Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/10940#discussion_r51052743 --- 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 -- BTW, this technique is already used here by dividing the std of feature. Note that you need to make sure `value != 0`, and if `value == 0`, the coefficient of that feature will be zero.
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