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https://issues.apache.org/jira/browse/MAHOUT-216?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen updated MAHOUT-216:
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Fix Version/s: 0.3
Affects Version/s: 0.2
> Improve the results of MAHOUT-145 by uniformly distributing the classes in
> the partitioned data
> -----------------------------------------------------------------------------------------------
>
> Key: MAHOUT-216
> URL: https://issues.apache.org/jira/browse/MAHOUT-216
> Project: Mahout
> Issue Type: Improvement
> Components: Classification
> Affects Versions: 0.2
> Reporter: Deneche A. Hakim
> Assignee: Deneche A. Hakim
> Fix For: 0.3
>
>
> the poor results of the partial decision forest implementation may be
> explained by the particular distribution of the partitioned data. For
> example, if a partition does not contain any instance of a given class, the
> decision trees built using this partition won't be able to classify this
> class.
> According to [CHAN, 95]:
> {quote}
> Random Selection of the partitioned data sets with a uniform distribution of
> classes is perhaps the most sensible solution. Here we may attempt to
> maintain the same frequency distribution over the ''class attribute" so that
> each partition represents a good but a smaller model of the entire training
> set
> {quote}
> [CHAN, 95]: Philip K. Chan, "On the Accuracy of Meta-learning for Scalable
> Data Mining"
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