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https://issues.apache.org/jira/browse/SPARK-3702?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14496274#comment-14496274
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Daniel Erenrich commented on SPARK-3702:
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One thing I'm interested in along these lines is a standard interface/method
for getting confidence scores from models. Currently I cannot write code that
generically accepts a model that can give me the probability that its
prediction is correct. There are many use cases where you would want to be able
to handle such models but there doesn't appear to be a standard way to get that
information despite the fact that many of the models already support this
functionality one way or another.
> Standardize MLlib classes for learners, models
> ----------------------------------------------
>
> Key: SPARK-3702
> URL: https://issues.apache.org/jira/browse/SPARK-3702
> Project: Spark
> Issue Type: Sub-task
> Components: MLlib
> Reporter: Joseph K. Bradley
> Assignee: Joseph K. Bradley
> Priority: Blocker
>
> Summary: Create a class hierarchy for learning algorithms and the models
> those algorithms produce.
> This is a super-task of several sub-tasks (but JIRA does not allow subtasks
> of subtasks). See the "requires" links below for subtasks.
> Goals:
> * give intuitive structure to API, both for developers and for generated
> documentation
> * support meta-algorithms (e.g., boosting)
> * support generic functionality (e.g., evaluation)
> * reduce code duplication across classes
> [Design doc for class hierarchy |
> https://docs.google.com/document/d/1BH9el33kBX8JiDdgUJXdLW14CA2qhTCWIG46eXZVoJs]
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