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Posted to issues@spark.apache.org by "DB Tsai (JIRA)" <ji...@apache.org> on 2016/07/05 15:30:11 UTC
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm
with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15362637#comment-15362637 ]
DB Tsai commented on SPARK-3181:
--------------------------------
I had worked with [~MechCoder] to implement huber estimator in python-scikit which had been merged. https://github.com/scikit-learn/scikit-learn/pull/5291
[~yanboliang] showed interest in porting it to Spark.
> 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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