[jira] [Updated] (SPARK-13777) Weighted Least Squares fails when there are features with identical values

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-13777?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joseph K. Bradley updated SPARK-13777:
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    Summary: Weighted Least Squares fails when there are features with identical values  (was: Weighted Leaset Squares fails when there are features with identical values.)

> Weighted Least Squares fails when there are features with identical values
> --------------------------------------------------------------------------
>
>                 Key: SPARK-13777
>                 URL: https://issues.apache.org/jira/browse/SPARK-13777
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>            Reporter: Imran Younus
>            Priority: Minor
>
> "normal" solver in LinearRegression uses Cholesky decomposition to calculate the coefficients. If the data has features with identical values (zero variance), then (A^T A) matrix is not positive definite any more and the Cholesky decomposition fails.
> For the same case, "l-bfgs" solver sets the coefficients of these constant features to zero and produces valid coefficients for the rest of the features. This behaviour is consistent with glmnet in R. "normal" solver should also do the same.



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