Github user MLnick commented on the issue:
https://github.com/apache/spark/pull/18896
The model is always trained in the scaled space regardless of the
`standardization` param.
e.g. in the test case doing:
```scala
val model = mlr.fit(multinomialDatasetWithZeroVar)
println(model.interceptVector)
println(model.coefficientMatrix)
val model2 =
mlr.setStandardization(false).fit(multinomialDatasetWithZeroVar)
println(model2.interceptVector)
println(model2.coefficientMatrix)
```
Gives you
```
[0.2659042827402973,0.5361544814545004,-0.8020587641947977]
0.18818263258382864 0.0
-0.024143986869901088 0.0
-0.16403864571392754 0.0
[0.2659042827402915,0.5361544814544981,-0.8020587641947895]
0.18818263258382964 0.0
-0.024143986869900692 0.0
-0.16403864571392893 0.0
```
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