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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2297) Add threshold setting for SVM binary predictions
Date Wed, 01 Jul 2015 08:40:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14609741#comment-14609741
] 

ASF GitHub Bot commented on FLINK-2297:
---------------------------------------

Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/874#discussion_r33658585
  
    --- Diff: docs/libs/ml/svm.md ---
    @@ -144,30 +145,42 @@ The SVM implementation can be controlled by the following parameters:
             <td><strong>Stepsize</strong></td>
             <td>
               <p>
    -            Defines the initial step size for the updates of the weight vector. 
    -            The larger the step size is, the larger will be the contribution of the weight
vector updates to the next weight vector value. 
    +            Defines the initial step size for the updates of the weight vector.
    +            The larger the step size is, the larger will be the contribution of the weight
vector updates to the next weight vector value.
                 The effective scaling of the updates is $\frac{stepsize}{blocks}$.
    -            This value has to be tuned in case that the algorithm becomes unstable. 
    +            This value has to be tuned in case that the algorithm becomes unstable.
                 (Default value: <strong>1.0</strong>)
               </p>
             </td>
           </tr>
           <tr>
    -        <td><strong>Seed</strong></td>
    +        <td><strong>Threshold</strong></td>
             <td>
               <p>
    -            Defines the seed to initialize the random number generator. 
    -            The seed directly controls which data points are chosen for the SDCA method.

    -            (Default value: <strong>0</strong>)
    +            Defines the limiting value for the decision function above which examples
are labeled as
    +            positive (+1.0). Examples with a decision function value below this value
are classified
    +             as negative (-1.0). In order to get the raw decision function value you
need to
    +             unset this parameter using the [[clearThreshold()]] function.  (Default
value: <strong>0.0</strong>)
    --- End diff --
    
    Do we want to have this turned on by default?


> Add threshold setting for SVM binary predictions
> ------------------------------------------------
>
>                 Key: FLINK-2297
>                 URL: https://issues.apache.org/jira/browse/FLINK-2297
>             Project: Flink
>          Issue Type: Improvement
>          Components: Machine Learning Library
>            Reporter: Theodore Vasiloudis
>            Assignee: Theodore Vasiloudis
>            Priority: Minor
>              Labels: ML
>             Fix For: 0.10
>
>
> Currently SVM outputs the raw decision function values when using the predict function.
> We should have instead the ability to set a threshold above which examples are labeled
as positive (1.0) and below negative (-1.0). Then the prediction function can be directly
used for evaluation.



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