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  1. Spark
  2. SPARK-5272

Refactor NaiveBayes to support discrete and continuous labels,features

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Details

    • Improvement
    • Status: Resolved
    • Major
    • Resolution: Incomplete
    • 1.2.0
    • None
    • MLlib

    Description

      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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        is related to

        New Feature - A new feature of the product, which has yet to be developed. SPARK-4894 Add Bernoulli-variant of Naive Bayes

        • Major - Major loss of function.
        • Resolved

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            Unassigned Unassigned
            josephkb Joseph K. Bradley
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