tanyinyan created SPARK-6348:
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Summary: 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
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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