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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 05:35:20 UTC
[jira] [Updated] (SPARK-5272) Refactor NaiveBayes to support
discrete and continuous labels,features
[ https://issues.apache.org/jira/browse/SPARK-5272?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-5272:
--------------------------------
Labels: bulk-closed clustering (was: clustering)
> 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
> Priority: Major
> Labels: bulk-closed, clustering
>
> 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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