Feynman Liang created SPARK-12804:
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Summary: ml.classification.LogisticRegression fails when
FitIntercept with same-label dataset
Key: SPARK-12804
URL: https://issues.apache.org/jira/browse/SPARK-12804
Project: Spark
Issue Type: Bug
Components: ML
Affects Versions: 1.6.0
Reporter: Feynman Liang
When training LogisticRegression on a dataset where the label is all 0 or all
1, an array out of bounds exception is thrown. The problematic code is
{code}
initialCoefficientsWithIntercept.toArray(numFeatures)
= math.log(histogram(1) / histogram(0))
}
{/code}
The correct behaviour is to short-circuit training entirely when only a single
label is present (can be detected from {{labelSummarizer}}) and return a
classifier which assigns all true/false with infinite weights.
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