Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/10274#discussion_r49654189
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala ---
@@ -94,8 +110,7 @@ private[ml] class WeightedLeastSquares(
if (standardizeFeatures) {
lambda *= aVar(j - 2)
}
- if (standardizeLabel) {
- // TODO: handle the case when bStd = 0
+ if (standardizeLabel && bStd != 0) {
--- End diff --
It's interesting to see that when regularization is zero, with/without
standardization on labels or features will not change the solution of Linear
Regression which you can experiment.
As a result, the only issue that the model will be non-interpretable will
be `yStd` is zeo and `regParam` is non-zero. You can have a `require` there
with proper message. I think logging a warning will be probably very easy for
users to ignore. Thanks.
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