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https://issues.apache.org/jira/browse/SPARK-7888?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14590380#comment-14590380
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DB Tsai commented on SPARK-7888:
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Last night, I figured out how to do this. If you look at the comment from line
237,
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
The means from features are removed before training (of course, removing the
means will densify the vector which is not good, so we have equivalent
formula), and then we fit a linear regression on the centralized data without
intercept. When we interpret the model, since it's trained in the centralized
data, the intercept is not required of course. However, in the original
problem, this centralization can be translated into intercept. As a result, we
can compute the intercept using closed form in line 183. You may want to draw
couple pictures to help you visualize this.
Back to the topic of disabling intercept, you can think this as training the
model without `centralization` so the line will cross the origin.
> Be able to disable intercept in Linear Regression in ML package
> ---------------------------------------------------------------
>
> Key: SPARK-7888
> URL: https://issues.apache.org/jira/browse/SPARK-7888
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: DB Tsai
> Assignee: holdenk
>
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