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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