Github user atalwalkar commented on a diff in the pull request: https://github.com/apache/spark/pull/2068#discussion_r16514387 --- 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. + +Standardization can not only improve the convergence rate during the optimization process, but also +avoid the problem that when training linear models with regularization against a feature having +a variance that is orders of magnitude larger than others, it might dominate the objective function +and make the estimator unable to learn from other features correctly as expected. + +### Model Fitting + +[`StandardScaler`](api/scala/index.html#org.apache.spark.mllib.feature.StandardScaler) has the +following parameters in the constructor, --- End diff -- "," -> ":"
--- 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