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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 09:19:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14609807#comment-14609807
] 

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_r33661424
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/pipeline/Predictor.scala
---
    @@ -121,7 +121,7 @@ object Predictor {
     
             input.mapWithBcVariable(model){
               (element, model) => {
    -            (element, predictOperation.predict(element, model))
    +            (element, predictOperation.predict(element, model, resultingParameters))
    --- End diff --
    
    Hmm I'm not so sure either what is the best way to go here. My initial idea was to isolate
the predict operation as much as possible from the code necessary to do the setup. I don't
like the `ParameterMap` much, because it allows you to have access to much more information
than necessary. IMO it's better to extract the information once at one place and then have
an explicit representation of the information.


> 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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