Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/10940#discussion_r51051112 --- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala --- @@ -341,11 +341,11 @@ class LogisticRegression @Since("1.2.0") ( regParamL1 } else { // If `standardization` is false, we still standardize the data - // to improve the rate of convergence; as a result, we have to - // perform this reverse standardization by penalizing each component - // differently to get effectively the same objective function when + // to improve the rate of convergence unless the standard deviation is zero; + // as a result, we have to perform this reverse standardization by penalizing + // each component differently to get effectively the same objective function when // the training dataset is not standardized. - if (featuresStd(index) != 0.0) regParamL1 / featuresStd(index) else 0.0 + if (featuresStd(index) != 0.0) regParamL1 / featuresStd(index) else regParamL1 --- End diff -- The constant `value` can be really large or very small negatively. The optimizer may not be able to converge well in this case. I don't prove or try it yet, but mathematically, with the following changes, this should be solving identical problem. ```scala // the training dataset is not standardized. if (featuresStd(index) != 0.0) regParamL1 / featuresStd(index) else regParamL1 / featuresMean(index) ```
--- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org