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https://issues.apache.org/jira/browse/SPARK-6348?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14364311#comment-14364311
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tanyinyan commented on SPARK-6348:
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Yes,I use a one-hot encoding before SVM , which is the 'sparsed before SVM '
exactly means ：）
> Enable useFeatureScaling in SVMWithSGD
> --------------------------------------
>
> Key: SPARK-6348
> URL: https://issues.apache.org/jira/browse/SPARK-6348
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.2.1
> Reporter: tanyinyan
> Priority: Minor
> Original Estimate: 2h
> Remaining Estimate: 2h
>
> Currently,useFeatureScaling are set to false by default in class
> GeneralizedLinearAlgorithm, and it is only enabled in
> LogisticRegressionWithLBFGS.
> SVMWithSGD class is a private class,train methods are provide in SVMWithSGD
> object. So there is no way to set useFeatureScaling when using SVM.
> I am using SVM on
> dataset(https://www.kaggle.com/c/avazu-ctr-prediction/data), train on the
> first day's dataset(ignore field id/device_id/device_ip, all remaining fields
> are concidered as categorical variable, and sparsed before SVM) and predict
> on the same data with threshold cleared, the predict result are all
> negative. Then i set useFeatureScaling to true, the predict result are
> normal(including negative and positive result)
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