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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Date Wed, 01 Jul 2015 08:54:05 GMT

]

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

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

--- 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 --

No we don't that's actually a bug I had forgotten about. I'll fix it here.

> Add threshold setting for SVM binary predictions
> ------------------------------------------------
>
>          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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