Shaochen Shi created SPARK-27577:
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Summary: 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: 2.4.2, 2.4.1, 2.4.0, 2.3.3, 2.3.2, 2.3.1, 2.3.0, 2.2.3,
2.2.2, 2.2.1, 2.2.0, 2.1.3, 2.1.2, 2.1.1, 2.1.0, 2.0.2, 2.0.1, 2.0.0, 1.6.3,
1.6.2, 1.6.1, 1.6.0, 1.5.2, 1.5.1, 1.5.0, 1.4.1, 1.4.0, 1.3.1, 1.3.0
Reporter: Shaochen Shi
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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