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https://issues.apache.org/jira/browse/FLINK-2297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14611761#comment-14611761
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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_r33764869
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/classification/SVM.scala
---
@@ -187,6 +200,34 @@ class SVM extends Predictor[SVM] {
parameters.add(Seed, seed)
this
}
+
+ /** Sets the threshold above which elements are classified as positive.
+ *
+ * The [[predict ]]and [[evaluate]]functions will return +1.0 for items
with a decision function
+ * value above this threshold, and -1.0 for items below it.
+ * @param threshold
+ * @return
+ */
+ def setThreshold(threshold: Double): SVM = {
+ parameters.add(ThresholdValue, threshold)
+ this
+ }
+
+ /** Sets whether the predictions should return the raw decision function
value or the
+ * thresholded binary value.
+ *
+ * When setting this to true, predict and evaluate return the raw
decision value, which is
+ * the distance from the separating hyperplane.
+ * When setting this to false, they return thresholded (+1.0, -1.0)
values.
+ *
+ * @param outputDecisionFunction When set to true, [[predict ]]and
[[evaluate]] return the raw
--- End diff --
whitespace between [[predict]] and "and"
> 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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