DB Tsai created SPARK-7780:
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Summary: The intercept in LogisticRegressionWithLBFGS should not
be regularized
Key: SPARK-7780
URL: https://issues.apache.org/jira/browse/SPARK-7780
Project: Spark
Issue Type: Bug
Components: MLlib
Reporter: DB Tsai
The intercept in Logistic Regression represents a prior on categories which
should not be regularized. In MLlib, the regularization is handled through
`Updater`, and the `Updater` penalizes all the components without excluding the
intercept which resulting poor training accuracy with regularization.
The new implementation in ML framework handles this properly, and we should
call the implementation in ML from MLlib since majority of users are still
using MLlib api.
Note that both of them are doing feature scalings to improve the convergence,
and the only difference is ML version doesn't regularize the intercept. As a
result, when lambda is zero, they will converge to the same solution.
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