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https://issues.apache.org/jira/browse/SPARK-22879?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16301724#comment-16301724
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Adrien Lavoillotte commented on SPARK-22879:
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In my case, the threshold was 0.2739699931543393, and the raw prediction was
-0.974572759358285.
* If you compute the proba from the raw prediction, you also get
0.2739699931543393, so the > comparison returns false
* If, however, you compute the {{rawThreshold}} from the threshold, you get
-0.9745727593582851 (notice that last 1), so the > comparison now returns true
> LogisticRegression inconsistent prediction when proba == threshold
> ------------------------------------------------------------------
>
> Key: SPARK-22879
> URL: https://issues.apache.org/jira/browse/SPARK-22879
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
> Affects Versions: 1.6.3
> Reporter: Adrien Lavoillotte
> Priority: Minor
>
> I'm using {{org.apache.spark.ml.classification.LogisticRegression}} for
> binary classification.
> If I predict on a record that yields exactly the probability of the
> threshold, then the result of {{transform}} is different depending on whether
> the {{rawPredictionCol}} param is empty on the model or not.
> If it is empty, as most ML tools I've seen, it correctly predicts 0, the rule
> being {{ if (proba > threshold) then 1 else 0 }} (implemented in
> {{probability2prediction}}).
> If however {{rawPredictionCol}} is set (default), then it avoids
> recomputation by calling {{raw2prediction}}, and the rule becomes {{if
> (rawPrediction(1) > rawThreshold) 1 else 0}}. The {{rawThreshold = math.log(t
> / (1.0 - t))}} is ever-so-slightly below the {{rawPrediction(1)}}, so it
> predicts 1.
> The use case is that I choose the threshold amongst
> {{BinaryClassificationMetrics#thresholds}}, so I get one that corresponds to
> the probability for one or more of my test set's records. Re-scoring that
> record or one that yields the same probability exhibits this behaviour.
> Tested this on Spark 1.6 but the code involved seems to be similar on Spark
> 2.2.
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