[SPARK-5272] Refactor NaiveBayes to support discrete and continuous labels,features

• Type: Improvement
• Status: Resolved
• Priority: Major
• Resolution: Incomplete
• Affects Version/s: 1.2.0
• Component/s: MLlib
• Labels: bulk-closed clustering
• Assignee: Unassigned
• Reporter: Joseph K. Bradley
• Created: 15/Jan/15 20:03
• Updated: 21/May/19 05:37
• Resolved: 21/May/19 05:37

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?

Issue Links

• is related to: SPARK-4894 Add Bernoulli-variant of Naive Bayes