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https://issues.apache.org/jira/browse/SPARK-5114?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-5114:
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Target Version/s: (was: 1.6.0)
> Should Evaluator be a PipelineStage
> -----------------------------------
>
> Key: SPARK-5114
> URL: https://issues.apache.org/jira/browse/SPARK-5114
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Affects Versions: 1.2.0
> Reporter: Joseph K. Bradley
>
> Pipelines can currently contain Estimators and Transformers.
> Question for debate: Should Pipelines be able to contain Evaluators?
> Pros:
> * Schema check: Evaluators take input datasets with particular schema, which
> should perhaps be checked before running a Pipeline.
> * Intermediate results:
> ** If a Transformer removes a column (which is not done by built-in
> Transformers currently but might be reasonable in the future), then the user
> can never evaluate that column. (However, users could keep all columns
> around.)
> ** If users have to evaluate after running a Pipeline, then each evaluated
> column may have to be re-materialized.
> Cons:
> * API: Evaluators do not transform datasets. They produce a scalar (or a
> few values), which makes it hard to say how they fit into a Pipeline or a
> PipelineModel.
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