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