Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/2068#discussion_r16561045 --- Diff: docs/mllib-feature-extraction.md --- @@ -70,4 +70,110 @@ for((synonym, cosineSimilarity) <- synonyms) { </div> </div> -## TFIDF \ No newline at end of file +## TFIDF + +## StandardScaler + +Standardizes features by scaling to unit variance and/or removing the mean using column summary +statistics on the samples in the training set. For example, RBF kernel of Support Vector Machines +or the L1 and L2 regularized linear models typically assume that all features have unit variance +and/or zero mean. --- End diff -- How about I say "For example, RBF kernel of Support Vector Machines or the L1 and L2 regularized linear models typically works better when all features have unit variance and/or zero mean." I actually have this statement from scikit documentation. http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
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