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https://issues.apache.org/jira/browse/SPARK-9075?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen updated SPARK-9075:
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Priority: Minor (was: Major)
I'm not sure this is a bug, in that clearly the algorithm has a requirement,
and your input doesn't meet that requirement, and it throws an exception as
expected.
However I'm curious if there is a reason it absolutely can't have more
categories than bins? it means you can't consider every category separately as
a decision criterion, but can still consider some of them, in theory, and
continue anyway? [~josephkb]
> DecisionTreeMetadata - setting maxPossibleBins to numExamples is incorrect.
> ----------------------------------------------------------------------------
>
> Key: SPARK-9075
> URL: https://issues.apache.org/jira/browse/SPARK-9075
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.4.0
> Reporter: Les Selecky
> Priority: Minor
>
> In
> https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
> there's a statement that sets maxPossibileBins to numExamples when
> numExamples is less than strategy.maxBins.
> This can cause an error when training small partitions; the error is
> triggered further down in the logic where it's required that
> maxCategoriesPerFeature be less than or equal to maxPossibleBins.
> Here's the an example of how it was manifested: the partition contained 49
> rows (i.e., numExamples=49 but strategy.maxBins was 57.
> The maxPossibleBins = math.min(strategy.maxBins, numExamples) logic therefore
> reduced maxPossibleBins to 49 causing the "require(maxCategoriesPerFeature <=
> maxPossibleBins" to throw an error.
> In short, this will be a problem when training small datasets with a feature
> that contains more categories than numExamples.
> In our local testing we commented out the "math.min(strategy.maxBins,
> numExamples)" line and the decision tree succeeded where it had failed
> previously.
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