-2979
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: DB Tsai
> Assignee: DB Tsai
> Fix For: 1.1.0
>
>
> Scaling to minimize the condition number:
>
> During the optimization process, the convergence (rate) depends on the
> condition number of the training dataset. Scaling the variables often reduces
> this condition number, thus mproving the convergence rate dramatically.
> Without reducing the condition number, some training datasets mixing the
> columns with different scales may not be able to converge.
>
> GLMNET and LIBSVM packages perform the scaling to reduce the condition
> number, and return the weights in the original scale.
> See page 9 in http://cran.r-project.org/web/packages/glmnet/glmnet.pdf
>
> Here, if useFeatureScaling is enabled, we will standardize the training
> features by dividing the variance of each column (without subtracting the
> mean), and train the model in the scaled space. Then we transform the
> coefficients from the scaled space to the original scale as GLMNET and LIBSVM
> do.
>
> Currently, it's only enabled in LogisticRegressionWithLBFGS
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