Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/13729#discussion_r67528031
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -990,11 +987,11 @@ private class LogisticAggregator(
var sum = 0.0
features.foreachActive { (index, value) =>
if (featuresStd(index) != 0.0 && value != 0.0) {
- sum += localCoefficientsArray(index) * (value /
featuresStd(index))
+ sum += coefficientsArray(index) * (value /
featuresStd(index))
--- End diff --
I believe it is by design and not being overlooked (perhaps the mean was
also passed in case future versions decided it was necessary to mean center the
data). This comment is from MLlib:
```
* Here, if useFeatureScaling is enabled, we will standardize the
training features by dividing
* the variance of each column (without subtracting the mean), and
train the model in the
* scaled space. Then we transform the coefficients from the scaled
space to the original scale
* as GLMNET and LIBSVM do.
```
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