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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