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https://issues.apache.org/jira/browse/SPARK-3702?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14496324#comment-14496324
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Joseph K. Bradley commented on SPARK-3702:
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That's supported via the "rawPredictions" output column in the
org.apache.spark.ml.classification.Classifier abstraction. As we add wrappers
to spark.mllib algorithms in the spark.ml package, we can have classifiers
which have confidences output this column. Classifiers which don't yet support
confidences can be subclasses of Predictor instead of Classifier. But yes, it
will be important to support more and more!
> 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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