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Posted to issues@spark.apache.org by "Imran Younus (JIRA)" <ji...@apache.org> on 2016/01/09 03:06:39 UTC
[jira] [Updated] (SPARK-12732) Fix LinearRegression.train for the
case when label is constant and fitIntercept=false
[ https://issues.apache.org/jira/browse/SPARK-12732?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Imran Younus updated SPARK-12732:
---------------------------------
Description:
If the target variable is constant, then the linear regression must check if the fitIntercept is true or false, and handle these two cases separately.
If the `fitIntercept` is true, then there is no training needed and we set the intercept equal to the mean of y.
But if the fit intercept is false, then the model should still train.
Currently, LinearRegression handles both cases in the same way. It doesn't train the model and sets the intercept equal to the mean of y. Which, means that it returns a non-zero intercept even when the user forces the regression through the origin.
was:
If the target variable is constant, then the linear regression must check if the fitIntercept is true or false, and handle these two cases separately.
If the fitIntercept is true, then there is no training needed and we set the intercept equal to the mean of y.
But if the fit intercept is false, then the model should still train.
Currently, LinearRegression handles both cases in the same way. It doesn't train the model and sets the intercept equal to the mean of y. Which, means that it returns a non-zero intercept even when the user forces the regression through the origin.
> Fix LinearRegression.train for the case when label is constant and fitIntercept=false
> -------------------------------------------------------------------------------------
>
> Key: SPARK-12732
> URL: https://issues.apache.org/jira/browse/SPARK-12732
> Project: Spark
> Issue Type: Bug
> Reporter: Imran Younus
> Priority: Minor
>
> If the target variable is constant, then the linear regression must check if the fitIntercept is true or false, and handle these two cases separately.
> If the `fitIntercept` is true, then there is no training needed and we set the intercept equal to the mean of y.
> But if the fit intercept is false, then the model should still train.
> Currently, LinearRegression handles both cases in the same way. It doesn't train the model and sets the intercept equal to the mean of y. Which, means that it returns a non-zero intercept even when the user forces the regression through the origin.
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