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https://issues.apache.org/jira/browse/SPARK-34448?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17291559#comment-17291559
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zhengruifeng commented on SPARK-34448:
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1, I just make a simple
impl(https://github.com/apache/spark/pull/31657/commits/49141bbb178ac28af3263efa31299f8eb835830b)
that internally center the vectors,
then the solution seems ok.
{code:java}
Coefficients: [0.29886424895473795,0.20097637066670226,0.0081964409252861]
Intercept: -4.0089605363236664 {code}
Moreover, it converge much faster.
2, however, if we center the vectors, then a lot of (>24) existing testsuite
fails. It seems existing scaling was designed on purpose.
3, existing scaling (x/std_x) was added in
https://issues.apache.org/jira/browse/SPARK-7262 , and aimed to keep in line
with {{glmnet}}. But I am not familiar with {{glmnet}}.
{{In sklearn, linear_model.LogisticRegression}} does not standardize input
vectors, while other linear models (i.e linear_model.ElasticNet) will
'subtracting the mean and dividing by the l2-norm'.
> Binary logistic regression incorrectly computes the intercept and
> coefficients when data is not centered
> --------------------------------------------------------------------------------------------------------
>
> Key: SPARK-34448
> URL: https://issues.apache.org/jira/browse/SPARK-34448
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
> Affects Versions: 2.4.5, 3.0.0
> Reporter: Yakov Kerzhner
> Priority: Major
> Labels: correctness
>
> I have written up a fairly detailed gist that includes code to reproduce the
> bug, as well as the output of the code and some commentary:
> [https://gist.github.com/ykerzhner/51358780a6a4cc33266515f17bf98a96]
> To summarize: under certain conditions, the minimization that fits a binary
> logistic regression contains a bug that pulls the intercept value towards the
> log(odds) of the target data. This is mathematically only correct when the
> data comes from distributions with zero means. In general, this gives
> incorrect intercept values, and consequently incorrect coefficients as well.
> As I am not so familiar with the spark code base, I have not been able to
> find this bug within the spark code itself. A hint to this bug is here:
> [https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala#L894-L904]
> based on the code, I don't believe that the features have zero means at this
> point, and so this heuristic is incorrect. But an incorrect starting point
> does not explain this bug. The minimizer should drift to the correct place.
> I was not able to find the code of the actual objective function that is
> being minimized.
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