Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/7080#discussion_r33505540
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -149,15 +158,26 @@ class LogisticRegression(override val uid: String)
val regParamL1 = $(elasticNetParam) * $(regParam)
val regParamL2 = (1.0 - $(elasticNetParam)) * $(regParam)
- val costFun = new LogisticCostFun(instances, numClasses,
$(fitIntercept),
+ val costFun = new LogisticCostFun(instances, numClasses,
$(fitIntercept), $(standardization),
featuresStd, featuresMean, regParamL2)
val optimizer = if ($(elasticNetParam) == 0.0 || $(regParam) == 0.0) {
new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
} else {
- // Remove the L1 penalization on the intercept
def regParamL1Fun = (index: Int) => {
- if (index == numFeatures) 0.0 else regParamL1
+ // Remove the L1 penalization on the intercept
+ if (index == numFeatures) 0.0 else {
+ if ($(standardization)) {
+ regParamL1
+ } else {
+ if (featuresStd(index) != 0.0) {
+ // If `standardization` is false, each component is
penalized differently to
--- End diff --
Maybe mention that the data is standardized, which is why we are performing
this reverse standardization for this?
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