[jira] [Resolved] (MAHOUT-826) Bayes/CBayes classification on a non-existing feature

["Sebastian Schelter (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-826?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sebastian Schelter resolved MAHOUT-826.
---------------------------------------

    Resolution: Won't Fix

The old naive bayes implementation got removed from the codebase, therefore this issue became obnoxious.
                
> Bayes/CBayes classification on a non-existing feature
> -----------------------------------------------------
>
>                 Key: MAHOUT-826
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-826
>             Project: Mahout
>          Issue Type: Bug
>          Components: Classification
>    Affects Versions: 0.5
>            Reporter: Andre-Philippe Paquet
>            Assignee: Robin Anil
>            Priority: Minor
>             Fix For: 0.7
>
>         Attachments: mahout-826.patch, mahout-826.patch
>
>
> (see http://comments.gmane.org/gmane.comp.apache.mahout.user/9597)
> Using CBayes or Bayes, when trying to classify a feature/word that doesn't exist in the model, instead of returning the default/unknown label, the algorithm returns all labels with a constant score (ex: 12.386649147018964). After a quick look in CBayesAlgorithm, I found the problem in the featureWeight function that returns the theta normalized weight even if the feature didn't have any match (result=0).
> As a fix, I overrided the function in a subclass and return 0 if the weight of the current feature in the current label is 0. 

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