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From "Yanbo Liang (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
Date Thu, 18 May 2017 04:50:04 GMT

    [ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16015198#comment-16015198
] 

Yanbo Liang commented on SPARK-3181:
------------------------------------

[~mlnick] Yeah, the Breeze bug has been fixed. I marked this JIRA target for 2.3 and will
update my PR soon. Thanks.

> 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: ML
>            Reporter: Fan Jiang
>            Assignee: Yanbo Liang
>              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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