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From "tanyinyan (JIRA)" <>
Subject [jira] [Created] (SPARK-6348) Enable useFeatureScaling in SVMWithSGD
Date Mon, 16 Mar 2015 06:43:38 GMT
tanyinyan created SPARK-6348:

             Summary: Enable useFeatureScaling in SVMWithSGD
                 Key: SPARK-6348
             Project: Spark
          Issue Type: Improvement
          Components: MLlib
    Affects Versions: 1.2.1
            Reporter: tanyinyan
            Priority: Minor

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(, 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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