Fan Jiang created SPARK-3181:
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Summary: 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
Affects Versions: 1.0.2
Reporter: Fan Jiang
Priority: Critical
Fix For: 1.1.1, 1.2.0
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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