Uploaded image for project: 'Spark'
  1. Spark
  2. SPARK-3181

Add Robust Regression Algorithm with Huber Estimator

Attach filesAttach ScreenshotVotersWatch issueWatchersCreate sub-taskLinkCloneUpdate Comment AuthorReplace String in CommentUpdate Comment VisibilityDelete Comments
    XMLWordPrintableJSON

Details

    • New Feature
    • Status: Resolved
    • Major
    • Resolution: Fixed
    • 2.2.0
    • 2.3.0
    • ML

    Description

      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

      Attachments

        Issue Links

        Activity

          This comment will be Viewable by All Users Viewable by All Users
          Cancel

          People

            yanboliang Yanbo Liang
            fjiang6 Fan Jiang
            DB Tsai DB Tsai
            Votes:
            0 Vote for this issue
            Watchers:
            13 Start watching this issue

            Dates

              Created:
              Updated:
              Resolved:

              Slack

                Issue deployment