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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