[jira] [Issue Comment Deleted] (SPARK-6348) Enable useFeatureScaling in SVMWithSGD

["tanyinyan (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-6348?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

tanyinyan updated SPARK-6348:
-----------------------------
    Comment: was deleted

(was: 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)



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org