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https://issues.apache.org/jira/browse/SPARK-12804?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Feynman Liang updated SPARK-12804:
----------------------------------
Description:
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:scala}
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.
was:
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.
> 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:scala}
> 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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