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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-6348) Enable useFeatureScaling in SVMWithSGD
Date Mon, 16 Mar 2015 23:43:38 GMT

    [ https://issues.apache.org/jira/browse/SPARK-6348?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14364226#comment-14364226
] 

Joseph K. Bradley commented on SPARK-6348:
------------------------------------------

Providing this feature sounds good.

Side note: SVMs should not be run using categorical variables; for those, you should probably
use a one-hot encoding.

> 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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