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Date: Fri, 7 Aug 2015 00:20:45 +0000 (UTC)
From: "Apache Spark (JIRA)" <jira@apache.org>
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Message-ID: <JIRA.12736020.1408698999000.209.1438906845665@Atlassian.JIRA>
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Subject: [jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm
 with Huber Estimator
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    [ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14661108#comment-14661108 ] 

Apache Spark commented on SPARK-3181:
-------------------------------------

User 'fjiang6' has created a pull request for this issue:
https://github.com/apache/spark/pull/8013

> Add Robust Regression Algorithm with Huber Estimator
> ----------------------------------------------------
>
>                 Key: SPARK-3181
>                 URL: https://issues.apache.org/jira/browse/SPARK-3181
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Fan Jiang
>            Assignee: Fan Jiang
>              Labels: features
>   Original Estimate: 0h
>  Remaining Estimate: 0h
>
> Linear least square estimates assume the error has normal distribution and can behave badly when the errors are heavy-tailed. In practical we get various types of data. We need to include Robust Regression  to employ a fitting criterion that is not as vulnerable as least square.
> In 1973, Huber introduced M-estimation for regression which stands for "maximum likelihood type". The method is resistant to outliers in the response variable and has been widely used.
> The new feature for MLlib will contain 3 new files
> /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala
> /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala
> /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala
> and one new class HuberRobustGradient in 
> /main/scala/org/apache/spark/mllib/optimization/Gradient.scala


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