Github user iyounus commented on a diff in the pull request:
https://github.com/apache/spark/pull/10274#discussion_r49543068
--- 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 --
The `WeightedLeastSquares` class is private and its instantiated in
`LinearRegression` class where `standerizeLabe` parameter is hard wired to be
`true`. So, the user doesn't have any control on this parameter.
We can throw an exception when `yStd` is zero and `regParam` is non-zero.
But, if that is the case, then, why not to throw exception when `yStd` is zero
regardless of what other parameters are? I cannot think of any interpretation
of the model in this case.
The option could be to simply log a warning when we don't standardize the
label here.
Let me know what you think.
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