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https://issues.apache.org/jira/browse/SPARK-2748?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14079207#comment-14079207
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Apache Spark commented on SPARK-2748:
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User 'srowen' has created a pull request for this issue:
https://github.com/apache/spark/pull/1659
> Loss of precision for small arguments to Math.exp, Math.log
> -----------------------------------------------------------
>
> Key: SPARK-2748
> URL: https://issues.apache.org/jira/browse/SPARK-2748
> Project: Spark
> Issue Type: Bug
> Components: GraphX, MLlib
> Affects Versions: 1.0.1
> Reporter: Sean Owen
> Priority: Minor
>
> In a few places in MLlib, an expression of the form log(1.0 + p) is
> evaluated. When p is so small that 1.0 + p == 1.0, the result is 0.0. However
> the correct answer is very near p. This is why Math.log1p exists.
> Similarly for one instance of exp(m) - 1 in GraphX; there's a special
> Math.expm1 method.
> While the errors occur only for very small arguments, given their use in
> machine learning algorithms, this is entirely possible.
> Also, while we're here, naftaliharris discovered a case in Python where 1 - 1
> / (1 + exp(margin)) is less accurate than exp(margin) / (1 + exp(margin)). I
> don't think there's a JIRA on that one, so maybe this can serve as an
> umbrella for all of these related issues.
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