Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/10940#discussion_r51054476
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -341,11 +341,11 @@ class LogisticRegression @Since("1.2.0") (
regParamL1
} else {
// If `standardization` is false, we still standardize the
data
- // to improve the rate of convergence; as a result, we
have to
- // perform this reverse standardization by penalizing each
component
- // differently to get effectively the same objective
function when
+ // to improve the rate of convergence unless the standard
deviation is zero;
+ // as a result, we have to perform this reverse
standardization by penalizing
+ // each component differently to get effectively the same
objective function when
// the training dataset is not standardized.
- if (featuresStd(index) != 0.0) regParamL1 /
featuresStd(index) else 0.0
+ if (featuresStd(index) != 0.0) regParamL1 /
featuresStd(index) else regParamL1
--- End diff --
@coderxiang You may try it out. When `value` is very small like `1e-6` as a
constant comparing the rest of the features, the corresponding coefficient will
be very large. In this case, the optimization on coefficients will be on
different scales, and this often causes some convergence issue in line search.
Similar argument can be made when the constant `value` is very large
comparing the rest of the features.
That's why we still standardize the features even users ask
`standardization == false`.
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