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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/01/15 21:03:36 UTC

[jira] [Created] (SPARK-5272) Refactor NaiveBayes to support discrete and continuous labels,features

Joseph K. Bradley created SPARK-5272:
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             Summary: Refactor NaiveBayes to support discrete and continuous labels,features
                 Key: SPARK-5272
                 URL: https://issues.apache.org/jira/browse/SPARK-5272
             Project: Spark
          Issue Type: Improvement
          Components: MLlib
    Affects Versions: 1.2.0
            Reporter: Joseph K. Bradley


This JIRA is to discuss refactoring NaiveBayes in order to support both discrete and continuous labels and features.

Currently, NaiveBayes supports only discrete labels and features.

Proposal: Generalize it to support continuous values as well.

Some items to discuss are:
* How commonly are continuous labels/features used in practice?  (Is this necessary?)
* What should the API look like?
** E.g., should NB have multiple classes for each type of label/feature, or should it take a general Factor type parameter?




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