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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2019/04/26 09:20:00 UTC
[jira] [Assigned] (SPARK-27577) Wrong thresholds selected by
BinaryClassificationMetrics when downsampling
[ https://issues.apache.org/jira/browse/SPARK-27577?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-27577:
------------------------------------
Assignee: (was: Apache Spark)
> Wrong thresholds selected by BinaryClassificationMetrics when downsampling
> --------------------------------------------------------------------------
>
> Key: SPARK-27577
> URL: https://issues.apache.org/jira/browse/SPARK-27577
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.3.0, 1.3.1, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.5.2, 1.6.0, 1.6.1, 1.6.2, 1.6.3, 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.4.0, 2.4.1, 2.4.2
> Reporter: Shaochen Shi
> Priority: Critical
> Labels: Correctness
>
> In binary metrics, a threshold means any instance with a score >= threshold will be considered as positive.
> However, in the existing implementation:
> # When `numBins` is set when creating a `BinaryClassificationMetrics` object, all records (ordered by scores in DESC) will be grouped into chunks.
> # In each chunk, statistics (in `BinaryLabelCounter`) of records are accumulated while the first record's score is selected as threshold.
> # All these generated/sampled records form a new smaller data set to calculate binary metrics.
> At the second step, it brings the BUG that the score/threshold of a record is correlated with wrong values like larger `true positive`, smaller `false negative` when calculating `recallByThresholds`, `precisionByThresholds`, etc.
> Thus, the BUG fix is straightfoward. Let's pick up the last records's core in all chunks as thresholds while statistics merged.
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