[jira] [Resolved] (SPARK-22879) LogisticRegression inconsistent prediction when proba == threshold

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-22879?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-22879.
----------------------------------
    Resolution: Incomplete

> 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
>              Labels: bulk-closed
>
> 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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