Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/10940#discussion_r51051112
--- 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 --
The constant `value` can be really large or very small negatively. The
optimizer may not be able to converge well in this case. I don't prove or try
it yet, but mathematically, with the following changes, this should be solving
identical problem.
```scala
// the training dataset is not standardized.
if (featuresStd(index) != 0.0) regParamL1 / featuresStd(index) else
regParamL1 / featuresMean(index)
```
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